# Zynq FPGA for Hardware Co-simulation of Takagi-Sugeno Neuro-Fuzzy for MPPT Algorithm Incorporating Sensorless Wind Speed Estimation in Grid-Connected Wind System

Mahdi Hermassi, Saber Krim, Youssef Kraiem, Mohamed Ali Hajjaji

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## To cite this version:

Mahdi Hermassi, Saber Krim, Youssef Kraiem, Mohamed Ali Hajjaji. Zynq FPGA for Hardware Co-simulation of Takagi-Sugeno Neuro-Fuzzy for MPPT Algorithm Incorporating Sensorless Wind Speed Estimation in Grid-Connected Wind System. Journal of engineering research = Mağallať al-abḥāṭ al-handasiyyať, 2024, 10.1016/j.jer.2024.09.017. hal-04735908

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Submitted on 6 Jun 2025

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# Zynq FPGA for hardware co-simulation of Takagi-Sugeno neuro-fuzzy for MPPT algorithm incorporating sensorless wind speed estimation in grid-connected wind system

Mahdi Hermassi<sup>a, *, 1</sup>, Saber Krim<sup>b, c</sup>, Youssef Kraiem<sup>d, e</sup>, Mohamed Ali Hajjaji<sup>f</sup>

## ARTICLE INFO

### Keywords:

Wind energy conversion system

Maximum power point tracking

Neuro-fuzzy

Fuzzy logic system

FPGA

## ABSTRACT

This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R²) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.

## 1. Introduction

Recently, a substantial endeavor is ongoing to establish a renewable energy source as an alternative to fossil fuels, with a primary focus on environmental preservation [1]. Consequently, wind energy technology has gained significant attention due to its array of advantages, which

encompass cost-effectiveness, simplicity of deployment, and minimal maintenance requirements [2]. Wind Turbines (WTs) can be categorized into two primary configurations based on the type of the generator they use: fixed speed and Variable Speed. In the case of fixed speed turbines, the generator is directly connected to the grid, and its rotational speed is adjusted to match the grid's desired frequency using a speed multiplier

<footnote><sup>a</sup> Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia</footnote>

<footnote><sup>b</sup> Laboratory of Automatic, Electrical Systems and Environment, National Engineering School of Monastir, University of Monastir, Monastir 5000, Tunisia</footnote>

<footnote><sup>c</sup> Department of Technology, Higher Institute of Applied Sciences and Technology of Kasserine, University of Kairouan, Kairouan 3100, Tunisia</footnote>

<footnote><sup>d</sup> IREENA Lab UR 4642, Nantes University, 37 bd de l'Université, Saint-Nazaire 44600, France</footnote>

<footnote><sup>e</sup> ESEO, 10 Bd Jeanneteau, Angers 49100, France</footnote>

<footnote><sup>f</sup> Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, Sousse, Tunisia</footnote>

<footnote>* Corresponding author.
E-mail address: mahdi.harmassi@gmail.com (M. Hermassi).</footnote>

<footnote n="1">¹ https://orcid.org/0000-0002-3410-5500</footnote>
[3]. The main advantage of Variable Speed WTs (VSWTs) compared to fixed-speed ones includes the ability to harness maximum power even at low wind speeds and enhanced flexibility achieved through power electronics (power converters), which improves their integration into the electrical network [4,5]. On the other hand, VSWTs employ various power generators, including doubly fed induction generators, synchronous machines with a wound rotor, and Permanent Magnet Synchronous Generators (PMSG).

Recently, PMSGs have garnered increasing attention as vital components in VSWTs. Their rising popularity can be attributed to several advantages, including the capacity to operate at variable speeds, reduced noise levels during low-power operation, high torque output, and minimal losses in the rotor system. When a synchronous machine possesses an ample number of poles, it can be directly connected to the turbine. Notably, the performance of the PMSG equipment has seen enhancements, accompanied by a decline in their cost. As a result, PMSGs are emerging as promising candidates for innovative designs in WTs [6–8]. Nevertheless, the inherent randomness and unpredictability of wind speeds in wind energy generation often hinder current research efforts aimed at maximizing the output of high-capacity wind generators. Consequently, this limitation results in reduced efficiency in harnessing wind energy and compromises the advantages of utilizing high-capacity WTs [9]. Conversely, the fluctuating nature of wind speeds and the inherent instability of wind energy introduce unique challenges when connecting large-scale wind farms to the power grid, potentially destabilizing the overall power system.

The operational range of a VSWT can be divided into four distinct zones, as illustrated in Fig. 1. Zone 1 is characterized by low wind speeds that are insufficient to generate electrical power using the machine. In zone 2, the system is optimized to harness the available wind energy effectively by adjusting power output with changing wind speeds, employing a Maximum Power Point Tracking (MPPT) algorithm, and maintaining the blade PA at its optimal position. In Zone 3, when wind speeds surpass their nominal value, the PA is adjusted to maintain the generated electrical power close to its nominal value. Finally, in Zone 4, where wind speeds are dangerously high and pose a risk to the WT’s integrity, emergency mechanisms are employed to shut down the turbine and prevent damage [10]. This work focuses on Zone 2 and Zone 3, combining a MPPT-based Takagi Sugeno (TS) Adaptive Neuro Fuzzy Inference System (ANFIS) technique with PA control system. The devised MPPT-based TS-ANFIS with PA control algorithm relies on a newly introduced Wind Speed Estimator (WSE)-based TS-ANFIS model, thereby improving the accuracy of wind speed estimation.

MPPT is indispensable for optimizing renewable energy systems,

especially those utilizing solar and wind sources. Its pivotal roles include maximizing energy harvesting by extracting maximum power from the source, enhancing efficiency through continuous adjustments, adapting to nonlinear conditions like varying solar irradiance and wind speeds, managing shading conditions effectively, reducing convergence time with advanced techniques, tailoring to specific system configurations, and integrating seamlessly in hybrid systems to maximize overall efficiency. Recent research has focused on employing advanced control techniques to optimize the performance of wind power systems, with the overarching goal of enhancing the effectiveness and reliability of WTs [11–13]. Given the erratic and difficult to predict nature of wind as an energy source, it is crucial for WTs to extract the maximum of the available wind power. Therefore, the continuous refinement and development of MPPT algorithms play a pivotal role in ensuring the efficiency of WTs. Regarding the maximization of captured power, MPPT algorithms can be classified into two major categories: Direct Power Controller (DPC), and Indirect Power Controller (IPC). Conventional MPPT algorithms, such as Power Signal Feedback (PSF), Optimal Torque (OT), and Tip Speed Ratio (TSR) are regrouped as a part of the IPC algorithms [14–16]. The IPC method entails pre-computing power by analyzing wind velocity plots to optimize mechanical power. Conversely, the DPC approach utilizes electrical power to operate the turbine at the MPPT. The DPC control algorithm incorporates techniques like Perturb and Observe (P&O) [17], Optimal Relation-Based (ORB) [18], and Incremental Conductance (INC) [19]. The DPC- and IPC-based schemes constitute the majority of conventional algorithms. The prevalent adoption and popularity of the conventional P&O technique are evident in the reviewed literature, attributed to its advantages such as simplicity, lack of sensor requirements, and ease of implementation. However, this method is not without its challenges, including oscillations around the MPPT and reduced efficiency during periods of high wind fluctuations [20]. Additionally, it grapples with a trade-off between efficiency and tracking speed. In the case of PSF, it relies on the WT’s measured power curve, which may not be suitable for installation in areas with high wind turbulence [21]. In [22], the TSR control adjusts the rotor speed to maintain an optimal value but requires precise wind speed information. In contrast, the authors in [23] introduces an adaptive torque controller that seeks to maximize power capture despite uncertainties in aerodynamics and turbulent effects. However, a drawback of the adaptive torque controller method arises from the indirect nature of wind speed measurement, hindering immediate and significant reflections of changes in wind speed on the reference signal. This limitation, combined with its relatively slow response to wind variations, can result in reduced energy capture [24].

<figure><img src="image_4.png" /><figcaption>Fig. 1. Wind speed ranges in VSWT operation.</figcaption></figure>
Computational intelligence techniques, notably employing soft computing methods such as fuzzy logic, neural networks and neuro fuzzy, represent a pivotal advancement in addressing the intricacies of renewable energy systems [25,26]. These techniques empower machines to learn from data, make informed decisions, and optimize solutions in dynamic and uncertain environments, mirroring the adaptive nature of human intelligence. Particularly in the context of MPPT, these advanced controllers exhibit remarkable responsiveness, efficiently adapting to sudden changes in wind speed [10,27,28].

In [29], the authors presented an MPPT-based controller using FLC for a variable speed WECS with a direct drive PMSG. This method optimizes efficiency and robustness in MPPT control by using DC voltage and power. The MPPT-based FLC is highly effective and capable of increasing power output. However, the FLC is limited due to its heavy reliance on pre-set fuzzy rules and fixed parameters, which hampers its adaptability in dynamic scenarios. Artificial NNs (ANNs) offer an alternative for determining maximum power peaks by processing input variables for optimal power output [30]. ANNs are flexible, with layers allowing customization as needed. They are more efficient and reliable than traditional controllers for harnessing wind energy, yet they have limitations such as black box structures, high computing loads, vulnerability to overfitting, and reliance on empirical models. Moreover, implementing ANNs requires a lookup table with predefined data.

To enhance the efficient capture of maximum wind energy, it is essential to have access to accurate wind speed information [31]. In practice, many VSWTs typically employ mechanical anemometers to measure wind speed. Typically, several anemometers are positioned at various distances to measure wind speed at different locations. The effective wind speed is determined as the average wind field across the rotor swept area, unaffected by the presence of the wind turbine. However, the anemometer on the wind turbine cannot directly measure the wind speed in this swept area, leading to a reduction in overall system performance [32]. Anemometers mounted atop nacelles can also contribute to inaccurate wind speed measurements [33]. Wind farms often employ multiple anemometers at various locations to gauge average wind speed. However, this practice introduces challenges in calibration and measurement precision [34–36]. Consequently, there has been growing global interest in replacing these anemometers with an effective wind speed estimator based on soft computing techniques [37]. This approach aims to address the drawbacks associated with traditional wind speed measurement and represents a promising avenue for improving VSWT performance. In this study, we proposed a wind speed estimator based on soft computing techniques depend on three inputs: mechanical speed, mechanical power, and the tip speed ratio (TSR). This method obviates the need for wind speed sensors, which are prone to calibration difficulties and measurement inaccuracies [32]. These sensors only capture wind conditions at specific points and are highly susceptible to environmental factors such as turbulence, air density variations, and obstructions, compromising their accuracy [33]. In the proposed estimator, we have employed only a torque sensor, which is used to obtain mechanical power. Torque sensors are less influenced by the aforementioned environmental factors, providing more consistent and reliable data [38]. Our proposed approach employs a torque sensor to directly measure the mechanical torque on a model wind turbine generator. By multiplying the measured torque by the rotor's angular velocity, we obtain a more accurate and direct assessment of the mechanical power extracted from the wind. This method avoids the internal losses associated with electrical power measurements, thereby offering a more reliable representation of the actual power extracted from the wind flow.

In the literature, artificial intelligence-based wind speed estimation can be categorized into four groups: fuzzy logic models, statistical models, artificial neural network models, and hybrid models [39–45]. In [39], the authors introduced an artificial neural network for wind speed estimation. In [40], an algorithm based on Gaussian radial basis function networks is proposed for wind speed estimation. The authors in [41]

presents a sensor less wind speed estimation algorithm based on an unknown input disturbance observer and the extreme learning machine for VSWTs. However, this estimator may not be accurate enough at certain critical points, such as during rapid and unpredictable changes in wind patterns. In [42], a sensor less wind speed estimator using fuzzy logic principles, inferred from typical WT power curves, is proposed. However, it may be challenging to determine suitable input limits for this method. The authors in [43] presents a wind speed estimation method based on an autoregressive statistical model, but it involves computationally complex calculations. In [44], wind speed estimation is accomplished using a support vector machine, which is effective for problems characterized by nonlinearity, small sample sizes, local minima, high dimensions, and requiring strong generalization. The authors in [45] introduces a wind speed estimation approach using support vector regression for wind power systems, although it heavily relies on WT power without accounting power losses.

Hybrid intelligent techniques have gained recognition for their adeptness in capturing intricate, nonlinear relationships between inputs and outputs data [46]. Among these techniques, ANFIS stands out as a hybrid approach that melds the capabilities of a Takagi-Sugeno Fuzzy Inference System (FIS) with Artificial Neural Networks (ANNs) [47]. ANFIS offers a significant advantage over traditional fuzzy logic systems by eliminating the need to define parameters for fuzzy Membership Functions (MFs) and the creation of IF-THEN fuzzy rules manually. ANFIS harnesses the learning abilities of ANN to automatically fine-tune fuzzy MFs and generate a minimal set of IF-THEN fuzzy rules, reducing the computational complexity [48]. ANFIS introduces learning capabilities into fuzzy systems by training fuzzy MFs to adapt and replicate the system's behavior based on data. Recently, researchers have been applying ANFIS to a wide range of applications, including control systems, forecasting models, classification task, and diagnostic systems [49–51].

Nowadays, industrial designers and researchers are increasingly advocating for software solutions such as microcontrollers and Digital Signal Processors (DSPs) to implement control algorithms in electrical systems. These solutions are favored for their cost-effectiveness and adaptability [52]. However, DSPs and microcontrollers have inherent limitations when it comes to handling highly complex control algorithms due to their sequential processing nature. To address these limitations associated with software solutions, Field-Programmable Gate Arrays (FPGAs) are emerging as a viable alternative for digital control [53]. When coupled with a high-speed analog-to-digital converter, FPGAs exhibit the capability to compute intricate control algorithms in just a few microseconds, surpassing the performance constraints of DSPs and microcontrollers [11,54,55].

To address the previously mentioned limitations and enhance system reliability while reducing complexity and cost, this paper introduces a hardware implementation on an FPGA board of TS-ANFIS for MPPT control strategy and wind speed estimation. The MPPT-based TS-ANFIS is integrated with a PA control system to dynamically adjust blade angles, optimizing power extraction and effectively mitigating risks associated with high wind speeds. Additionally, the WSE-based TS-ANFIS model is proposed to estimate the effective wind speed and overcome traditional anemometer limitations, thus making a significant contribution to the field of wind energy systems. In conclusion, the article introduces novel concepts and achieves significant objectives in both theoretical and practical aspects. These objectives can be summarized as follows:

1. The article presents a novel MPPT control algorithm utilizing a TS-ANFIS approach. This method aims to address the limitations of conventional MPPT control algorithms.

2. The PA control system combined with the MPPT-based TS-ANFIS algorithm, effectively adjusts blade angles to maximize power extraction and mitigate risks associated with high wind speeds.
3. The article introduces a novel WSE-based TS-ANFIS model, refining wind speed estimation accuracy and overcoming conventional anemometer limitations that hinder precise effective wind speed measurement. This approach also minimizes overall costs, maintenance, and enhances system reliability compared to traditional anemometer usage.

4. TS-ANFIS is a powerful soft computing algorithm suitable for a wide range of operating conditions. It leverages the learning capabilities of ANNs to train the input MFs and IF-THEN fuzzy rules. Once the training phase concludes, TS-ANFIS seamlessly adjusts the system's behavior, rendering it exceptionally sturdy in the face of evolving conditions and notably precise in its performance efficiency.

5. The proposed TS-ANFIS mechanism eliminates the need for complex mathematical models such as differential equations when dealing with nonlinearities. Instead, it integrates prior system knowledge to make predictions about future behavior. It outperforms ANNs in terms of learning capability, requires fewer adjustable parameters, and achieves faster convergence. In addition, it boasts a low computational complexity which rely on intricate algorithms, have substantial memory requirements, and are heavily reliant on parameter selection.

6. The article evaluates the performance of the MPPT control algorithm and wind speed estimation technique through simulation using the XSG tool. It is worth highlighting that this study distinguishes itself from previous research by conducting hardware-in-the-loop co-simulation, which involves integrating a Xilinx FPGA Zed-Board. This approach allows for a comprehensive assessment of space complexity, frequency maximum, and throughput on the FPGA Zed-Board for the proposed approaches.

7. Finally, the article conducts hardware co-simulation on an FPGA Zed-Board. This approach minimizes the risks of equipment damage, ensures the safety of the experimental setup, and significantly reduces the time required for experimentation, prototyping, and time-to-market, thereby lowering overall system development costs.

The following is the organizational structure of this paper: In Section 2, a comprehensive overview of the modeling of WT is discussed. In Section 3, a detailed exploration of the TS-ANFIS algorithm for wind speed estimation and MPPT control design with PA control is presented. Section 4 provides an in-depth examination of the FPGA hardware architecture for both wind speed estimation and MPPT algorithm, along

with an explanation of their synthesis and implementation. In Section 5, the proposed wind speed estimation and the suggested MPPT algorithm are implemented and simulated using the XSG tool, followed by a comparison with other studied in literature. Finally, Section 6 serves as the conclusion of this work and sets the stage for future directions and perspectives.

## 2. Modeling of wind energy conversion system

The VSWT system studied in this work is depicted in Fig. 2. It comprises a WT equipped with three blades, a PMSG, and a three-phase rectifier denoted as the AC/DC converter. This configuration establishes a decentralized generator connected to the power grid via a common DC bus. Additionally, a three-phase inverter, referred to as the DC/AC converter, is responsible for transmitting power to the power grid. To mitigate the harmonics produced by this inverter, an inductor filter is employed.

The expressions for aerodynamic power and torque depend on the specific system that is extracting power from the wind. The aerodynamic power can be calculated as [5]:

$$P_{aer} = \frac{1}{2} \rho \pi R^2 v^3 C_p (\lambda, \beta) \quad (1)$$

where $C_p$ is the power coefficient, R is the radius of the area swept by the blades, $\rho$ is the air density (kg/m³), v is the wind speed (m/s), $\beta$ is the PA of the blades (°), and $\lambda$ is the TSR.

The Eq. (2) is used to determine the aerodynamic torque, denoted as $T_{aer}$, generated on the rotor of the WT.

$$T_{aer} = \frac{P_{aer}}{\Omega_m} \quad (2)$$

The power coefficient $C_p$ is a measure of the relationship between the wind power and aerodynamic power, and it is depends to the PA ($\beta$) and the TSR ($\lambda$). The formula of the TSR can be expressed as follows [58]:

$$\lambda = \frac{\Omega_m R}{V} \quad (3)$$

where $\Omega_m$ is the rotational speed of the WT (rad/s).

In this study, the WT is operated to harness the maximum available power. According to the Betz theory, the power coefficient cannot surpass 0.593, and this coefficient is dependent on the blade's geometrical

<figure><img src="image_5.png" /><figcaption>Fig. 2. Configuration of the VSWT.</figcaption></figure>
shape. The expression of the coefficient C<sub>p</sub> is derived as follows [11]:

$$C_p = 0.5 \left( \frac{151}{\lambda_i} - 0.58\beta - 0.002\beta^{2.14} - 10 \right) e^{-\frac{18.4}{\lambda_i}} \\ \lambda_i = \frac{1}{\frac{1}{\lambda - 0.02\beta} - \frac{0.003}{\beta^3 + 1}} \quad (4)$$

Fig. 3 illustrates the evolution of the coefficient C<sub>p</sub> according to λ and β. It reveals on optimal speed ratio value (λ<sub>opt</sub>= 8.15) at which the power coefficient C<sub>p</sub> reaches its maximum value of 0.4794 when β is set to zero.

Fig. 4 provides a concise representation of the diagram of WT model. Eq. (5) provides the expression for the maximum power achieved when employing the MPPT algorithm.

$$\left\{ \begin{array}{l} T_{em-MPPT} = \frac{1}{2} \frac{\rho \pi R^5 C_p - \max}{\lambda_{opt}^3} \Omega_{m-opt}^2 \\ P_{MPPT} = T_{em-MPPT} \Omega_{m-opt} \end{array} \right. \quad (5)$$

Fig. 5 illustrates the power availability characteristics at various wind speeds as a function of the generator's rotational speed. These curves indicate that operating the generator at a fixed speed would not fully utilize the theoretical maximum power curves. To optimize power extraction, it is essential to adjust the generator shaft's rotation speed according to the wind speed.

Indeed, suppose for a wind speed of V2, point A corresponds to the peak of the aerodynamic power with coordinates (P1, Ω1). If the turbine speed remains constant while the wind speed changes from V2 to V3, the power P2 corresponds to another point B, which may not be at the peak of the second characteristic curve. To extract the maximum power, it becomes necessary to adjust the generator speed to a new rotational speed Ω2 higher than the previous one Ω1. Therefore, varying the rotation speed according to the wind is essential to achieve maximum power (point C).

### 3. Proposed methods for WSE and MPPT algorithm

This study presents an innovative WSE-based TS-ANFIS model designed to estimate the effective wind speed for VSWT without relying on traditional sensors such as anemometers. Subsequently, this wind speed data is employed to compute the reference mechanical speed, denoted as Ω<sub>ref-m</sub>. To enhance the system's performance and maximize power output for VSWT, a MPPT-based TS-ANFIS controller is introduced. This controller operates by dynamically adjusting the electromagnetic torque to maintain the mechanical speed at the reference value. Furthermore, the proposed approach for controlling the VSWT focuses on optimizing wind energy utilization while ensuring the safety of the electrical conversion system in cases where wind speeds exceed

their predefined limits. When wind speeds exceed the established threshold, the orientation angle is adjusted to alleviate the stress on the lower part of the turbine blades. This adjustment leads to a reduction in torque force, subsequently resulting in a decrease in rotor speed and, consequently, a decrease in the output power. Therefore, to safeguard the WT from high wind speeds, especially those surpassing its rated capacity, PA control system is proposed.

#### 3.1. Control of the PA system

The primary objective in controlling the WT is to ensure it operates at its MPPT to inject maximum power into the power grid. However, when the wind speed exceeds the nominal speed, the control system adjusts the PA to reduce the torque applied to the lower blade surface. Under such conditions, the WT operates in a restricted mode, foregoing MPPT to maintain the output power and rotational speed at specified levels. The PA control serves the purpose of keeping the WT within the safe operating zone, as depicted in Fig. 1 (Zone 3). The principal of this control strategy are outlined in the following equation:

$$\left\{ \begin{array}{l} \beta_{ref} = \beta_0 = 0 \quad \text{for } 0 \le \Omega_m \le \Omega_{mn} \\ \beta_{ref} = \frac{\Delta\beta}{\Delta\Omega_m} (\Omega_m - \Omega_{mn}) + \beta_0 \quad \text{for } \Omega_m \ge \Omega_{mn} \end{array} \right. \quad (6)$$

with β<sub>0</sub> represents the initial PA.

#### 3.2. TS-ANFIS for WSE and MPPT speed control

TS-ANFIS represents a hybrid learning approach that emerge ANN with TS FIS. It serves as an intelligent learning approach applied in decision-making, predictive modeling, and classification tasks. The ANN plays a pivotal role in training the fuzzy MFs to replicate the non-linear dynamics of the system. TS-ANFIS effectively harnesses the combined strengths of ANN's intelligent learning abilities and FIS's capacity for human knowledge-based decision-making. The architecture of ANFIS is depicted in Fig. 6, with 'M' and 'N' representing two inputs and 'Z' denoting the output.

During the learning process in the forward pass, the least square method is employed to update the subsequent parameters of a first-order polynomial denoted as a, b and r. The expression for the output of a first-order TS FIS with two IF-THEN rules is as follows:

$$\text{Rules } -1: \text{IF 'M' is } X_1 \text{ and 'n' is } Y_1 \text{ THEN } Z_1 = a_1 M + b_1 N + r_1$$

$$\text{Rules } -2: \text{IF 'N' is } X_2 \text{ and 'n' is } Y_2 \text{ THEN } Z_2 = a_2 M + b_2 N + r_2$$

The ANFIS architecture comprises five layers of neurons.

##### 3.2.1. Layer 1

The initial layer is known as the Fuzzification layer, where each input variable is associated with MFs. The number of neurons in this layer corresponds to the total number of MFs assigned to the input variables. Each neuron in this layer is adaptable, with their weights being adjusted as part of the learning process. The available MF types include bell-shaped, triangular, trapezoidal, Gaussian, sigmoidal, and more. In this work, the suggested methods leverage triangular MFs. The mathematical representation of the triangular MF is articulated as follows:

$$\mu_{X_i}(m) = \begin{cases} 0 & \text{if } m \le a_i \\ \frac{m - a_i}{b_i - a_i} & \text{if } a_i \le m \le b_i \\ \frac{c_i - m}{c_i - b_i} & \text{if } b_i \le m \le c_i \\ 0 & \text{if } x \le c_i \end{cases} \quad (7)$$

where i represents the number of MFs allocated to each input.

<figure><img src="image_6.png" /><figcaption>Fig. 3. Cp versus λ.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Diagram of WT model.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 5. Active power versus rotational speed of generator.</figcaption></figure>

The shape of the triangular MF relies on the values of premise parameters (a, b, and c). These parameters are adjusted during the backward pass through the utilization of the back propagation gradient descent method, which considers the errors produced in each training cycle.

### 3.2.2. Layer 2

The second layer is referred to as the product layer or participation layer. In this layer, every neuron is non-adaptive and performs the calculation of the product of incoming signals. Each node is responsible for generating the firing strength of a rule.

$$W_i = \mu_{X_i}(m) \cdot \mu_{Y_i}(n) \qquad (8)$$

### 3.2.3. Layer 3

The third layer is known as the normalization layer. The nodes within this layer are non-adaptive and are responsible for calculating the normalized firing strength. This normalized firing strength represents the ratio of a rule's firing strength to the sum of the firing strengths of all the rules.

$$\bar{W}_i = \frac{W_i}{W_1 + W_2} \qquad (9)$$

### 3.2.4. Layer 4

The fourth layer is designated as the Defuzzification layer within the ANFIS architecture. Each node in this layer is adaptive and performs the operation of multiplying the normalized firing strength of a rule by the corresponding first-order polynomial function. In this layer, each node generates a crisp output.

$$\bar{W}_i \cdot Z_i = \bar{W}_i (a_i m + b_i n + r_i) \qquad (10)$$

### 3.2.5. Layer 5

The fifth and final layer is referred to as the output layer. It contains a single non-adaptive node that adds up all the incoming crisp signals, resulting in a single crisp output.

$$Z = \sum \bar{W}_i Z_i = \frac{\sum W_i Z_i}{\sum W_i} \qquad (11)$$

ANFIS can be trained using either the backpropagation gradient descent method or hybrid method. The hybrid optimization method combines both the least square method and the backpropagation descent method, yielding results of greater accuracy compared to the gradient descent method alone. The training process concludes when either the maximum specified number of training epochs is reached, or the predetermined minimum error tolerance is attained. The generation of the FIS can be accomplished through grid partitioning or subtractive clustering. To define the membership functions and fuzzy rules, the grid partition method is used, generating input membership functions by uniformly partitioning the input variable ranges and creating a single-output Sugeno fuzzy system. Each input membership function combination is represented by a single rule in the fuzzy rule base.

### 3.2.6. Wind speed estimation

Numerous research efforts have been undertaken to manage the operation of WTs. These studies primarily rely on wind speed sensors, like an anemometer, for control purposes. Nevertheless, these sensors
<figure><img src="image_9.png" /><figcaption>Fig. 6. ANFIS architecture.</figcaption></figure>

tend to provide unreliable and imprecise data due to two main factors. Fig. 7

* Firstly, the efficiency of wind speed sensors gradually diminishes over time, leading to increased overall maintenance requirements for the WT system [56].

* Secondly, as these sensors are strategically placed on the nacelle at specific points, the wind speed measurements they provide only capture the wind conditions at those specific locations [57]. This limitation stems from the single-point measurement nature of anemometers, potentially providing an inaccurate representation of the overall wind conditions across the entire rotor plane of the WT [58].

To overcome these limitations, this study proposes a WSE-based TS-ANFIS model to estimate the effective wind speed. The proposed TS-ANFIS model structure is automatically adjusted through a combination of least-square estimation and the backpropagation algorithm. Fig. 8 illustrates the proposed TS-ANFIS configuration designed for WSE. Table 1 provides the statistical characteristics of the data used to

develop an effective wind speed estimator, which is intended for the entire operational range of a baseline WT. The inputs for the suggested estimator include mechanical speed, mechanical power and TSR, with the output being the effective wind speed as determined by the TS-ANFIS model. The TS-ANFIS parameters for the wind speed estimator are presented in Table 2. In constructing the FIS, a grid partitioning approach was employed, involving the assignment of three MFs to each input variable and linear MFs to output. Additionally, Triangular MFs were employed, with a maximum value of 1 and a minimum value of 0, which three MFs for each input. The entire process of training and evaluating of the FIS is carried out using the fuzzy logic toolbox in MATLAB, the flow-chart of the ANFIS is shown in Fig. 7.

The trained MFs for TSR, mechanical speed, and mechanical power are represented in Figs. 9(a), (b), and (c) respectively. The training process for the WSE-based TS-ANFIS estimator employed a maximum of 200 epochs with a minimum error tolerance set to zero. The system was trained using a hybrid optimization method, which updates consequent parameters using the least square method in the forward pass and premise parameters using the backpropagation gradient descent method in the backward pass. The dataset used is divided into two subsets: the training data and the testing data.

The training data was meticulously selected to provide essential insights into the system's behavior, taking into account the statistical properties of the collected data as outlined in Table 1. This training dataset was utilized to train the ANFIS, and once the training process was completed, the system's performance was evaluated using the testing data. The training error was recorded in terms of the Root Mean Squared Error (RMSE). Fig. 9(d) illustrated the training error process of MFs.

**Remark:** Traditional approaches relying on complex mathematical models, such as differential equations, often struggle when dealing with uncertain and complex nonlinear systems. TS-ANFIS, on the other hand, operates without the need for precise mathematical models and leverages prior knowledge about the system to predict its future behavior. TS-ANFIS employs straightforward mathematical computations to address nonlinearities instead of complex differential equations. The effectiveness of a rule-based fuzzy system heavily relies on the expertise of the individual developing the IF-THEN fuzzy rules and their understanding of the system's behavior. However, TS-ANFIS possesses the capability to autonomously generate fuzzy rules by analyzing training data. Furthermore, TS-ANFIS ensures model compactness by utilizing a reduced number of fuzzy rules compared to conventional fuzzy models. This reduction in the number of rules, coupled with simpler

<figure><img src="image_10.png" /><figcaption>Fig. 7. Flow-chart of ANFIS.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Fig. 8. Architecture of the WSE-based TS-ANFIS model.</figcaption></figure>

<table><caption>**Table 1**
Analyzing statistical characteristics of a WT database.</caption><thead><tr><td>Input parameters</td><td>Maximum value</td><td>Minimum value</td><td>Average value</td></tr></thead><tbody><tr><td>Wind speed (m/s)</td><td>12.1863</td><td>7.18</td><td>9.9667</td></tr><tr><td>Mechanical power (W)</td><td>3500</td><td>2.0105e-07</td><td>2.4726e+03</td></tr><tr><td>Mechanical speed (rad/s)</td><td>157</td><td>1.0000e-05</td><td>141.1187</td></tr><tr><td>TSR</td><td>8.8696</td><td>6.3131e-07</td><td>8.0616</td></tr></tbody></table>

<table><caption>**Table 2**
ANFIS parameter information for wind speed estimator.</caption><thead><tr><td>Parameters</td><td>Value</td><td>Parameters</td><td>Value</td></tr></thead><tbody><tr><td>Number of training data pairs</td><td>100,001</td><td>Number of nodes</td><td>78</td></tr><tr><td>Number of nonlinear parameters</td><td>27</td><td>Number of fuzzy rules</td><td>27</td></tr><tr><td>Number of linear parameters</td><td>27</td><td>Total number of parameters</td><td>54</td></tr></tbody></table>

mathematical computations and support for parallel processing, not only reduces system complexity but also accelerates the estimation process.

### 3.2.7. MPPT-based TS-ANFIS controller

In Zone 2 of operation of the VSWT in Fig. 1, the WT rotor speed is constrained to reach an optimal TSR $\lambda_{opt}= 8.15$. The MPPT control strategy is used to regulate the electromagnetic torque of the generator. This adjustment is made to compel the mechanical speed to follow a reference value represented as $\Omega^*_m$. The proposed MPPT control strategy's architecture is illustrated in Fig. 10.

The main objective is to maximize the power extracted from the available kinetic wind power. To achieve this, a speed control mechanism, using the TS-ANFIS controller for the generator, is implemented. The maximum mechanical power can be attained when the system operates at its corresponding maximum power coefficient value $C_p$ and the optimal TSR $\lambda_{opt}$. Consequently, the desired speed of the generator is determined using the following equation:

$$ \Omega_m^* = \frac{\lambda_{opt} V_{est}}{R} \qquad (12) $$

Upon calculating the estimated wind speed with the WSE-based TS-ANFIS model, the optimal mechanical speed is ascertained using the derived wind speed estimate. To attain the maximum power point, the

MPPT-based TS-ANFIS controller is employed, which takes the mechanical speed and its reference as inputs. The controller then produces the electromagnetic torque reference, with its primary aim being to steer the mechanical speed toward its designated reference mechanical speed. The TS-ANFIS parameters for the MPPT method are presented in Table 3. Fig. 11 shows the ANFIS structure of the proposed MPPT method. The MPPT-based TS-ANFIS achieved through the creation of the FIS using grid partitioning. This process involves employing four MFs for each input variable and incorporating linear MFs for the output.

To update the antecedent and the premise parameters, a hybrid optimization method is employed, and a maximum of 200 epochs is selected with zero tolerance of errors. TS-ANFIS automatically generates four IF-THEN fuzzy rules by analyzing the training data.

## 4. XSG architecture

XSG serves as a design tool that enables designers to utilize the MathWorks model-based Matlab/Simulink design environment for configuring FPGAs. One notable advantage is that prior experience with Xilinx FPGA devices RTL design methods is not a prerequisite when working with XSG [55,59,60]. As a result, it reduces both the time required for design and the time it takes to bring a product to market. XSG automates downstream FPGA steps, encompassing RTL synthesis and implementation, to produce an FPGA programming bit-stream [12,60,61].

Within the Xilinx Block-set under Matlab/Simulink, numerous fundamental building blocks are available, including adders, multipliers, registers, and memories. With these blocks, designers can construct the architecture of their proposed algorithms. Once the design is complete and produces the desired simulation results, XSG offers the convenience of automatically generating Very-high-speed integrated circuits Hardware Description Language (VHDL) code. This generated VHDL code can then undergo synthesis and implementation on the FPGA. Fig. 12 provides an illustrative depiction of the design methodology employing XSG [62,63].

The proposed MPPT-based TS-ANFIS controller with PA control and WSE-based TS-ANFIS model are structured within the Matlab/Simulink environment, utilizing a specific Xilinx library. Once the design meets the simulation objectives, the XSG enables the automatic generation of VHDL code. In this study, all the necessary steps for FPGA implementation, including synthesis, placement, and routing, are performed to generate the programming file for the FPGA [64].
<figure><img src="image_12.png" /><figcaption>Fig. 9. (a) Trained MFs of mechanical speed, (b) Trained MFs of mechanical power, (c) Trained MFs of TSR, (d) Training error.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 10. Architecture of the MPPT control strategy.</figcaption></figure>

<table><caption>**Table 3**
TS-ANFIS parameter for MPPT method.</caption><thead><tr><td>Parameters</td><td>Value</td><td>Parameters</td><td>Value</td></tr></thead><tbody><tr><td>Number of training data pairs</td><td>100,001</td><td>Number of nodes</td><td>53</td></tr><tr><td>Number of nonlinear parameters</td><td>24</td><td>Number of fuzzy rules</td><td>16</td></tr><tr><td>Number of linear parameters</td><td>16</td><td>Total number of parameters</td><td>40</td></tr></tbody></table>

## 4.1. Design XSG architecture of the WSE-based TS-ANFIS and the MPPT-based TS-ANFIS with PA control algorithm

In Matlab environment, the TS-ANFIS editor graphical interface is accessible through the fuzzy logic toolbox. This toolbox empowers users to construct a FIS based on provided input/output data. The FIS's MF parameters are adjusted using a combination of the back propagation algorithm and the least squares method.

The Fig. 13 illustrates the MPPT-based TS-ANFIS algorithm featuring PA control and estimator-based TS-ANFIS model.

To ensure the turbine operates in MPPT mode, an optimal TSR of 8.15 is utilized to determine the optimal torque. At each calculation point, the power coefficient $C_p$ must be at its maximum value for the turbine to produce maximum power. This is achieved by identifying the optimal TSR corresponding to $C_{pmax}$ from the turbine's characteristic curve. Additionally, the mechanical speed reference, denoted as $\omega_{mref}$, is calculated using Eq. (12) with an optimal TSR of 8.15 and a blade radius $R$ of 2 m, as shown in Table 4.

Moreover, the input TSR is computed using Eq. (3), which accounts for variations in the mechanical speed of the machine influenced by the PA. The proposed PA control regulates the mechanical speed of the generator, particularly when the wind speed exceeds its nominal value. As wind speed surpasses the nominal threshold, the pitch angle $\beta$ increases, causing a reduction in both the power coefficient $C_p$ and the TSR, thereby limiting the mechanical speed.

The hardware XSG blocks of the PA control strategy in Fig. 14 are
<figure><img src="image_14.png" /><figcaption>Fig. 11. ANFIS structure for MPPT method.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig. 12. Design methodology for XSG.</figcaption></figure>

determined as per Eq. (6), with the nominal mechanical speed ωmn calculated as follows:

$$ \omega_{mn} = \frac{2 \times \pi \times F}{p} = \frac{2 \times \pi \times 50}{2} = 157(\text{rad/s}) \quad (13) $$

The trained MFs by the WSE-based TS-ANFIS model is illustrated in Fig. 9(a), (b), and (c). The initial step involves identifying all the MF based on their respective equation systems. For instance, if we take the MF labeled as MF2, it is defined by the Eq. (14) and depicted in Fig. 14.

$$ \mu_A(x) = \begin{cases} 0 & \text{if } x \le 0.05333 \\ \frac{x - 0.05333}{3.706 - 0.05333} & \text{if } 0.05333 \le x \le 3.706 \\ \frac{8.871 - x}{8.871 - 3.706} & \text{if } 3.706 \le x \le 8.871 \\ 0 & \text{if } x \ge 8.871 \end{cases} \quad (14) $$

The TS Fuzzy Logic Control (FLC) is constructed based on Min-Max rules, utilizing the MIN function as the AND operator to connect various fuzzy MFs. The MIN function is employed to determine the minimum output value from the mechanical speed, mechanical power, and TSR inputs. Eq. (15), modeled in Fig. 15, exemplifies a sample rule. The defuzzification is computed according to Eq. (16), determined based on the input values and their associated weights.

$$ \text{if } (\Omega_m \text{ is MF1}) \text{ and } (P_m \text{ is MF2}) \text{ and } (\lambda \text{ is MF2}) \text{ then } V \text{ is MF5} \quad (15) $$

$$ y = \frac{\sum X_{ij} \times Y_{ij}}{\sum X_{ij}} \quad (16) $$

## 5. Simulation results and discussion

To evaluate the efficacy of the proposed MPPT-based TS-ANFIS method with PA control strategy and WSE-based TS-ANFIS model for PMSG-based VSWT connected to the power grid, an extensive simulation study is conducted within the Matlab/Simulink environment. The evaluation and validation process focused on the efficiency of the WSE-based TS-ANFIS, which relies on three inputs: mechanical speed, mechanical power, and TSR. A lookup table was constructed to organize these inputs based on a predefined wind speed profile. This approach facilitates the assessment of the WSE-based TS-ANFIS against the original wind speed data, ensuring a comprehensive analysis of its performance.

The system is implemented using the XSG tool and subsequently validated through digital simulation. The foundation of our investigations relies on system parameters meticulously documented in Tables 4–6.

Moreover, the results obtained from these evaluations confirm the excellent tracking accuracy, rapid response time, and performance precision of the suggested solutions for sensorless WSE and MPPT control strategy of PMSG-based VSWT.
<figure><img src="image_16.png" /><figcaption>Fig. 13. XSG design of MPPT with PA control-based WSE.</figcaption></figure>

<table><caption>**Table 4**

Parameters of the WT.</caption><thead><tr><td>Parameters</td><td>Value</td></tr></thead><tbody><tr><td>Number of blades</td><td>3</td></tr><tr><td>Density of air (kg/m³), ρ</td><td>1.225 kg/m³</td></tr><tr><td>Blade radius (m), R</td><td>2</td></tr><tr><td>Rated power, P<sub>rat</sub></td><td>3.5 kW</td></tr><tr><td>Rated wind speed V<sub>rat</sub></td><td>11.4 m/s</td></tr></tbody></table>

### 5.1. Scenario 1: Slowly and constant wind speed variations

In this part, we examine the impact of the constant and slowly changes in wind speed on both the estimated wind speed and the MPPT with PA control system. Fig. 16 represents the wind speed profile of this scenario.

In Fig. 17 (a), the simulation results represent both the original and estimated wind speed, while in Fig. 17 (b), the WSE error is illustrated. The implementation of the proposed approach results in a close match between the estimated and reference wind speed and a high-level of accuracy in tracking the reference wind speed values, with minor
<figure><img src="image_17.png" /><figcaption>Fig. 14. XSG design of MF “MF2” in WSE-based TS-ANFIS model.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. 15. An example of rule with XSG blocks.</figcaption></figure>

<table><caption>**Table 5**
Parameters of the PMSG.</caption><thead><tr><td>Parameters</td><td>Value</td></tr></thead><tbody><tr><td>Resistance of stator, Rs</td><td>0.82 Ω</td></tr><tr><td>Inductance of the stator, Ls</td><td>15.1 mH</td></tr><tr><td>Pole pair number, p</td><td>2</td></tr><tr><td>Flux of the permanent magnet (Wb), φ</td><td>0.4832 Wb</td></tr></tbody></table>

<table><caption>**Table 6**
Parameters of the power grid.</caption><thead><tr><td>Parameters</td><td>Value</td></tr></thead><tbody><tr><td>Grid-side filter resistance</td><td>0.2 Ω</td></tr><tr><td>Grid-side filter inductance</td><td>25 mH</td></tr><tr><td>Grid frequency, F</td><td>50 Hz</td></tr><tr><td>Grid voltage</td><td>230 V</td></tr><tr><td>DC capacitance</td><td>2200 μF</td></tr><tr><td>Reference DC voltage</td><td>400 V</td></tr></tbody></table>

Moreover, the performance of the proposed approaches is evaluated in three distinct scenarios of variable wind speed conditions:

* Scenario 1: slowly and constant wind speed variations.

* Scenario 2: highly and sudden wind speed variations.

* Scenario 3: random wind speed variations.

<figure><img src="image_19.png" /><figcaption>Fig. 16. Wind speed of scenario 1.</figcaption></figure>

fluctuations. These results affirm the proposed WSE-based TS-ANFIS model exceptional capability to estimate effective wind speed, highlighting its accuracy and robustness as a reliable estimator.

Fig. 18(a) and (b) depict the variations in power coefficient ($C_p$) and TSR, respectively. It is evident that the MPPT-based TS-ANFIS algorithm
<figure><img src="image_20.png" /><figcaption>Fig. 17. (a) Original and estimated wind speed, (b) Wind speed error between the original and estimated.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Fig. 18. (a) Power coefficient, (b) TSR.</figcaption></figure>

consistently seeks the maximum power point. In this specific scenario, the wind speed remains below its rated value, resulting in an optimal beta value of zero, as shown in Fig. 19, and a TSR of 8.15. Notably, TS-ANFIS effectively tracks the optimal values of $C_p$, $\beta$, and TSR, out-performing the PID method. For instance, at $t=4$ s, the $C_p$ of the PID controller does not reach the optimal $C_p$ value. The active power injected

into the grid and the reactive power injected into the grid are shown in Fig. 20 (a) and (b), respectively. Fig. 20 (a) shows the difference between MPPT-based PID and TS-ANFIS controllers in terms of the injected active power. As can be seen, the MPPT-based TS-ANFIS controller is able to inject more power into the grid than the MPPT-based PID controller. It also appears to be more stable.

<figure><img src="image_22.png" /><figcaption>Fig. 19. (a) Mechanical speed, (b) Electromagnetic torque.</figcaption></figure>
<figure><img src="image_23.png" /><figcaption>Fig. 20. (a) Active power supplied to the power grid, (b) Reactive power injected to the power grid.</figcaption></figure>

## 5.2. Scenario 2: Highly and sudden wind speed variations

In this scenario, we focus on assessing the performance of the proposed WSE-based TS-ANFIS model and MPPT-based TS-ANFIS controller when confronted with rapid and significant fluctuations in wind speed.

The same set of estimated parameters utilized in the scenario of slowly wind speed variations is maintained. Specifically, the wind speed experiences a sudden increase from 10 to 14 m/s at 2 s and subsequently drops to 7 m/s at 7 s. The wind speed then remains constant until 12 s, after which it abruptly rises to 10 m/s, as depicted in Fig. 21.

Fig. 22 illustrates a comparison between the original and estimated values of the wind speed profile along with the corresponding error, in response at the aforementioned rapid changes in wind speed. As anticipated, the results align closely with those obtained in situations involving gradual wind speed changes. These outcomes affirm the effectiveness of the proposed WSE-based TS-ANFIS model algorithm in estimate the wind speed even in the presence of sudden and substantial wind speed variations.

Figs. 23(a) and 23(b) depict the power coefficient ($C_p$) and TSR, respectively. During the initial period from $t=0$ s to $t=2$ s, the wind speed remains below the rated nominal value. Consequently, the TSR is adjusted to the optimal value of 8.15, and the $C_p$ is set to 0.4794 using the MPPT-based TS-ANFIS controller. This showcases a swift and efficient response, with the system converging rapidly to the optimal values of $C_p$ and TSR. However, at $t=2$ s, a sudden increase in wind speed exposes a limitation in the MPPT-based PID controller, evident in Figs. 23 and 24. While the controller performs well under typical conditions, its

effectiveness diminishes when faced with extraordinary events. The abrupt surge in wind speed exceeds the controller's adaptive capabilities, potentially due to constraints in its PID gains, reference tracking, or sensitivity to sudden changes. In stark contrast, the MPPT-based TS-ANFIS controller demonstrates remarkable effectiveness and performance in this scenario, outperforming the MPPT-based PID controller in terms of tracking accuracy, precision, and response time.

Additionally, at $t = 2$ s, the PA control system is activated, causing a reduction in the power coefficient $C_p$ and TSR. It is evident that this control mechanism is designed to increase the PA when the wind speed exceeds the rated wind speed (11.4 m/s). This adjustment is made to safeguard the WT from overloading by limiting the mechanical power to its rated value (3.5 kW). As illustrated in Fig. 25, the effectiveness of the TS-ANFIS method in optimizing the active power injected into the power grid is clearly demonstrated, surpassing the performance of the conventional PID controller. It shows that the proposed method has better power-capturing performance in comparison with the PID method.

## 5.3. Scenario 3: random wind speed variations

In Scenario 3, the purpose is directed towards evaluating the resilience of the suggested estimation method of the wind speed and the effectiveness of the TS-ANFIS strategy-based MPPT algorithm in the presence of random wind speed variations, as depicted in Fig. 26. Unlike the previous scenarios, where wind speed changes followed a specific pattern, this scenario introduces a degree of unpredictability with fluctuations occurring without a discernible pattern. This randomness in wind speed variations poses a unique challenge for wind speed estimation and control strategies.

Fig. 27 depicts a comparison between the original and estimated values of the wind speed profile, along with the associated error, in response to the rapid changes in wind speed mentioned earlier. As expected, the findings closely match those obtained in scenario 3 with gradual wind speed variations. These results validate the efficacy of the proposed algorithms in accurately estimating wind speed, even when faced with randomly fluctuations in wind speed.

Fig. 28 (a) and (b) illustrate the behavior of the power coefficient and the TSR, respectively. Notably, when the wind speed is below the rated value, the power coefficient converges toward its optimal value. Simultaneously, the TSR approaches to its optimal value, accompanied by a PA ($\beta$) set to zero to operate in the maximum power point. However, at $t = 5$ s, the wind speed surpasses the rated level, initiating the activation of PA control, as shown in Fig. 29 (b). Consequently, the PA increases proportionally to the wind speed exceeding its rated value, resulting in a discernible decrease in the $C_p$. This dynamic interplay between wind speed, PA control, and the resulting power coefficient

<figure><img src="image_24.png" /><figcaption>Fig. 21. Wind speed of scenario 2.</figcaption></figure>
<figure><img src="image_25.png" /><figcaption>Fig. 22. (a) Reference and estimated wind speed, (b) Wind speed error between the reference and the estimated value.</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>Fig. 23. (a) Power coefficient, (b) TSR.</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>Fig. 24. (a) Mechanical speed, (b) Pitch angle.</figcaption></figure>

highlights the system's adaptive capacity, optimizing power capture in response to fluctuations in the ambient wind environment. As a result, the mechanical speed is subject to limitations to safeguard the turbine, as depicted in Fig. 29 (a). Furthermore, there is an imposed restriction

stipulating that the generated power is confined to its nominal value of 3.5 kW.

The obtained results indicate that the MPPT-based TS-ANFIS method outperforms the MPPT-based PID controller in terms of tracking
<figure><img src="image_28.png" /><figcaption>Fig. 25. (a) Active power supplied to the grid, (b) Reactive power injected to the grid.</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>Fig. 26. Wind speed of scenario 3.</figcaption></figure>

accuracy, adaptability, and precision. Additionally, the response time of the TS-ANFIS method is 0.01 s, whereas the PID controller yields a response time of 0.1 s, making the TS-ANFIS technique exhibit a faster response time and convergence. The MPPT-based TS-ANFIS method exhibits a remarkable edge in the active power injected to the power

grid, as depicted in Fig. 30 (a) compared to its counterpart, the MPPT-based PID controller. Notably, at $t = 2$ s, the MPPT-based TS-ANFIS system generates a substantial 3192 W of injected active power, showing a noteworthy disparity from the PID controller’s 3090 W output. This significant variance underscores the pronounced efficacy of the TS-ANFIS method in not only capturing but also enhancing power output, solidifying its position as a superior choice for MPPT algorithm. The ability to consistently extract more power from the wind source is a crucial aspect of improving the efficiency and overall performance of WT, making the MPPT algorithm based on TS ANFIS an enticing choice for renewable energy applications.

Despite this inherent unpredictability, the proposed solutions exhibit notable adaptability and precision. The evaluated parameters remain consistent with those employed in the previous scenarios, ensuring a fair comparison. The results obtained from this scenario not only underscore the versatility of the approaches but also emphasize their potential applicability in real-world conditions where wind speed changes follow irregular patterns.

To provide additional clarity regarding the estimation outcomes, a range of statistical properties has been meticulously examined. These properties encompass the mean absolute deviation, denoted as $E[|ε|]$, the standard deviation of the estimation error, symbolized as $\sigma_ε$, and the Correlation Coefficient (CC) measuring the relationship between original and estimated wind speed. The summarized statistical findings are presented in Table 7.

<figure><img src="image_30.png" /><figcaption>Fig. 27. (a) Original and estimated wind speed, (b) Wind speed error between the original and estimated.</figcaption></figure>
<figure><img src="image_31.png" /><figcaption>Fig. 28. (a) Power coefficient, (b) TSR.</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>Fig. 29. (a) Mechanical speed, (b) Pitch angle.</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>Fig. 30. (a) Active power supplied to the grid, (b) Reactive power injected to the grid.</figcaption></figure>
<table><caption>**Table 7**
Performance of WSE-based TS-ANFIS for three scenarios.</caption><thead><tr><th>Wind speed (m/s)</th><th>Criteria</th><th>Our proposed TS-ANFIS</th></tr></thead><tbody><tr><td rowspan="3">4</td><td>E[|ε|]</td><td><b>0.0002941</b></td></tr><tr><td>σ<sub>e</sub></td><td><b>0.0008451</b></td></tr><tr><td>CC</td><td><b>0.9999752</b></td></tr><tr><td rowspan="3">8</td><td>E[|ε|]</td><td><b>0.0010121</b></td></tr><tr><td>σ<sub>e</sub></td><td><b>0.0041210</b></td></tr><tr><td>CC</td><td><b>0.9999673</b></td></tr><tr><td rowspan="3">14</td><td>E[|ε|]</td><td><b>0.0003510</b></td></tr><tr><td>σ<sub>e</sub></td><td><b>0.0009566</b></td></tr><tr><td>CC</td><td><b>0.9998975</b></td></tr></tbody></table>

$$E[|\epsilon|] = \frac{\sum_{j=1}^{k} |\epsilon_j|}{k} \qquad (18)$$

$$\sigma_{\epsilon} = \sqrt{\frac{\sum_{j=1}^{k} (\epsilon_j - \bar{\epsilon})^2}{k}} \qquad (19)$$

The correlation coefficient CC is expressed as follows [65]:

$$CC(A, B) = \frac{1}{M-1} \sum_{k=1}^{M} \left( \frac{A_k - E(A)}{\sigma_A} \right) \left( \frac{B_k - E(B)}{\sigma_B} \right) \quad (20)$$

where σA and E(A) are the standard deviation and mean of X, respectively, and σB and E(B) are the standard deviation and mean of B, respectively.

Table 7 represent the performance the WSE-based TS-ANFIS model, the result obtained that CC is higher and low E[|ε σ_ε| indicate that the WSE-based TS-ANFIS demonstrates performance and precision in estimating effective wind speed. The ability of the proposed methods to handle random wind speed variations further solidifies their robustness and reliability in the real-time of sensor-less control for wind speed estimation in PMSG-based systems.

Table 8 illustrates an overcall comparative evaluation between the MPPT-based PID and TS-ANFIS controllers. The MPPT-based TS-ANFIS controller demonstrates superior precision and tracking accuracy, achieving a static error of 0.02 %, a response time of 0.001 s, and a negligible overshoot. In contrast, the PID controller exhibits a static error of 0.25 % and a response time of 0.02 s. These results underscore the outstanding performance, precision, and rapid MPPT capabilities of the TS-ANFIS method.

## 5.4. Overall VS WT efficiency

The efficacy of the MPPT algorithm in the investigated control system is affirmed through validation using both PID and the proposed TS-ANFIS controllers. This validation serves to illustrate the superior performance and significance of employing the TS-ANFIS system. Furthermore, to gauge the overall efficiency of the VSWT, a comparative analysis is carried out between the TS-ANFIS and PID methods, focusing on the efficiency of active power injected to the power grid. This efficiency is quantified by the following equation [66]:

$$\eta_{p-\text{sys}} = \frac{\int_{t_{\text{initiale}}}^{t_{\text{final}}} P_r dt}{\int_{t_{\text{initiale}}}^{t_{\text{final}}} P_{th} dt} \times 100\% \qquad (17)$$

where $P_r$ is the injected active power and $P_{th} = \frac{1}{2} \rho \pi R^2 v^3 C_p \max$ represents

<table><caption>**Table 8**
Overall comparative evaluation of two MPPT techniques performance.</caption><thead><tr><th>MPPT algorithm</th><th>Set-point tracking accuracy</th><th>Static error (%)</th><th>Overshoot (%)</th><th>Response time (s)</th></tr></thead><tbody><tr><td>PID</td><td>Low</td><td>0.25</td><td>Important (≈21 %)</td><td>0.02</td></tr><tr><td>TS-ANFIS</td><td>Excellent</td><td>0.02</td><td>Neglected (≈3%)</td><td>0.001</td></tr></tbody></table>

the optimal mechanical power associated with the wind speed profile.

The active power injected to the power grid efficiency is determined as below for the TS-ANFIS and PID controllers:

$$\eta_{p-\text{sys}}(\text{PID}) = 92.63\%$$

$$\eta_{p-\text{sys}}(\text{TS-ANFIS}) = 99.84\%$$

The PID controller exhibits an efficiency of 92 %, while the TS-ANFIS controller demonstrates a higher efficiency of 99 %. This difference in efficiency suggests that the TS-ANFIS controller is more effective at optimizing power capture and minimizing losses compared to the PID controller.

## 5.5. Comparative study with other published works

In this section, a thorough comparison of the proposed WSE and the MPPT control algorithm in WT against other works published in the literature is discussed.

### 5.5.1. WSE

To provide additional clarity regarding the estimation outcomes in comparison with other published works, we include the mean absolute deviation denoted as E[|ε|], the standard deviation of the estimation error symbolized as σ_ε, and the Correlation Coefficient (CC) measuring the relationship between the original and estimated wind speed. The comparison with the proposed WSE and other published works is summarized in Table 8.

In addition, for more comparison in estimating models, the predominant metric used to evaluate performance is often RMSE. A higher RMSE is generally considered undesirable, signifying less accurate estimates, while a lower RMSE is indicative of better estimation accuracy. The formula for RMSE is provided as follows [11]:

$$RMSE = \sqrt{\frac{1}{k} \sum_{j=1}^{k} (X_j - Y_j)^2} \qquad (21)$$

The determination coefficient, often denoted as R², establishes robust connection residuals, indicating the extent of relationship between original and estimated value [65]. Ranging from 0 to 1, and R² value, such as 0.99, signifies that 99 % of the variance in the expected variables is explained by R² within the overall observed responses of the data. The straightforward formula for calculating R² is provided in Eq. (22). Moreover, The Pearson correlation coefficient (r) is employed to assess the linear relationship between estimated wind speed and the original. The Pearson correlation coefficient r ranges from -1-1, with higher values indicating stronger correlations. A value closer to 1 signifies a strong positive linear relationship, while values near 0 suggest a lack of correlation. This statistical measure facilitates a robust comparison of the proposed wind speed estimation against established methods, offering insights into accuracy and reliability. A higher Pearson correlation coefficient underscores the effectiveness of the proposed approach, providing valuable information for researchers and practitioners in the field of wind energy and meteorology. The expression of the r is defined in Eq. (23) [67].

$$R^2 = 1 - \frac{\sum_{j=1}^{k} (X_j - Y_j)^2}{\sum_{j=1}^{k} (\bar{Y} - Y_j)^2} \qquad (22)$$

$$r = \frac{k \left( \sum_{j=1}^{k} Y_j X_j \right) - \left( \sum_{j=1}^{k} Y_j \right) \cdot \left( \sum_{j=1}^{k} X_j \right)}{\sqrt{\left( k \sum_{j=1}^{k} Y_j^2 - \left( \sum_{j=1}^{k} Y_j \right)^2 \right) \cdot \left( k \sum_{j=1}^{k} X_j^2 - \left( \sum_{j=1}^{k} X_j \right)^2 \right)}} \qquad (23)$$

where k is the size of the test data, and $X_j$ and $Y_j$ are the estimated wind speed and original wind speed values, respectively.

Table 9 highlights the consistent superiority of the proposed estimator over the Kalman Filter (KF), Unknown Input Disturbance
<caption>**Table 9**</caption>

Comparative study between the estimator based on TS-ANFIS and other published works.

<table><thead><tr><th>Wind speed (m/s)</th><th>Criteria</th><th>KF [41]</th><th>UIDOB-ELM [41]</th><th>DOB-MLP [68]</th><th>Our proposed TS-ANFIS</th></tr></thead><tbody><tr><td rowspan="3">4</td><td>E[|e|]</td><td>0.1166744</td><td>0.0223197</td><td>0.0033654</td><td><strong>0.0002941</strong></td></tr><tr><td>σ<sub>e</sub></td><td>0.1474021</td><td>0.0379211</td><td>0.0061566</td><td><strong>0.0008451</strong></td></tr><tr><td>CC</td><td>0.9327350</td><td>0.9971221</td><td>0.9999275</td><td><strong>0.9999752</strong></td></tr><tr><td rowspan="3">8</td><td>E[|e|]</td><td>0.3064720</td><td>0.0696266</td><td>0.0049361</td><td><strong>0.0010121</strong></td></tr><tr><td>σ<sub>e</sub></td><td>0.3995568</td><td>0.1221717</td><td>0.0122540</td><td><strong>0.0041210</strong></td></tr><tr><td>CC</td><td>0.9217446</td><td>0.9932722</td><td>0.9999162</td><td><strong>0.9999673</strong></td></tr><tr><td rowspan="3">14</td><td>E[|e|]</td><td>0.5437899</td><td>0.0936600</td><td>-</td><td><strong>0.0003510</strong></td></tr><tr><td>σ<sub>e</sub></td><td>0.7400401</td><td>0.1634251</td><td>-</td><td><strong>0.0009566</strong></td></tr><tr><td>CC</td><td>0.8594187</td><td>0.9920963</td><td>-</td><td><strong>0.9998975</strong></td></tr></tbody></table>

Observer (UIDOB) Extreme Learning Machine (ELM), and Disturbance Observer-based Multi-Layer Perceptron (DOB-MLP) estimators, as reflected in consistently lower values for both E[|e|] and σ<sub>e</sub>. Furthermore, all CC results indicate higher values for the suggested estimator-based TS-ANFIS method compared to other estimators. This trend underscores the superior performance of the proposed TS-ANFIS relative to the KF, UIBOD-ELM, and DOB-MLP estimators.

The RMSE, R², and r between the original wind speed and the estimated wind speed by the WSE-based TS-ANFIS method consistently exhibit lower values compared to those of the Genetic Programming (GP), Support Vector Machine with Radial Basis kernel Function (SVM-RBF), ANN, and ELM estimators, as summarized in Table 10. These findings underscore the superior performance and effectiveness of the proposed estimator relative to other methods, providing compelling evidence of its efficacy in wind speed estimation.

Table 9 shows that the WSE-based TS-ANFIS model has strong estimation outcomes and accuracy due to its low RMSE and higher R² and r values as compared to other methods of study [62] which used the GP, SVM-RBF, ANN, and EL.

### 5.5.2. MPPT control strategy

Table 11 illustrates a comparative analysis between the novel MPPT-based TS-ANFIS approach and various existing control designs within the literature for the wind system MPPT algorithm. These include Proportional Integral (PI), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Sliding Mode Control (SMC), Integral SMC (ISMC), Backstepping, Optimal Torque Control (OTC) based on Proportional Integral (PI) (OTC-PI), and OTC based on Artificial Neural Network (OTC-ANN). This analysis is conducted with a focus on parameters such as static error, set-point tracking accuracy, and response time.

In Table 11, it is evident that the proposed MPPT-based TS-ANFIS controller outperforms alternative methods in terms of tracking accuracy, achieving a minimal static error of 0.02 %. Additionally, it demonstrates a faster response time, registering a remarkable 0.001 s response time as compared to other methods.

## 6. FPGA implementation

The proposed MPPT-based TS-ANFIS controller with PA control and WSE-based TS-ANFIS model are developed using the XSG tool and

<caption>**Table 10**</caption>

Comparative study between the estimator based on TS-ANFIS and other published works.

<table><caption>**Table 11**</caption><thead><tr><th></th><th>RMSE</th><th>R<sup>2</sup></th><th>r</th></tr></thead><tbody><tr><td>GP [67]</td><td>2.439442</td><td>0.9928</td><td>0.996389</td></tr><tr><td>SVM-RBF [67]</td><td>2.430239</td><td>0.9927</td><td>0.996363</td></tr><tr><td>ANN [67]</td><td>2.441259</td><td>0.9929</td><td>0.996468</td></tr><tr><td>ELM [67]</td><td>0.540149</td><td>0.9996</td><td>0.9998</td></tr><tr><td><strong>Our proposed TS-ANFIS</strong></td><td><strong>0.0085381</strong></td><td><strong>0.99985</strong></td><td><strong>0.99996</strong></td></tr></tbody></table>

Comparative study between the MPPT-based TS-ANFIS algorithm and other published works.

<table><thead><tr><th>References</th><th>MPPT algorithm</th><th>Set-point tracking accuracy</th><th>Static error (%)</th><th>Response time (s)</th></tr></thead><tbody><tr><td rowspan="3">[69]</td><td>PI</td><td>Good</td><td>6.2</td><td>0222</td></tr><tr><td>FLC</td><td>Very good</td><td>0.2</td><td>0023</td></tr><tr><td>ANN</td><td>Excellent</td><td>0.06</td><td>0.0045</td></tr><tr><td>[70]</td><td>ISMC</td><td>Good</td><td>-</td><td>0.28</td></tr><tr><td>[71]</td><td>Backstepping</td><td>Very good</td><td>1.1</td><td>0.005</td></tr><tr><td>[72]</td><td>OTC-PI</td><td>-</td><td>-</td><td>0.07</td></tr><tr><td>[73]</td><td>OTC-ANN</td><td>-</td><td>-</td><td>0.035</td></tr><tr><td rowspan="4">[73]</td><td>PI</td><td>Medium</td><td>0.241</td><td>0.041</td></tr><tr><td>Backstepping</td><td>Good</td><td>0.175</td><td>0.020</td></tr><tr><td>SMC</td><td>Very Good</td><td>0.198</td><td>0.015</td></tr><tr><td>ANN</td><td>Very Good</td><td>0.103</td><td>0.0035</td></tr><tr><td><strong>Proposed method in this work</strong></td><td><strong>TS-ANFIS</strong></td><td><strong>Excellent</strong></td><td><strong>0.02</strong></td><td><strong>0.001</strong></td></tr></tbody></table>

implemented on the FPGA Zed-Board platform. The system generator employs a block-based methodology in conjunction with Matlab/Simulink environment. Under XSG, the structure of the MPPT algorithm, PA control, and WSE is meticulously designed using logical, elementary, and mathematical blocks. The XSG architecture description language assumes a pivotal role by offering essential features for seamless communication with Matlab/Simulink. This capability allows the creation of a virtual operational scenario, facilitating rapid approximation and validation of the hardware implementation through co-simulation testing. Beyond verifying the synthesized control strategy, this approach significantly reduces the time required for experimental tests, enhancing overall implementation efficiency. XSG proves to be an relevant tool, integrating seamlessly with Simulink to expedite the validation process and optimize the implementation of the control strategy, thereby streamlining the entire development cycle.

### 6.1. Hardware-in-the-loop co-simulation

Firstly, the initial phase involves simulating the XSG architecture designed for the proposed approaches in real-time using the Matlab/Simulink environment version 2017b and the Vivado tool version 18.1. Secondly, the VHDL code for the designed system is generated and undergoes the synthesis process. Finally, the functionality and performance of the suggested algorithms can be tested within the FPGA hardware environment, utilizing the hardware co-simulation method.

Co-simulation of hardware serves as an intermediary stage that bridges the gap between initial digital simulation and the ultimate practical realization through experimental testing, as illustrated in Figs. 31 and 32. The main benefits of implementing the hardware co-simulation approach include risk mitigation by preventing potential damage, ensuring the success of experimental operations, shortening the overall duration required for conclusive experimental validation, and lowering both prototyping time and the costs associated with system design. The hardware co-simulation process relies on the Vivado tool in conjunction with a core generator to produce and synthesize the programming bit file (bitstream file) required for the FPGA.

In the hardware co-simulation phase, a Joint Test Action Group (JTAG) module called 'JTAG co-sim' is created to serve as an equivalent to the XSG block within the proposed methods, positioned between the gateway-in and gateway-out components. This newly generated JTAG co-sim block supersedes the prior architecture of the suggested algorithms. The overall schematic of the hardware co-simulation process is depicted in Fig. 31.

As indicated in Fig. 31, the JTAG co-sim is connected to the hardware co-simulation process. It begins by establishing a connection between the Xilinx FPGA platform and the computer through a USB-JTAG
<figure><img src="image_34.png" /><figcaption>Fig. 31. Hardware co-simulation architecture.</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>Fig. 32. Hardware co-simulation for the suggested approaches.</figcaption></figure>

programming cable. The next step involves downloading the bit-stream via the JTAG cable. Fig. 32 further illustrates the connection of the FPGA board to the computer through the JTAG cable and USB port. During co-simulation, the XSG module reads the measuring mechanical speed, mechanical power, and TSR data from the system model and transmits them to the design implemented on the FPGA board through the JTAG connection. Following execution, the board sends its outputs—the reference electromagnetic torque and the PA (β) via the JTAG cable to the system generator. After co-simulation is finished, the results are retrieved in Figs. 33 to 35.

As shown in Figs. 33 to 35, the matching precision between the hardware co-simulation results and those obtained from Matlab/Simulink environment underscores the effectiveness of the proposed approaches. The hardware co-simulation aligns closely with the Matlab/Simulink results not only validates the implementation on the FPGA but also serves as a testament to the robustness of the proposed MPPT algorithm and the estimator model. The consistency in outcomes across these two aspects reinforces confidence in the reliability of the MPPT-based TS-ANFIS method with PA control and the estimator-based TS-ANFIS model when executed on the FPGA Zed-Board platform.

This congruity between simulated and hardware-based results not only validates the initial simulation but also supports the notion that the hardware implementation faithfully reproduces the expected behaviors. It serves as a crucial step in ensuring that the FPGA-based system performs as anticipated and meets the specifications defined in the Matlab/Simulink environment. The alignment in results fortifies the claim that the proposed co-simulation methodology is efficient and dependable for validating the FPGA implementation of the complex control strategy.

## 6.2. Hardware cost and device utilization

Following the successful implementation of the proposed system, Table 11 furnishes a detailed overview of the FPGA device resources used. The summary report delineates the hardware resource utilization necessary for the proposed methods, comparing them against the available resources on the FPGA Zed-Board. Consequently, the allocated hardware resources on the FPGA board are considered satisfactory and unburdened, showcasing a utilization rate of 46.605% for the slice lookup tables (LUTs), 17.492% for the Flip-Flop (FF), 0.224% for the LUTRAM in contrast to the available configuration on the FPGA board. Furthermore, the results highlight the cost-effectiveness achieved through FPGA implementation. The architecture of the proposed design not only occupies a reduced area but also delivers favorable performance. Indeed, the real-time FPGA implementation can find application in diverse industrial processes for WT.

Additionally, the efficacy of the implemented methods is assessed through the analysis of speed metrics, including throughput (as determined by Eq. 24), maximum frequency (computed using Eq. 25), and efficiency (determined by Eq. 26). Furthermore, the efficiency of each version, measured as throughput/slices, is provided. This efficiency parameter offers a comprehensive understanding of the overall hardware metrics performance of the implementation.

$$ \text{Freq}_{\text{Max}} = \frac{1}{T - \text{WNS}} [\text{MHz}] \qquad (24) $$

with a target clock period (T), and the parameters WNS represent the Worst Negative Slack within the intra-clocks path section.

$$ \text{Throughput} = N \times \text{Freq}_{\text{Max}} [\text{Mbps}] \qquad (25) $$

$$ \text{Efficiency} = \frac{\text{Throughput}}{\text{Slices}} [\text{Mbps/Slices}] \qquad (26) $$

According to the synthesis findings presented in Table 12, the suggested architecture exhibits efficient resource utilization on the FPGA Zed-Board, featuring a remarkable clock frequency of 80.38 MHz, a power consumption of 0.215 W, and a substantial throughput of 2572.16 megabits per second.

## 7. Conclusion

In this work, a WSE-based TS-ANFIS model for accurate wind speed estimation and a MPPT-based TS-ANFIS algorithm to maximize power extraction from a PMSG-based VSWT connected to the power grid is introduced. Integration of a PA control system with the MPPT-based TS-ANFIS proved effective in optimizing power extraction while mitigating risks associated with high wind speed. Both TS-ANFIS methods for the MPPT algorithm and the WSE underwent 200 epochs of training, utilizing a hybrid optimization method for optimal outcomes with minimal training error. Performance evaluation of the MPPT control strategy and the WSE was conducted through simulation in the Matlab/Simulink environment using XSG tools and hardware co-simulation on the FPGA Zed-Board platform. Diverse wind speed scenarios were considered for a thorough assessment of the proposed approaches. The findings demonstrated that the MPPT-based TS-ANFIS controller outperformed the conventional PID controller in tracking the maximum power point with superior rapid response time and precision. Additionally, compared to
<figure><img src="image_36.png" /><figcaption>Fig. 33. Hardware Co-simulation of: (a) Wind speed estimation, (b) Power coefficient.</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>Fig. 34. Hardware Co-simulation of: (a) Tip speed ratio, (b) Mechanical speed.</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>Fig. 35. Hardware Co-simulation of: (a) Injected active power, (b) Injected reactive power.</figcaption></figure>
<table><caption>**Table 12**
Synthesis of hardware resource utilization.</caption><thead><tr><th></th><th></th><th>Utilization</th><th>Available</th><th>Utilization %</th></tr></thead><tbody><tr><td rowspan="3">Recourses</td><td>FF</td><td>18,612</td><td>106,400</td><td>17.492</td></tr><tr><td>LUT</td><td>24,794</td><td>53,200</td><td>46.605</td></tr><tr><td>LUTRAM</td><td>39</td><td>17,400</td><td>0.224</td></tr><tr><td rowspan="2">Speed</td><td>Freq<sub>Max</sub>(Mhz)</td><td>80.38</td><td>-</td><td>-</td></tr><tr><td>Throughput (Mbps)</td><td>2572.16</td><td>-</td><td>-</td></tr><tr><td colspan="2">Power consumed (W)</td><td>0.215</td><td>-</td><td>-</td></tr></tbody></table>

other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. In fact, the proposed MPPT-based TS-ANFIS method extracts the maximum power produced by the WECS with an efficiency estimated at 99.84 %, whereas the PID method records efficiencies of 92.63 %. It is confirmed that the MPPT-based TS-ANFIS controller injected more power to the power grid compared to the PID controller. This enhanced performance underscores the efficacy of the proposed approach in optimizing the energy capture efficiency of the PMSG-based WT under varying wind conditions. Thus, a hardware co-simulation on an FPGA Zed-Board demonstrated that the architecture occupies only a few hardware resources, with 17.492 % FF, 46.605 % LUT, and 0.224 LUTRAM, and satisfies real-time requirements, such as a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps. This approach minimizes risks, ensures experimental setup safety, and significantly reduces the time required for prototyping and experimentation.

Future work will focus on applying the Zebra Optimization Algorithm (ZOA) to improve the initial parameters and outcomes in the MPPT-based TS-ANFIS algorithm. ZOA, recognized as one of the most powerful bio-inspired metaheuristic algorithms introduced in 2022, will be integrated with MPPT-based TS-ANFIS algorithm to enhance MPP tracking accuracy and efficiency. This combined approach has the potential to optimize power generation by accurately tracking the MPP under various operating conditions.

## Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## CRediT authorship contribution statement

**Saber KRIM:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Investigation. **Youssef Kraiem:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Investigation. **Mohamed Ali HAJJAJI:** Visualization, Validation, Supervision. **Mahdi HERMASSI:** Writing – review & editing, Writing – original draft, Validation, Software, Methodology, Formal analysis, Data curation, Conceptualization.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## References

[1] O. Ogunrinde, E. Shittu, K.K. Dhanda, Investing in renewable energy: reconciling regional policy with renewable energy growth, IEEE Eng. Manag. Rev. 46 (4) (2018) 103–111.

[2] M. Hedayati-Mehdiabadi, J. Zhang, K.W. Hedman, Wind power dispatch margin for flexible energy and reserve scheduling with increased wind generation, IEEE Trans. Sustain. Energy 6 (4) (2015) 1543–1552.

[3] Y. Krim, D. Abbes, Impact of a PMSG inter-turn short-circuits fault on a grid-connected variable-speed wind energy conversion system, Wind Eng. 46 (6) (2022) 1775–1791.

[4] B. Meghni, D. Dib, A.T. Azar, A second-order sliding mode and fuzzy logic control to optimal energy management in wind turbine with battery storage, Neural Comput. Appl. 28 (2017) 1417–1434.

[5] Y. Krim, et al., Classical vector, first-order sliding-mode and high-order sliding-mode control for a grid-connected variable-speed wind energy conversion system: a comparative study, Wind Eng. 42 (1) (2018) 16–37.

[6] M. Abdelrahem, C.M. Hackl, R. Kennel, Finite position set-phase locked loop for sensorless control of direct-driven permanent-magnet synchronous generators, IEEE Trans. Power Electron. 33 (4) (2017) 3097–3105.

[7] Y. Zhao, et al., A review on position/speed sensorless control for permanent-magnet synchronous machine-based wind energy conversion systems, IEEE J. Emerg. Sel. Top. Power Electron. 1 (4) (2013) 203–216.

[8] V. Yaramasu, et al., A new power conversion system for megawatt PMSG wind turbines using four-level converters and a simple control scheme based on two-step model predictive strategy—Part I: modeling and theoretical analysis, IEEE J. Emerg. Sel. Top. Power Electron. 2 (1) (2013) 3–13.

[9] M.M. Mahmoud, et al., A combination of an OTC based MPPT and fuzzy logic current control for a wind-driven PMSG under variability of wind speed, Energy Syst. 13 (4) (2022) 1075–1098.

[10] J. Pande, et al., A review of maximum power point tracking algorithms for wind energy conversion systems, J. Mar. Sci. Eng. 9 (11) (2021) 1187.

[11] M. Hermassi, et al., Design of vector control strategies based on fuzzy gain scheduling PID controllers for a grid-connected wind energy conversion system: hardware FPGA-in-the-loop verification, Electronics 12 (6) (2023) 1419.

[12] M. Hermassi, et al., Hardware FPGA implementation of an intelligent vector control technique of three-phase rectifier for wind turbine connected to the grid. Proceedings of the IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), IEEE, 2022.

[13] Y. Krim, et al., Power management and second-order sliding mode control for standalone hybrid wind energy with battery energy storage system, Proc. Inst. Mech. Eng., Part I: J. Syst. Control Eng. 232 (10) (2018) 1389–1411.

[14] J. Castelló, J.M. Espí, R. García-Gil, Development details and performance assessment of a wind turbine emulator, Renew. Energy 86 (2016) 848–857.

[15] A. Ghaffari, M. Krstić, S. Seshagiri, Power optimization and control in wind energy conversion systems using extremum seeking, IEEE Trans. Control Syst. Technol. 22 (5) (2014) 1684–1695.

[16] F.E.V. Taveiros, L.S. Barros, F.B. Costa, Back-to-back converter state-feedback control of DFIG (doubly-fed induction generator)-based wind turbines, Energy 89 (2015) 896–906.

[17] B. Lahfaoui, et al., Real time study of P&O MPPT control for small wind PMSG turbine systems using Arduino microcontroller, Energy Procedia 111 (2017) 1000–1009.

[18] Q. Mei, et al., A novel improved variable step-size incremental-resistance MPPT method for PV systems, IEEE Trans. Ind. Syst. 58 (6) (2010) 2427–2434.

[19] D. Kumar, K. Chatterjee, A review of conventional and advanced MPPT algorithms for wind energy systems, Renew. Sustain. Energy Rev. 55 (2016) 957–970.

[20] S.M.R. Kazmi, et al., A novel algorithm for fast and efficient speed-sensorless maximum power point tracking in wind energy conversion systems, IEEE Trans. Ind. Electron. 58 (1) (2010) 29–36.

[21] L.C. Pagnini, M. Burlando, M.P. Repetto, Experimental power curve of small-size wind turbines in turbulent urban environment, Appl. Energy 154 (2015) 112–121.

[22] K.E. Johnson, et al., Control of variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture, IEEE Control Syst. Mag. 26 (3) (2006) 70–81.

[23] M. Cheng, Y. Zhu, The state of the art of wind energy conversion systems and technologies: a review, Energy Convers. Manag. 88 (2014) 332–347.

[24] X. Zhang, et al., Maximum power point tracking algorithms for wind power generation system: review, comparison and analysis, Energy Sci. Eng. 11 (1) (2023) 430–444.

[25] R. Tiwari, et al., Neural network based maximum power point tracking control with quadratic boost converter for PMSG—wind energy conversion system, Electronics 7 (2) (2018) 20.

[26] S. Labdai, et al., Artificial neural network-based adaptive control for a DFIG-based WECS, ISA Trans. 128 (2022) 171–180.

[27] R. Kumar, et al., Maximum power point tracking in wind energy conversion system using radial basis function based neural network control strategy, Sustain. Energy Technol. Assess. 36 (2019) 100533.

[28] E. Rahmanian, H. Akbari, G.H. Sheisi, Maximum power point tracking in grid connected wind plant by using intelligent controller and switched reluctance generator, IEEE Trans. Sustain. Energy 8 (3) (2017) 1313–1320.

[29] R. Moutchou, et al., Adaptive fuzzy logic controller for MPPT control in PMSG wind turbine generator, Artif. Intell. -Based Smart Power Syst. (2023) 129–140.

[30] A.P. Marugán, et al., A survey of artificial neural network in wind energy systems, Appl. Energy 228 (2018) 1822–1836.

[31] A.B. Asghar, X. Liu, Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine, Neurocomputing 272 (2018) 495–504.

[32] B.-H. Lee, et al., An estimation of the extreme wind speed using the Korea wind map, Renew. Energy 42 (2012) 4–10.

[33] R. Sozzi, F. Rossi, T. Georgiadis, Parameter estimation of surface layer turbulence from wind speed vertical profile, Environ. Model. Softw. 16 (1) (2001) 73–85.
[34] S.M. Diniz, F. Sadek, E. Simiu, Wind speed estimation uncertainties: effects of climatological and micrometeorological parameters, Probab. Eng. Mech. 19 (4) (2004) 361–371.

[35] D. Fu, et al., Wind turbine load optimization control and verification based on wind speed estimator with time series broad learning system method, IET Control Theory Appl. (2024).

[36] D. Jena, S. Rajendran, A review of estimation of effective wind speed based control of wind turbines, Renew. Sustain. Energy Rev. 43 (2015) 1046–1062.

[37] D. Song, et al., New perspectives on maximum wind energy extraction of variable-speed wind turbines using previewed wind speeds, Energy Convers. Manag. 206 (2020) 112496.

[38] H.S. Kang, C. Meneveau, Direct mechanical torque sensor for model wind turbines, Meas. Sci. Technol. 21 (10) (2010) 105206.

[39] H. Li, K. Shi, P. McLaren, Neural-network-based sensorless maximum wind energy capture with compensated power coefficient, IEEE Trans. Ind. Appl. 41 (6) (2005) 1548–1556.

[40] W. Qiao, et al., Wind speed estimation based sensorless output maximization control for a wind turbine driving a DFIG, IEEE Trans. Power Electron. 23 (3) (2008) 1156–1169.

[41] X. Deng, et al., Sensorless effective wind speed estimation method based on unknown input disturbance observer and extreme learning machine, Energy 186 (2019) 115790.

[42] A. Naba, A. Nadhir, Power curve based-fuzzy wind speed estimation in wind energy conversion systems, J. Adv. Comput. Intell. Intell. Inform. 22 (1) (2018) 76–87.

[43] K. Tan, S. Islam, Optimum control strategies in energy conversion of PMSG wind turbine system without mechanical sensors, IEEE Trans. Energy Convers. 19 (2) (2004) 392–399.

[44] X. Yang, et al., Soft sensor based on support vector machine for effective wind speed in large variable wind. Proceedings of the 9th International Conference on Control, Automation, Robotics and Vision, IEEE, 2006.

[45] A.G. Abo-Khalil, D.-C. Lee, MPPT control of wind generation systems based on estimated wind speed using SVR, IEEE Trans. Ind. Electron. 55 (3) (2008) 1489–1490.

[46] D. Karaboga, E. Kaya, Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey, Artif. Intell. Rev. 52 (2019) 2263–2293.

[47] B. Tarek, D. Said, M. Benbouzid, Maximum power point tracking control for photovoltaic system using adaptive neuro-fuzzy “ANFIS”. Proceedings of the Eighth international Conference and Exhibition on Ecological Vehicles and Renewable Energies (EVER), IEEE, 2013.

[48] Salleh, M.N.M., N. Talpur, and K. Hussain. Adaptive neuro-fuzzy inference system: Overview, strengths, limitations, and solutions. In: Proceedings of the Data Mining and Big Data: Second International Conference, DMBD 2017, Fukuoka, Japan, July 27–August 1, 2017, Proceedings 2. 2017. Springer.

[49] E.-J. Pérez-Pérez, et al., Fault diagnosis in wind turbines based on ANFIS and Takagi–Sugeno interval observers. Expert Syst. Appl. 206 (2022) 117698.

[50] H.M.I. Pousinho, V.M.F. Mendes, J.Pd.S. Catalão, A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal, Energy Convers. Manag. 52 (1) (2011) 397–402.

[51] M.A. Al-qaness, et al., Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting, Appl. Energy 314 (2022) 118851.

[52] S. Krim, M.F. Mimouni, Design of improved direct torque control based on a five level torque controller and a new Sugeno-Takagi fuzzy super-twisting controller applied to an induction machine, Eng. Appl. Artif. Intell. 126 (2023) 106900.

[53] M.A. Hajjaji, et al., FPGA implementation of digital images watermarking system based on discrete Haar wavelet transform, Secur. Commun. Netw. 2019 (1) (2019) 1294267.

[54] Z.B.H. Salah, et al., Hardware implementation of maximum power point tracking algorithms for photovoltaic systems: a comparative study. Proceedings of the IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), IEEE, 2022.

[55] M. Hermassi, et al., Xilinx-FPGA for real-time implementation of vector control strategies for a grid-connected variable-speed wind energy conversion system. Proceedings of the 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET), IEEE, 2022.

[56] M.A.S. Ali, et al., Wind-speed estimation and sensorless control for SPMSG-based WECS using LMI-based SMC, IEEE Access 8 (2020) 26524–26535.

[57] W.H. Lio, A. Li, F. Meng, Real-time rotor effective wind speed estimation using Gaussian process regression and Kalman filtering, Renew. Energy 169 (2021) 670–686.

[58] P. Chen, D. Han, Effective wind speed estimation study of the wind turbine based on deep learning, Energy 247 (2022) 123491.

[59] A. Jemaa, et al., Hardware Implementation of a fuzzy logic controller for a hybrid wind-solar system in an isolated site, Int. J. Photoenergy 2018 (1) (2018) 5379864.

[60] S. Krim, et al., FPGA-based real-time implementation of a direct torque control with second-order sliding mode control and input-output feedback linearisation for an induction motor drive, IET Electr. Power Appl. 14 (3) (2020) 480–491.

[61] S. Krim, et al., Design and implementation of direct torque control based on an intelligent technique of induction motor on FPGA, J. Electr. Eng. Technol. 10 (4) (2015) 1527–1539.

[62] S. Krim, et al., Control with high performances based DTC strategy: FPGA implementation and experimental validation, EPE J. 29 (2) (2019) 82–98.

[63] M. Bouderbala, et al., Experimental validation of predictive current control for DFIG: FPGA implementation, Electronics 10 (21) (2021) 2670.

[64] M. Hermassi, et al., Adaptive neuro fuzzy technology to enhance PID performances within VCA for grid-connected wind system under nonlinear behaviors: FPGA hardware implementation, Comput. Electr. Eng. 117 (2024) 109264.

[65] B. Majout, et al., Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: an experimental validation, Energy Rep. 11 (2024) 4356–4378.

[66] A.A. Salem, et al., Implementation and validation of an adaptive fuzzy logic controller for MPPT of PMSG-based wind turbines, IEEE Access 9 (2021) 165690–165707.

[67] V. Nikolić, et al., Extreme learning machine approach for sensorless wind speed estimation, Mechatronics 34 (2016) 78–83.

[68] X. Deng, et al., An effective wind speed estimation based extended optimal torque control for maximum wind energy capture, IEEE Access 8 (2020) 65959–65969.

[69] O. Boukhrachef, M. Hadel, A. Djerdir, Maximum power point tracking of a wind turbine based on artificial neural networks and fuzzy logic controllers. Proceedings of the International Conference on Artificial Intelligence and its Applications, Springer, 2021.

[70] H. Chojaa, et al., Integral sliding mode control for DFIG based WECS with MPPT based on artificial neural network under a real wind profile, Energy Rep. 7 (2021) 4809–4824.

[71] M. Nadour, A. Essadki, T. Nasser, Comparative analysis between PI & backstepping control strategies of DFIG driven by wind turbine, Int. J. Renew. Energy Res. 7 (3) (2017) 1307–1316.

[72] B. Meghni, H. Chojaa, A. Boulmaiz, An optimal torque control based on intelligent tracking range (MPPT-OTC-ANN) for permanent magnet direct drive WECS. Proceedings of the IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), IEEE, 2020.

[73] H. Chojaa, et al., Comparative study of MPPT controllers for a wind energy conversion system. Proceedings of the International Conference on Advanced Technologies for Humanity, Springer, 2021.