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# A Progressive Approach of Designing and Analysis of Solar and Wind Stations Integrated with the Grid Connected Systems

N. Balavenkata Muni, S. Sasikumar, K. Hussain, K. Meenendranath Reddy

► To cite this version:

N. Balavenkata Muni, S. Sasikumar, K. Hussain, K. Meenendranath Reddy. A Progressive Approach of Designing and Analysis of Solar and Wind Stations Integrated with the Grid Connected Systems. 5th International Conference on Computational Intelligence in Data Science (ICCIDS), Mar 2022, Virtual, India. pp.83-96, 10.1007/978-3-031-16364-7_7. http-04381274

HAL Id: hal-04381274

https://inria.hal.science/hal-04381274v1

Submitted on 9 Jan 2024

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# A progressive approach of designing and analysis of Solar and wind stations integrated with the grid connected systems

¹N.Balavenkata Muni, ²Dr. S.Sasikumar, ³Dr.K. Hussain, ⁴K.Meenendranath Reddy

¹,²Department of Electrical Engineering, Annamalai University, Annamalainagar, Tamil Nadu.

³Department of Electrical Engineering, SITCOE, Ichalkaranji, Kolhapur, Maharashtra.

⁴SVR Engineering college, Nandyal, AP.

**Abstract** — This article illustrates the advanced control strategy and analysis of hybrid resources integrated to grid system. The hybrid resources consist of solar station and wind station, which are integrated to the grid through AC bus system. The Perturb and Observe algorithm (P&O) is used in Maximum Power Point Tracking (MPPT) to increase the power output of PV station and Wind station to maximum. The modeling and analysis of the system is improved using fuzzy based optimization is done in MATLAB/SIMULINK platform. The effectiveness of the system is improved using fuzzy algorithm during different load conditions. The improvements of grid voltage, grid power and grid currents can be observed during different load conditions from conventional PI controller to fuzzy logic controller. The advanced controller efficiently maintains the grid parameters stably in the hybrid systems.

**Keywords:** Solar station, Wind station, Fuel cell systems, fuzzy controller and MPPT controller

## 1. Introduction

The renewable resources have significantly penetrated in the power system. The demand on the power system is increasing 20% every year therefore it is very important that the researchers shall be matured in the way of the renewable energy resources for the growth of electrical requirements. In the last few decades the significance of these resources are increasing continuously along with the existing system owing to its effective and efficient performance, when it is associated with the distributed generators. The choosing of DGs are important when it is integrating with existing AC grids.

Several research are in progress along with the energy systems like batteries, charging capacitors, super capacitors, magnetic energy storage systems, fuel cell electrolyzes etc. Recently, the doubly fed induction generators along with fuel cell electrolyze have produced and proved several advantages. Several control strategies have developed to control the output requirements. MPPT techniques with algorithms are incorporated to increase the power output and to maintain the voltage stability values with respect to the system specified values.

This paper has focused on the analysis of hybrid energy resources during the integration of AC grid systems. An advanced fuzzy controller has proposed the power management algorithm with different load condition.

In this article section 2 represents the system description, section 3 describes the design of controller for PV station, wind station and energy storage device, section 4 describes bidirectional topologies for battery, section 5 describes fuzzy process and section 6 presents the analysis of the system with the proposed controller.

## 2. System Description

The proposed model consists of the design of PV, Wind stations and batteries are connected to 11kW AC grid network, shown in Fig.1. The three 2.4kW small size solar stations are interconnected and connected to common coupling bus system. A doubly fed induction generator with constant speed is acted as wind station. The power output from PV panels and wind turbines are supervised by MPPT circuit with perturb & Observe algorithm.

In this paper an optimization based controller is chosen to amplify the power output to the load so that stability of the system is maintained. Both linear and nonlinear loads are tested for steady state operations.
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<figure><img src="image_3.png" /><figcaption>Fig.1. System Model</figcaption></figure>

## 3. Design and control of PV, wind and battery systems

In this section, we illustrate the design and control of the PV station, wind station and battery storage device for integrating to common bus system at AC loads are connected.

### 3.1 Model and operation of wind controller

A fixed speed DFIG is taken for wind station. There are two control modes based on wind speed and power output, i.e. pitch control mode and power limitation mode

#### 3.1.1 Pitch control mode

In this case control of wind speed comes into presence. If wind speed exceeds the rated value, it must bring to rated value to maintain the extracted power to the rated value. The power constant $K_p$ and pitch angle $\alpha$ should be varies to maintain sufficient electromagnetic torque of wind turbine. The related expressions are shown below.

$$ \lambda_{wind} = N_{turbine} * \frac{R_r}{v_{wind}} \quad (1) $$

$$ K_p = p_{wind} * \frac{R_r}{0.5*A_b*v_{wind}^3} \quad (2) $$

where $N_{turbine}$, $\lambda_{wind}$, $p_{wind}$ and $K_p$ are the rotational value, rotational value ratio, blow power and power gain respectively. The above all parameters are corresponding to rated value of wind speed. $R_r$ and $A_b$ are the radius of rotor and area blades respectively. By solving the above equation the pitch angle $\alpha$ can be obtained.

#### 3.1.2 Power limitation mode

In this mode, the turbine speed is maintained to rated value. The power demand ad speed ratio is calculated as follows.

$$ \lambda_{lim} = N_{t norm} * \frac{R_r}{v_{wind}} \quad (3) $$

$$ K_{p lim} = P_{m-rate} * \frac{R_r}{0.5*A_b*v_{wind}^3} \quad (4) $$

$$ \text{Pitch angle } K_p(\lambda_{lim}, \alpha) = K_{p lim} \quad (5) $$

## 3.2 Model and Operation of PV controller

### 3.2.1 PV modeling

From Fig.2, the generated current (I) by the PV cell is given by

<figure><img src="image_4.png" /><figcaption>Fig.2 Internal circuit of PV station</figcaption></figure>

$$ I = I_{sc} - I_d - I_{sh} \quad (8) $$

$$ I = I_{sc} - \frac{V+IR_s}{R_{sh}} - I_o (e^{\frac{v+IR_s}{nV_T}} - 1) \quad (9) $$
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<figure><img src="image_5.png" /><figcaption>Fig.3 PV Model</figcaption></figure>

The preferring maximum power from the PV panels with controller of procuring MPPT [8] is described by the known techniques. The rated voltage as Open circuit voltage and the rated current as short circuit currents are considered as backend parameters to measure the rated specifications of PV panels at different environmental conditions with MPPT techniques. There is another front end technology to measure the accurate panel voltage and currents with faster rate than the backend process. This advanced technology is known as Perturb and Observe algorithm (P and O).

### 3.2.2 P and O with MPPT techniques

Particularly, this method is used to procure maximum power from the panels using MPPT procedures[9]. The output of PV panels are regularly measured and compared with previous values. The voltage increment or decrement is compared with the increase or decrease of powers procured from the panels if the increased voltage is proportional to increased power then the location of operating point is on left side. If the increased voltage is inversely proportional to the power then the operating point is located in right side. The perturbation is to be chosen such that the operating point shall be on left side. In this method, MPPT is used to measure the voltages of PV panels and battery. If PV voltage is reached to maintain which is more than battery voltage then the battery is charged to maximum so that the maximum power from PV panels are obtained. Otherwise the voltage is discharged from battery to maintain the PV power so that the maximum power is maintained at constant rate at any load conditions. In this paper, PWM cycle is used to trigger the charge controllers to extract the maximum power from PV panels. This MPPT technique with P and O algorithm is efficient easy technology to acquire the maximum power from PV and Wind systems.

## 3.3 Model and operation of bidirectional controller for battery

Lead acid batteries are the most used type of energy storage systems due to their reduced cost and high durability [10]. Due to their popularity, a large amount of research was performed to identify their properties and behavior for simulation purposes. Identifying the battery behavior is very important for the design of the converter. A large number of battery models were developed that include circuit based models such as [11]. It is important to identify a model for the battery that estimates the state of charge accurately to be able to assess the performance of the power converter operation. The battery model must be compatible with the charging and discharging profiles of typical lead acid batteries. The state of charge must be determined accurately for the purposes of converter control. A battery model is as found in [12] and can be summarized as follows.

The model diagram for the energy storage system is given in Fig.4, which is connected for PV panels in the given grid connected power systems. Fig.5 and Fig.6 are represented the respective characteristics during discharging and charging of energy storage system in the fuzzy based environment.
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<figure><img src="image_6.png" /><figcaption>Fig.4. Bidirectional battery controller</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig.5. Battery discharge characteristics during fuzzy environment</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 6.Battery charging characteristics during fuzzy environment</figcaption></figure>

The integrating loop provides an estimation of the actual charge of the battery. The voltage is then calculated and supplied to the controlled voltage source that converts the signal into a power signal. Even though this model has several simplifications, it still can provide an accurate response.

In order to verify the model, a test simulation will be performed and the charging and discharging characteristics will be examined. A short circuit test will demonstrate the discharge characteristics and an open circuit test will demonstrate the charging characteristics of the model. The model described is important for the simulation and gives an idea about the characteristics of the battery system.
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In the Fig.7, the performance of fuzzy logic controller in PV station is observed at different values of inputs and observed the different outputs. The duty cycle variation is observed in the Table 1 for different current and voltages with an irradiance of 1000 w/m²

<figure><img src="image_9.png" /><figcaption>Fig.7. Simulink block for Fuzzy logic controller</figcaption></figure>

## 4. Bi-directional Topologies

This section describes the various DC-DC converter topologies. They are (a) seclude type converter (ii) non-seclude type converter. The seclude type converter comprises of two Bi-directional type bridge converters which are separated by isolated transformers. The skeleton model converter type is given in [9].

<table><caption>Table: 1 Duty cycle for input current and voltage</caption><thead><tr><td>Current(A)</td><td>Voltage(V)</td><td>Duty Cycle(D)</td></tr></thead><tbody><tr><td>3</td><td>15</td><td>0.9169</td></tr><tr><td>5</td><td>18</td><td>0.7293</td></tr><tr><td>10</td><td>25</td><td>0.6402</td></tr><tr><td>15</td><td>17</td><td>0.6429</td></tr></tbody></table>

The proposed converter type provides high efficacy with the coordination of transformer which acts as a protection cover for the grid system and energy storage system. The only exception in this is providing large transformer in the system to produce large efficiency to the converter system.

Such converters should not be used unless the voltage gain could not be achieved through non-isolated converters. The other type is the non-isolated converter which also has many topologies. The simplest design is the synchronous buck converter which is still used with electric vehicles as mentioned in [12].

In this design, the ideal voltage-boost -gain is calculated as follows:

$$ \frac{V_o}{V_i} = \frac{1}{D} $$

The simulation block is shown in Fig.8, is implemented in SIMULINK. The performance of fuzzy based PV controller is tested with different irradiance such as 1000w/m² and 800w/m². The variation of voltage, current and power is obtained in normal PI-MPPT and Fuzzy-MPPT environments, as shown in Table 2.

<figure><img src="image_10.png" /><figcaption>Fig.8. Simulink block for fuzzy MPPT controller</figcaption></figure>
<table><caption>Table 2: PV outputs</caption><thead><tr><th rowspan="2">Irradiance (w/m²)</th><th colspan="3">PI MPPT</th><th colspan="3">Fuzzy MPPT</th></tr><tr><th>Voltage (V)</th><th>Current (A)</th><th>Power (W)</th><th>Voltage (V)</th><th>Current (A)</th><th>Power (W)</th></tr></thead><tbody><tr><td>800.0</td><td>528.3</td><td>3.9</td><td>2055.0</td><td>585.7</td><td>4.2</td><td>2465.9</td></tr><tr><td>1000.0</td><td>660.4</td><td>4.2</td><td>2783.0</td><td>658.3</td><td>4.5</td><td>2975.5</td></tr></tbody></table>

## 5. Fuzzy Logic Controller operational process

The process of fuzzy rules is incorporated into what is called a Fuzzy Interfacing System. It is classified into three cases, viz., Fuzzification, fuzzy process and Defuzzification which exercise the system inputs for the significant system outputs.

The linguistic variables denoted as NL,NM,NS,Z,PS,PM and PL stand for negative large, negative medium, negative small, zero, positive small, positive medium and positive large respectively. For both of the inputs, the equation of the triangular member function (MF) employed to determine the grade of membership values (μAᵢ(x)) is given by

$$ \mu A_i(\text{TED}) = \frac{1}{b} (b - 2 |\text{TED} - a|) \qquad (10) $$

where ‘a’ represents the coordinate of the point at the grade of memberships (MFs) and ‘b’ is the width of MFs. μAᵢ(TED) is the grade value of membership. Fig.9 and Fig.10 represent the fuzzy surface view and fuzzy membership triangles for the fuzzy rules respectively.

<figure><img src="image_11.png" /><figcaption>Fig.9. Fuzzy surface view for fuzzy rules</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig.10. Fuzzy membership plot</figcaption></figure>

## 6. Simulation analysis and results

The simulation results of the test system, shown in Fig.11 are presented with fuzzy logic approach. Table 1 detail the duty cycle for the input voltage and current of the energy storage device. Table.2 describes the variation of outputs PV station for different irradiance in the presence of conventional MPPT and fuzzy MPPT controllers.
<figure><img src="image_13.png" /><figcaption>Fig.11 Simulink model of overall system</figcaption></figure>

In case of PI controller used in hybrid system, the power output of wind station is unstable and is oscillated 4.95kw to 5kw, as represented in Fig.12.

The wind station voltage is represented in Fig.13. The characteristic of PWM for duty cycle is shown in Fig.14. The response of PV station for conventional controller is presented in Fig.15. The PV voltages and currents are slightly distorted initially and stabilized after 0.15sec, represented in Fig.16.

<figure><img src="image_14.png" /><figcaption>Fig.12. Power output of wind station in case of PI controller</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig.13. Wind station voltage in case of PI controller</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Fig. 14. PWM vs Duty cycle in case of fuzzy based PV controller</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 15. PV MPPT Voltage and Current</figcaption></figure>

When fuzzy MPPT controller with P&O algorithm is used in PV station, the distortion in load voltage and current becomes less at any load condition. The voltage and current are stabilized to 400V and 19A respectively, represented in Fig.17.

<figure><img src="image_18.png" /><figcaption>Fig. 16. Load Power (P) with Fuzzy logic controller in PV station</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>Fig. 17. Load Power (P) with Fuzzy logic controller in PV station</figcaption></figure>
Table 3 is the detailed comparison of PV voltage, current and Power during conventional MPPT and proposed MPPT approach. Table 4 gives the stable load parameters at any point of load.

<table><caption>Table 3: The variation of PV voltage, current and PV power in PI MPPT and Fuzzy MPPT</caption><thead><tr><th rowspan="2">Time (sec)</th><th colspan="3">PI MPPT</th><th colspan="3">Fuzzy MPPT</th></tr><tr><th>Voltage (V)</th><th>Current (A)</th><th>Power (W)</th><th>Voltage (V)</th><th>Current (A)</th><th>Power (W)</th></tr></thead><tbody><tr><td>0.25</td><td>427.7</td><td>3.0</td><td>1281.3</td><td>503.2</td><td>3.2</td><td>1586.8</td></tr><tr><td>0.5</td><td>528.5</td><td>3.7</td><td>1956.3</td><td>621.8</td><td>3.9</td><td>2422.7</td></tr><tr><td>0.75</td><td>552.4</td><td>3.9</td><td>2135.3</td><td>649.9</td><td>4.1</td><td>2644.4</td></tr><tr><td>1</td><td>558.0</td><td>3.9</td><td>2179.1</td><td>656.5</td><td>4.1</td><td>2698.6</td></tr><tr><td>2</td><td>560.0</td><td>3.9</td><td>2194.5</td><td>658.8</td><td>4.1</td><td>2717.7</td></tr><tr><td>3</td><td>559.5</td><td>3.9</td><td>2190.9</td><td>658.3</td><td>4.1</td><td>2713.2</td></tr><tr><td>4</td><td>559.6</td><td>3.9</td><td>2191.2</td><td>658.3</td><td>4.1</td><td>2713.6</td></tr><tr><td>5</td><td>559.9</td><td>3.9</td><td>2193.8</td><td>658.7</td><td>4.1</td><td>2716.7</td></tr></tbody></table>

<table><caption>Table 4: The effect of Irradiance on Load values</caption><thead><tr><th rowspan="3">Irradiance (w/m²)</th><th colspan="6">Load Parameters</th></tr><tr><th colspan="3">PI MPPT</th><th colspan="3">Fuzzy MPPT</th></tr><tr><th>Voltage (V)</th><th>Current (A)</th><th>Power (W)</th><th>Voltage (V)</th><th>Current (A)</th><th>Power (W)</th></tr></thead><tbody><tr><td>800.0</td><td>18.5</td><td>379</td><td>7011.5</td><td>18.5</td><td>385</td><td>7122.5</td></tr><tr><td>1000.0</td><td>18.52</td><td>388.4</td><td>7193.2</td><td>19.5</td><td>395</td><td>7702.5</td></tr></tbody></table>

## 7. Summary

The proposed fuzzy approach for the analysis of hybrid sources connected to the grid network. The proposed progressive approach is incorporated in PV station and inverter control and the outputs are compared with conventional mppt controller. The output voltage and active power of the loads are enhanced 10 to 15% in the presence of proposed approach, which are tabulated. The load distortion effect is considerably reduced and stable output parameters are obtained during the different loads.

The comparative results obtained with the progressive approach has proved that the fuzzy approach of improves the voltage stability with no distortion at any load. Further, the proposed controller has enhanced the transient stability such that the stable grid parameters are maintained.

## Reference

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