bluuebunny/crossref_metadata_2025 huggingface dataset:
A broadband directional coupler based on ferrite cores
Authors: Liu Lei , Song Nan , Wang Zheng‐Bin
Published: February 2021

A broadband directional coupler based on ferrite cores is proposed for high frequency (HF)/Very high frequency (VHF)/Ultra High Frequency (UHF) communication systems. The transformer‐type coupler is composed of two double‐aperture ferrite cores and 1‐mm‐diameter strands. The ferrite cores with different relative permeabilities are screened to achieve an ultra‐wide bandwidth (5 –1000 MHz). The coupler demonstrates excellent electric performances. The voltage standing wave ratio of each port is less than 1.2, a stable −20 dB coupling level is maintained over the operating bandwidth and the insertion loss is less than 0.8 dB. Such couplers have compact sizes and are of very low cost.
Encoding discriminative representation for point cloud semantic segmentation
Authors: Zhao Yuehua , Ma Jie , Wang Qian , Ye Mao , Wu Lin
Published: February 2021

The main challenge for semantic segmentation based on deep learning is how to encode effective representation due to the nature of point cloud. This letter proposes a plug‐and‐play module to abstract information from local, multi‐scale context, and interdependency to make up for the deficiency. Given an intermediate feature, our module first performs a multi‐scale context layer to adaptive density variance and gets rich contextual information. Then, our module infers attention masks along two dimensions, channel and pointwise in parallel. The attention masks are performed on the input features for further cultivation. Through this module, we can refine features from different perspectives. Experiments conducted on the Stanford 3D Indoor Semantics dataset demonstrate the efficacy of the proposed method. The results show that our method can achieve state‐of‐the‐art performance.
Full‐range wavelength switching at tunable distributed amplification‐DFB laser with high‐accuracy feedforward control
Authors: Niiya Yuri , Kuboki Takeshi , Kato Kazutoshi
Published: February 2021

To realise an optical packet switching system, a tunable laser that can achieve high‐speed and wide‐range tuning is required. One candidate, we have studied for this role, is the tunable distributed amplification (TDA)‐distributed feedback (DFB) laser. In this study, with an aim to realise high‐speed switching at the full C‐band tuning range of a TDA‐DFB laser array, we investigated 800‐GHz‐wide switching, which is the maximum tuning range of a single TDA‐DFB laser. By improving the accuracy of feedforward control, we achieved a high‐speed wavelength switching time of 30 ns.
A deep‐learning‐ and reinforcement‐learning‐based system for encrypted network malicious traffic detection
Authors: Yang Jin , Liang Gang , Li Beibei , Wen Guozhu , Gao Tianyu
Published: February 2021

Traditional network intrusion detection methods lack the ability of automatic feature extraction for encrypted network malicious traffic, and thus, the detection rates are low. Moreover, the means of this malicious traffic are concealed, and the key malicious features are usually hidden in many normal data packets, so fewer encrypted malicious traffic samples can be captured. This easily leads to insufficient system training, low detection rate, and high false alarm rate. This letter proposes an encrypted network malicious traffic detection model based on deep learning, in which automatic feature extraction is performed against encrypted network malicious traffic. The proposed model has self‐learning and self‐adaption abilities. Furthermore, a sample generation method of encrypted traffic based on deep Q‐networks and deep convolution generative adversarial networks is proposed, in which new samples are learned from the training samples of encrypted traffic and solves problems, such as insufficient original training samples and unbalanced samples. In a validation experiment, the proposed model could distinguish between normal and abnormal encrypted network traffic, and the accuracy rate reached 99.94%. Experimental results show that the proposed model in this letter can provide a new and better solution for an encrypted network malicious traffic detection system.
Developing a new framework for transactive peer‐to‐peer thermal energy market
Authors: Davoudi Mehdi , Moeini‐Aghtaie Moein , Ghorani Rahim
Published: February 2021

The rapid deployment of district heating systems in local energy markets and increasing the number of small‐scale heat producers along with the expansion of local electricity markets increase the need for a transaction framework to manage the transactions between local participants in both heat and electricity markets. This paper presents a peer‐to‐peer thermal energy transaction framework to manage the transactions between small‐scale heat prosumers. This framework enables small‐scale thermal energy producers and consumers to participate in the market as price maker agents. Moreover, the optimal strategy of heat market participants is determined by proposing a linear profit function for each agent. This optimization problem enables the agents to determine their optimal participation strategy in electricity and heat markets by addressing the interdependencies of electricity market, gas price, and heat market. The numerical results successfully demonstrate the benefits and applicability of the proposed framework.
Comparison of parametric and non‐parametric Bayesian inference for fusing sensory estimates in physiological time‐series analysis
Authors: Zhu Tingting , Javed Hamza , Clifton David A.
Published: February 2021

The rapid proliferation of wearable devices for medical applications has necessitated the need for automated algorithms to provide labelling of physiological time‐series data to identify abnormal morphology. However, such algorithms are less reliable than gold‐standard human expert labels (where the latter are typically difficult and expensive to obtain), due to their large inter‐ and intra‐subject variabilities. Actions taken in response to these algorithms can therefore result in sub‐optimal patient care. In a typical scenario where only unevenly sampled continuous or numeric estimates are provided, without access to the “ground truth”, it is challenging to choose which algorithms to trust and which to ignore, or even how to merge the outputs from multiple algorithms to form a more precise final estimate for individual patients. In this work, the novel application of two previously proposed parametric fully‐Bayesian graphical models is demonstrated for fusing labels from (i) independent and (ii) potentially correlated algorithms, validated on two publicly available datasets for the task of respiratory rate (RR) estimation. These unsupervised models aggregate RR labels and estimate jointly the assumed bias and precision of each algorithm. Fusing estimates in this way may then be used to infer the underlying ground truth for individual patients. It is shown that modelling the latent correlations between algorithms improves the RR estimates, when compared to commonly employed strategies in the literature. Finally, it is demonstrated that the adoption of a strongly Bayesian approach to inference using Gibbs sampling results in improved estimation over the current state‐of‐the‐art (e.g. hierarchical Gaussian processes) in physiological time‐series modelling.
Monica Healthcare: From the research laboratory to commercial reality—A real‐life case study
Authors: Hayes‐Gill Barrie R
Published: February 2021

The desire of many engineers is to see their work end up as a final product offering a real benefit to society—for a lecturer/professor at a university, this is a dream often out of reach of the majority. However, the university academic is a changed species from the early days of the binary line between Universities and Polytechnics and when a lecturer meant just that—teaching to future engineers. This article describes the process and experience gained by a university engineer to spin out their research from the university sector and achieve the goal of a product reaching a global audience.
Adaptive output‐feedback quadratic tracking control of continuous‐time systems via value iteration with its application
Authors: Peng Yunjian , Meng Qingqing , Sun Weijie
Published: February 2021

This study is dedicated to develop an adaptive output‐feedback (OPFB) tracking control scheme for continuous‐time linear systems to achieve the infinite‐horizon linear quadratic tracking (LQT) solution. The existence conditions of the OPFB control for LQT solution are proposed and an upper bound is found for the discount factor to assure the stability of the OPFB solution. To develop an online learning solution without knowing the system drift dynamics, a novel value iteration (VI) algorithm is presented based on the integral reinforcement learning technique which requires measured augmented system states. Moreover, a convergence analysis is proposed for the VI algorithm. Compared to the policy iteration method, the VI algorithm relaxes the initial stabilising control policy requirement. Specifically, in order to further obviate the knowledge of system states, a neural network‐based adaptive observer is used during learning and it is no longer needed after the online learning algorithm converges. Effectiveness of the proposed learning scheme is illustrated through an interesting application into a single‐phase grid‐connected PV power inverter.
Adaptive non‐linear integral sliding mode fault‐tolerant control allocation scheme for octorotor UAV system
Authors: Ijaz Salman , Fuyang Chen , Hamayun Mirza Tariq
Published: February 2021

This study proposes a new active fault‐tolerant control (FTC) and fault estimation scheme for a non‐linear octorotor system. The proposed method utilises the idea of an online control allocation (CA) scheme to fully engage the rotors redundancy based on the information from the fault estimation unit. The nominal performance is first achieved using non‐linear dynamic inversion (NDI) technique and then to incorporate the robustness, an adaptive non‐linear sliding mode control is united with a baseline NDI controller. The proposed method is used to attain the desired altitude and attitude tracking control of an octorotor system. Furthermore, to control the un‐actuated states (called internal dynamics) of octorotor system, a separate integral sliding mode‐based NDI controller is designed that provides the translational axes control by generating the desired roll and pitch commands. Simulations on the non‐linear model of octorotor system validate the dominant performance of the proposed scheme compared to the existing methods in the literature.
Experimental validation of the simultaneous damping and tracking controller design strategy for high‐bandwidth nanopositioning – a PAVPF approach
Authors: Babarinde Adedayo K. , Li Linlin , Zhu LiMin , Aphale Sumeet S.
Published: February 2021

For scanning applications, damping and tracking controllers are employed in a dual‐loop fashion. Whilst these damping and tracking controllers are designed sequentially in literature (damping first, tracking later), it has been found that the tracking controller (typically integral or proportional–integral) influences the ‘desired’ pole locations (and thereby its damping performance) achieved by the positive acceleration, velocity and position feedback (PAVPF) damping controller. This work starts by first highlighting this unwanted effect that results in low positioning bandwidth. To address this drawback, this work presents the design, analysis and experimental validation of the simultaneous design method for the PAVPF control‐based combined damping and tracking scheme, aimed at achieving accurate, high‐bandwidth nanopositioning. It also details a recursive analytical method to simultaneously optimise the damping and tracking controller parameters resulting in almost a three‐fold increase in closed‐loop bandwidth when compared with the traditional sequential method. To further confirm the advantages of the proposed simultaneous design method, comparative experimental results conducted on one axis of a piezo‐actuated nanopositioner are presented. Significant improvements in the steady‐state positioning as well as transient response are noted. These improvements combined, result in significant gains in the raster scanning performance of nanopositioning stages.
Fast visual saliency based on multi‐scale difference of Gaussians fusion in frequency domain
Authors: Li Weipeng , Yang Xiaogang , Li Chuanxiang , Lu Ruitao , Xie Xueli
Published: February 2021

To reduce the computation required in determining the proper scale of salient object, a fast visual saliency based on multi‐scale difference of Gaussians fusion in frequency domain (MDF) is proposed. First, based on the phenomenon that the foreground energy is highlighted and densely distributes on certain band of spectrum, the scale coefficients of foreground in an image can be literately approximated on the amplitude spectrum. Next, relying on the linear integration property of Fourier transform, the feature spectrum is obtained through the weighted infinite integral of difference of Gaussian feature maps with respect to the scale of object. Then, the saliency of each channel is obtained from feature spectrum by the inverse Fourier transform and scale filtering. Finally, through the channel integration, the MDF saliency map is obtained. Experiments on Li‐Jian data set demonstrate that combined with most appropriate colour space and scale filter, MDF achieves obvious acceleration (5.4 times faster than frequency domain analysis and spatial information) while getting desired accuracy (area under the curve, 0.8814 at Li‐Jian data set), which achieves the best accuracy efficiency trade‐off.
Powered‐two‐wheelers and smart cities: the case of variable message signs
Authors: Spyropoulou Ioanna , Impersimi Eriola
Published: February 2021

At present, a number of smart urban mobility solutions exist and furthermore are emerging towards the design of smart cities offering equitable, green and efficient transport. As this concept addresses the needs of all travellers, it is essential to explore the relevance of existing and proposed smart solutions for distinct user categories. Powered‐two‐wheelers (PTWs) comprise a distinct vehicle category exhibiting specific movement dynamics and characteristics while exhibiting considerable presence in urban areas and increasing ownership trends during the recent years. Thus, PTW needs should be identified and considered in the design of smart cities. This study explores PTW diversion behaviour considering variable message signs (VMS) operation. Relevant data was collected through a stated preference questionnaire survey and diversion propensity was explored through the design of probit models. Survey results indicated that although PTWs believe that VMS fail to address their needs, their attitudes towards them are still positive. Model results exhibited that most of the information elements transmitted via VMS affect PTW diversion behaviour. Other contributory factors, considering PTW diversion included traffic code violation behaviour, rider ‘flexibility’, age, gender etc. Rider sub‐populations, considering riding on the pavement or pedestrianised areas and internet use for traffic information, were also explored.
Predicting driver behaviour at intersections based on driver gaze and traffic light recognition
Authors: Rahman Md. Junaedur , Beauchemin Steven S. , Bauer Michael A.
Published: February 2021

This work introduces and evaluates a model for predicting driver behaviour, namely turns or proceeding straight, at traffic light intersections from driver three‐dimensional gaze data and traffic light recognition. Based on vehicular data, this work relates the traffic light position, the driver's gaze, head movement, and distance from the centre of the traffic light to build a model of driver behaviour. The model can be used to predict the expected driver manoeuvre 3 to 4 s prior to arrival at the intersection. As part of this study, a framework for driving scene understanding based on driver gaze is presented. The outcomes of this study indicate that this deep learning framework for measuring, accumulating and validating different driving actions may be useful in developing models for predicting driver intent before intersections and perhaps in other key‐driving situations. Such models are an essential part of advanced driving assistance systems that help drivers in the execution of manoeuvres.
Modelling and control coordination scheme of a wind‐to‐hydrogen set for future renewable‐based power systems
Authors: Kim Jinho , Muljadi Eduard , Nelms Robert M.
Published: February 2021

New challenges regarding system stability and efficiency arise when power systems operate with a high penetration level of inverter‐based renewable sources (IBRSs) and few synchronous generators. Since IBRSs have been on the rise, to secure the stable operation of future power systems, IBRSs will be required to support systems without having to rely on remaining synchronous generators. Also, to efficiently manage the uncertainty of renewable production, power‐to‐gas technology can provide the required flexibility. This study proposes modelling and a control coordination scheme (CCS) of a wind‐to‐hydrogen (W2H) set to optimise electricity production from a variable‐speed wind turbine generator (WTG) while helping balance between supply and demand in a system. To achieve this, a grid‐forming (GFM) inverter‐based WTG is modelled and a set of electrolyser and fuel cell is integrated at the DC circuit of a GFM‐WTG to be coordinated. Furthermore, the CCS offers an opportunity to reduce the investment cost for deploying a W2H set by utilising the control capabilities of a WTG and reducing the need for an additional device. The performance of the proposed W2H set with the CCS was verified considering the variations in system load and wind speed by using Power System Computer Aided Design (PSCAD)/ElectroMagnetic Transients including Direct Current (EMTDC).
Efficient recurrent attention network for remote sensing scene classification
Authors: Liang Le , Wang Guoli
Published: February 2021

Scene classification for remote sensing is a popular topic, and many recent convolutional neural networks (CNNs)‐based methods have shown the great model capacity and learning ability of highly discriminative features. Given a large number of training data, CNN can extract extensive features and learn to predict a remote sensing image. However, for supervised learning tasks, deep models often rely on a large number of labelled remote sensing images, which are difficult to pre‐process. Thus, training a lightweight deep learning model is essential. Easy‐classified and hard samples may also cause an imbalance of training set and lead the model to overwhelm the loss function. Accordingly, a novel Efficient Recurrent Attention Network (ERANet) for remote sensing scene classification is proposed. Different from traditional deep learning methods, Efficientnet‐B0 is introduced as a lightweight backbone for the ARCNet framework, replacing the original one. By applying the modified efficient backbone, the low Floating Point Operations (FLOPs) and parameter numbers of the proposed ERANet are maintained. The significance of focal loss is determined and applied to address the sample imbalance problem and yield a desirable performance. Extensive experiments on several challenging remote sensing scene classification data sets prove the efficiency of the proposed ERANet.
Switching regulator based on switched‐inductor SEPIC DC‐DC converter with a continuous input current for lithium‐ion batteries
Authors: Villanueva‐Loredo Juan Antonio , Ortiz‐Lopez Ma Guadalupe , Leyva‐Ramos Jesus , Diaz‐Saldierna Luis Humberto
Published: February 2021

This study discusses a control strategy applied to the switched‐inductor single‐ended primary‐inductance converter (SEPIC) converter suitable for applications where the voltage delivered by a DC source varies from above or below the nominal value. The SEPIC converter can provide a non‐isolated positive output voltage for the above applications; moreover, it exhibits better characteristics when combined with a switched‐inductor cell. A design‐oriented procedure is given that appropriately selects the elements of the converter for a given output voltage and power delivery requirements. A control scheme with two loops is chosen. A high‐gain compensator is used in the inner loop such that the average inductor current follows a current reference. A proportional integral (PI) controller is used in the outer loop for output voltage regulation. The controller was designed using loop‐shaping techniques and applied to regulate an output voltage of 21 V. The design was verified with experimental results in a switching regulator where step changes were applied to the load. The performance of the switching regulator was also tested for variations of the input voltage.
Evaluation of silicon MOSFETs and GaN HEMTs in soft‐switched and hard‐switched DC‐DC boost converters for domestic PV applications
Authors: Ansari Sajad A. , Davidson Jonathan N. , Foster Martin P.
Published: February 2021

Hard‐switched high‐gain DC‐DC converters such as the boost converter play an important role in renewable energy systems. Research to increase their efficiency is important and can be achieved using soft‐switching techniques; however, that approach requires an auxiliary circuit. The auxiliary circuit decreases power density and reliability while increasing the cost. Moreover, soft‐switching topologies usually cannot improve the efficiency for all power and voltage ranges. Wide bandgap (WBG) devices, such as gallium nitride (GaN), result in lower switching losses than silicon (Si), can be used while retaining the simple structure of a hard‐switched topology. However, the high cost of these devices is problematic for their frequently cost‐sensitive applications. To quantify the cost and efficiency, this study compares soft‐switching techniques and WBG‐based switches in DC‐DC boost converters for a photovoltaic (PV) energy application. The performance of four prototypes including the soft‐switched and hard‐switched DC‐DC converters with both state‐of‐the‐art Si and GaN switches are evaluated in terms of cost, power density, efficiency, and reliability using theoretical analysis, simulation and experimental results. It is shown that the GaN‐based hard‐switched converter provides higher efficiency and power density; it is more expensive than its Si‐based counterpart, yet is cheaper than soft‐switched converters.
A fast and accurate approach for power losses quantification of photovoltaic power systems under partial‐shading conditions
Authors: Al‐Smadi Mohammad K. , Mahmoud Yousef , Xiao Weidong
Published: February 2021

Predicting the output power of partially‐shaded photovoltaic (PV) systems has a crucial role in the field of photovoltaic system studies and design. A significant number of models, circuit simulation techniques and power prediction formulations are proposed in the literature. However, several factors could limit their application such as computational burden, accuracy and their applicability to different levels of photovoltaic system. An analytical circuit‐based approach is proposed to predict the output power and power losses of partially‐shaded photovoltaic systems. The proposed approach is distinguished by high accuracy, low computational time and applicability to all photovoltaic system levels. Hence, it enables the assessment of partial shading mitigation ability of various photovoltaic system architectures. Furthermore, the proposed approach has no limitations on the size of photovoltaic system, for example, as in circuit‐based simulation analysis. An extensive simulation‐based comparison study for five partially‐shaded photovoltaic systems is conducted to compare the accuracy and computational time of the proposed approach with the detailed, well‐established single diode model. The accuracy of the proposed approach is also validated experimentally on a photovoltaic system of 12 series‐connected photovoltaic modules. Furthermore, a set of comparisons with experimental measurements from the literature is presented.
Load frequency control of a multi‐area system incorporating realistic high‐voltage direct current and dish‐Stirling solar thermal system models under deregulated scenario
Authors: Babu Naladi Ram , Saikia Lalit Chandra
Published: February 2021

A maiden attempt was made to demonstrate the impact of realistic high‐voltage direct current (RHVDC) tie‐line along with realistic dish‐Stirling solar thermal system (RDSTS) models in multi‐area load frequency control studies under a deregulated scenario. Area‐1, area‐2 and area‐3 comprise thermal‐RDSTS systems with relevant generation rate constraints. Each area includes two generation and two distribution companies. A new secondary controller with a cascaded combination of fractional order (FO) proportional derivative with filter coefficient (N) and FO‐proportional‐integral‐derivative with filter coefficient (N) (FOPDN‐FOPIDN) is proposed for the first time. The performance indices (peak magnitude, settling time and oscillations magnitude) of the proposed controller shows the dominance in comparison with the existing FOPI, PIDN and FOPIDN controllers in the policy schemes like poolco, bilateral and contract violation. A recent metaheuristic algorithm named the crow‐search algorithm is successfully applied for simultaneous optimisation of the proposed FOPDN‐FOPIDN controller gains and RDSTS parameters under the deregulated environment for the first time. Under the above policy schemes, the effect of RDSTS and RHVDC tie‐line using FOPDN‐FOPIDN controller is investigated, and the use of RHVDC tie‐line with AC tie‐line is explored. System dynamic response comparison with parallel AC‐RHVDC tie‐lines reveals better performance over AC tie‐line and RHVDC tie‐line when used alone.
Optimal flexibility coordination for energy procurement in distribution networks
Authors: Hooshmand Ehsan , Rabiee Abbas , Jalilzadeh Saeid , Soroudi Alireza
Published: February 2021

The main goal of distribution network operator is to establish a balance between supply and demand at the lowest cost while considering the technical constraints. Nowadays, distribution network operators exploit various types of flexibilities to minimise operational costs. However, each flexibility resource has its own technical and economic characteristics. This paper proposes a day‐ahead energy dispatch model which allows the distribution network operator to minimise the energy procurement costs on an hourly basis. The developed model considers various flexibility resources such as renewable energy sources, energy storage systems, demand response, optimal distribution system reconfiguration, and on‐load tap changers optimal settings. The proposed model is formulated as a convex mixed‐integer second‐order conic programming model. It is implemented on the IEEE standard 33‐bus and 70‐bus radial systems to demonstrate its capabilities. The obtained numerical results substantiate the role of distributed energy resources in energy procurement cost reduction, while the impact of distribution system reconfiguration and on‐load tap changers on voltage profile improvement.
Norm combination constrained set‐membership function‐based approach with limit cycle oscillator control of grid‐interactive photovoltaic system
Authors: Kumar Shailendra , Al‐Durra Ahmed , El‐Saadany Ehab
Published: February 2021

This study presents a norm combination constrained set‐membership function (CCSMF)‐based approach for two‐stage photovoltaic (PV) array to be used in the three‐phase three‐wire system. Here, the norm is introduced in the control technique to improve the response of existing approaches. The norm is the addition of the magnitudes of the load current components in a space. It is the most natural way of evaluating the various magnitude of harmonic components. In addition, the CCSMF technique furnishes harmonics disturbance rejection, current compensation, grid currents stabilisation and correction of power factor performances in order to allocate system integrated with residential non‐linear loads. Further, it is used to extract the fundamental component of load currents for generating the reference currents. The range of the amplitude and frequency of the grid voltages are continuously monitored by the limit cycle oscillator‐based algorithm. Under the outage of PV insolation, the voltage source converter (VSC) is switched to the compensator unit, and grid feeds power to the emergency load, which improves the utilisation factor of VSC. The presented system along with the proposed technique is tested in the laboratory and grid current total harmonic distortion is brought down within the limit of the IEEE‐519 standard.
Interval optimization‐based scheduling of interlinked power, gas, heat, and hydrogen systems
Authors: Nasiri Nima , Sadeghi Yazdankhah Ahmad , Mirzaei Mohammad Amin , Loni Abdolah , Mohammadi‐Ivatloo Behnam , Zare Kazem , Marzband Mousa
Published: February 2021

The combined heat and power (CHP) plant is one of the emerging technologies of gas‐fired units, which plays an important role in reducing environmental pollutants and delivering high energy efficiency. Moreover, the hydrogen energy storage (HES) system with extra power storage from wind turbine via power to hydrogen technology allows the injection of stored energy into the power grid by reverse hydrogen to power services, offsetting in this way the uncertainty of wind power. Consequently, simultaneous usage of CHP and HES units not only makes the maximum use of wind power distribution but also increases flexibility and reduces the operating costs of the entire network. Therefore, this paper proposes an interval optimization technique for managing the uncertainty of wind power generation in the integrated electricity and natural gas (NG) networks considering CHP–HES. Moreover, to enhance the flexibility of the NG network, a linearized Taylor series‐based model is proposed for modelling linepack of gas pipelines in the proposed scheduling framework that is formulated mixed‐integer linear programming and solved using the Cplex solver. The obtained results indicate that the simultaneous use of CHP–HES in the day‐ahead scheduling reduces the operating cost and increases the flexibility of the whole network.
Adaptive step size based drift‐free P&O algorithm with power optimiser and load protection for maximum power extraction from PV panels in stand‐alone applications
Authors: Gopahanal Manjunath Madhu , Vyjayanthi Chintamani , Modi Chirag N.
Published: February 2021

This paper presents an adaptive step size based drift‐free perturb and observe algorithm with power optimiser and load protection for maximum power extraction from photovoltaic panels in stand‐alone applications. The proposed algorithm consists of an adaptive step‐size technique, drift mitigation technique and power optimiser and load protection technique for fast maximum power extraction with less steady‐state power loss and protection features. The adaptive step‐size technique continuously monitors the absolute value of change in power () to adjust the perturbation step size () for fast tracking of maximum power point, and the drift mitigation technique mitigates the confusion in selecting the correct perturbation direction that exists in the conventional perturb and observe algorithm for a sudden change in irradiance conditions. Besides, the presented power optimiser and load protection uses a closed‐loop duty cycle (D) value for detecting possible underloading and overloading conditions, wherein, in case of underloading or overloading, it turns off the power optimiser to protect itself and the connected load from any damage or malfunctioning. The overall performance of the proposed algorithm, in terms of its dynamic operations, reduced steady‐state power loss, and protection features are evaluated using MATLAB simulations and verified with an experimental prototype. Comparative analysis of the proposed algorithm with state‐of‐the‐art perturb and observe techniques is carried out for validating its effectiveness and feasibility in real‐time applications.
VSC‐HVDC system‐based on model predictive control integrated with offshore wind farms
Authors: Sultan Yara A. , Kaddah Sahar S. , Eladl Abdelfattah A.
Published: February 2021

Recently, the offshore wind farms (OWFs) have excellent potential in global power networks. So, the demand for high power and high‐quality transmission system is becoming more pressing with the fast growth of OWFs. A promising solution is provided by the voltage source converter‐high voltage direct current (VSC‐HVDC) system. This paper proposes a control method for controlling the VSC‐HVDC system to integrate OWFs into power grids based on model predictive control (MPC). To minimise the distortion and filtering of the harmonics injected into the utility grid, the receiving end converter of the VSC‐HVDC system is connected with LCL filter. The proposed control strategy maintains active/reactive power‐sharing with efficient regulation of AC voltage among the different OWFs operation conditions. The detailed system components modelling and simulation are executed in the MATLAB/Simulink environment, which is used to perform both dynamics and transient evaluation of the suggested control strategy. The acquired simulation results emphasize the capability of the proposed control strategy to maintain system stability under different operation conditions compared to the synchronous reference frame‐based current limiting droop control method (SRF‐based CLD).
Design and modelling of self‐excited SRG and FM‐SRG for wind energy generation
Authors: Tefera Kitaba , Tripathy Praveen , Adda Ravindranath
Published: February 2021

The paper discusses the design and analysis of synchronous reluctance generator with a rating of 2.1 kW. These generators with low power ratings may be a suitable candidate for the rural electrification. In synchronous reluctance generator, the design of the rotor is the most challenging part of the design. Here, the thickness of the axial and tangential ribs plays a significant role in its performance. The design procedure presented in the paper includes the effect of stator resistance, as it cannot be neglected in the design of synchronous reluctance generator with low power ratings. The paper discusses the design procedure of synchronous reluctance generator and analyses the self‐excited ferrite‐magnet synchronous reluctance generator. Various parameters of the designed machine are analysed through an analytical model and Ansys Electronics Desktop software. Further, the paper also includes the experimental validation of the synchronous reluctance generator results obtained through finite element analysis. The paper also proposes formulae to approximate the minimum values of the excitation capacitor requirement, for a self‐excitation of synchronous reluctance generator with inductive load.
Modern electric machines and drives for wind power generation: A review of opportunities and challenges
Authors: Chen Hao , Zuo Yuefei , Chau K. T. , Zhao Wenxiang , Lee Christopher H. T.
Published: February 2021

With ever‐increasing concerns on energy crisis and environmental protection, there is a fast‐growing interest in wind power generation systems. As electric machines and drives are core components in wind turbines, it is a pressing need for researchers and engineers to develop advanced electric machines and drives for wind power generation. This paper provides a thorough review of modern electric machines and drives for wind power generation, with emphasis on machine topologies, operation principles, performance characteristics, as well as control strategies. The key features of electric machines and drives including their merits and demerits, e.g. torque/power density, efficiency, and cost, are compared and summarised. Trade‐offs involved in various presented methods and strategies are highlighted. The major challenges and difficulties, which electric machines and drives for wind power generation are facing, are discussed. Moreover, the developing trends and opportunities are revealed, while the latest development is also discussed.
Piezoelectric energy harvester converting wind aerodynamic energy into electrical energy for microelectronic application
Authors: R Sitharthan , S Yuvaraj , Padmanabhan Sanjeevikumar , Holm‐Nielsen Jens Bo , M Sujith , M Rajesh , N Prabaharan , K vengatesan
Published: February 2021

Piezoelectric Energy Harvesting Systems play a vital role in energizing microelectronic devices with the low‐frequency operation. Here, a novel piezoelectric energy harvesting device has been developed for low power electronic devices. The developed Piezoelectric Energy Harvesting Systems consists of a cantilever with poles projecting outwards and the cantilevers one end is connected to the wind‐catcher, and another end is connected to the torsional spring. The developed Piezoelectric Energy Harvesting Systems signifies its application in energizing microelectronic devices. The cantilever is placed inwards to the piezoelectric crystal stack. When the wind strikes, a vortex is created in the windcatcher, which oscillates the cantilever and generates stress in the piezoelectric crystal stack to develop electric energy. The output voltage obtained from the Piezoelectric Energy Harvesting Systems does not affect any input frequency of the piezoelectric crystal. The result obtained shows that the developed Piezoelectric Energy Harvesting Systems generates 120–200 eV with 2.9 × 1016–4.84 × 1016 Hz frequency considering an elementary charge unit as 40 for a variable wind flow of 4–9 m/s. This research aims to develop an efficient wind‐based Piezoelectric Energy Harvesting Systems for low powered microelectronic devices.
Deadbeat direct power control for dual three‐phase PMSG used in wind turbines
Authors: Jin Shibo , Zhao Wenxiang , Ji Jinghua , Xu Dezhi
Published: February 2021

The classical switching‐table‐based direct power control usually employs one active voltage vector during the whole sample period, which will bring distinct current harmonics and large power ripple. This paper proposes an improved deadbeat direct power control for a dual three‐phase permanent magnet synchronous generator. The deadbeat control of both active power and reactive power is employed to calculate the duty cycle of the active vector. By this way, the active and reactive power can follow the references as much as possible during each sample period. Then a weight coefficient is introduced and its effects on the steady‐state performances of active and reactive power are analysed. With this proposed method, the current harmonics and power ripple are suppressed considerably. Meanwhile, the simple structure and fast dynamic response are preserved. The experimental results validate the effectiveness of the proposed strategy.
Sensorless MRAS control of emerging doubly‐fed reluctance wind generators
Authors: Agha‐Kashkooli Mohammad‐Reza , Jovanović Milutin
Published: February 2021

A new model reference adaptive system based estimation technique for vector control of real and reactive power of a brushless doubly fed reluctance generator without a shaft position sensor is proposed. The rotor speed is being precisely observed in a closed‐loop manner by eliminating the error between the measured and estimated inverter‐fed (secondary) winding current angles in a stationary frame. Contrary to the existing model reference adaptive system observer designs reported in the brushless doubly fed reluctance generator literature, the reference model is entirely parameter‐free and only utilises direct measurements of the secondary currents. Furthermore, the current estimates coming from the adaptive model are obtained using the measured voltages and currents of the grid‐connected (primary) winding, which has provided prospects for much higher accuracy and superior overall performance. The realistic simulations, preliminary experimental results, and the accompanying parameter sensitivity studies have shown the great controller potential for typical operating conditions of variable speed wind turbines with maximum power point tracking.
A new soft‐switching multi‐input quasi‐Z‐source converter for hybrid sources systems
Authors: Torki Harchegani Amir , Asghari Amin , Jazaeri Mostafa
Published: February 2021

This study focuses on a zero voltage transition multi‐input quasi‐Z‐source converter (qZSC). This topology can be used for input sources with different voltages and currents to provide a constant output voltage. In the proposed structure, several qZSCs are combined and the output filter of all stages is eliminated. Therefore, the overall number of circuit elements, cost, volume and weight are reduced in comparison with when they are in a separate operation. In this converter, the soft‐switching conditions are provided for all stages by using only one auxiliary circuit. The employed technique provides zero‐voltage zero‐current switching (ZVZCS) and zero‐voltage switching for main switches at turn‐on and turn‐off instants, respectively. The auxiliary switch turns on under zero‐current switching and turns off under ZVZCS. Furthermore, the reverse recovery losses of diodes are reduced. The theoretical foundation of the proposed converter is presented and its performance is simulated by OrCAD software. The obtained results show 3.5% improvement in the efficiency at nominal loads, compared to its hard‐switching counterpart. A dual‐input prototype of the proposed structure is successfully built to support the validity of the theoretical analysis.
Multiple extended stealth target tracking from image observation based on non‐linear sub‐random matrices approach
Authors: Barbary Mohamed , Abd ElAzeem Mohamed H.
Published: February 2021

The detection and tracking of extended stealth targets (ESTs) is a challenging task in radar technology, especially if from image observations because of the fluctuations of radar cross section. To overcome this challenge, multi‐Bernoulli (MB) filter can be used to extract the extended target (ET) states in efficient and reliable manner. Recently, the MB‐filter‐based random matrices model (RMM) approach has been proposed for ellipsoidal ET tracking with additional state variables. However, RMM‐MB filter is demonstrated with known detection profile, which is unsuitable for EST tracking. Thus, a joint detection and tracking of multiple ESTs based on track‐before‐detect (TBD) approach, which is an efficient way to track low‐observable ESTs, is proposed. In EST‐RMM‐TBD scenarios, although the extension ellipsoid is efficient, it may not be accurate because of some missing useful information, such as size, shape, and orientation. To address this, a EST‐sub‐RMM‐TBD composed of sub‐ellipses is introduced, each representing an RMM. Based on such models, a sub‐RMM‐MB‐TBD filter is used to estimate the kinematic states and extensions of sub‐objects for each EST. Furthermore, a sequential Monte Carlo (SMC) implementation to estimate non‐linear kinematic EST state is applied. The results indicate that the proposed SMC‐sub‐RMM‐MB‐TBD filter has more accurate cardinality estimation and smaller optimal sub‐pattern assignment errors than the recent extended tracking filters.
Incentive‐based feasibility of a hybrid AC/DC microgrid in Saudi Arabia
Authors: Alahmed A. , Alharbi H. , Elamin I. , Al‐Muhaini M.
Published: February 2021

The concept of microgrids (MGs) is revolutionizing the current power system. Most of the contemporary MGs are AC based; however, and due to the growth of DC‐based machines and household appliances, hybrid AC/DC MGs are noticeably gaining more attention. This paper carries a detailed and comprehensive economic analysis on a proposed hybrid AC/DC MG in the Kingdom of Saudi Arabia, a region where MGs are rarely studied. The aim is to find if such configurations are economically feasible in the region, and what incentives are needed to ensure feasibility. Multiple load categories and renewable generation sources are modelled from real and local data to enhance the practicality of the study. Using the designed MG testbed, the incentive‐based economic feasibility study is implemented to analyze the effectivity of the configuration and the proposed testbed in the region. The research concludes that the hybrid framework is not economically feasible due to the subsidized low energy retail prices in the kingdom, which makes customers unwilling to join a MG network that might charge higher rates. A comprehensive financial model is introduced to suggest proper incentives, possibly by the government, to the different customer and income classes to make the AC/DC MG option feasible.
Efficient tracking of spatially correlated signals in wireless sensor fields: A weighted stochastic gradient approach
Authors: Alasti Hadi
Published: February 2021

A weighted stochastic gradient algorithm is proposed for cost‐efficient tracking of unknown, correlated spatial signals from randomly distributed sensor observations in localized wireless sensor field. The algorithm is implemented in spatial modelling and spatial tracking phases. In spatial modelling phase, the algorithm finds the model parameters, and in spatial tracking phase, it updates these parameters. The spatial signal is modelled with its M iso‐contour lines at equally spaced levels and the sensors with sensor observations in Δ margin of these contour levels report to the fusion centre (FC) for spatial monitoring purpose. Based on progressive learning and in successive iterations, the algorithm improves its findings of the signal strength's range, and the spatial, temporal and spectral attributes of the signal. To reduce the cost, in each iteration, only a subset of wireless sensors transmits the observations to the FC, in response to its query. In this article, the percentage of the reporting sensors to the FC is defined as the algorithm's cost. With importance sampling perspective, the sample space is reduced to those sensors whose observations are within a Δ margin of atleast one of these M contour levels. The Δ margin is pruned or enhanced using the proposed weighted stochastic gradient algorithm, dynamically in order to reduce the spatial tracking cost. The evaluation results show that after spatial modelling, spatial tracking is drastically of low cost and its performance is better than that of the conventional stochastic gradient method. The modelling error, the cost and the convergence of the proposed algorithm are investigated extensively, in this article. Spatial correlation in signal distribution and the coordinates of the wireless sensors are the only initial assumptions in spatial monitoring of the unknown signal distribution. The main purpose of this algorithm is low‐cost identification of unknown correlated spatial signals from sensor observations, over time. An example for application of the proposed algorithm is environmental monitoring using wireless sensor observations.