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Abstract Power quality is a crucial determinant for integrating wind energy into the electrical grid. This integration necessitates compliance with certain standards and levels. This study presents cascadedfuzzy power control (CFPC) for a variable-speed multi-rotor wind turbine (MRWT) system. Fuzzy logic is a type of smart control system already recognized for its robustness, making it highly suited and reliable for generating electrical energy from the wind. Therefore, the CFPC technique is proposed in this work to control the doubly-fed induction generator (DFIG)-based MRWT system. This proposed strategy is applied to the rotor side converter of a DFIG to improve the current/power quality. The proposed control has the advantage of being model-independent, as it relies on empirical knowledge rather than the specific characteristics of the DFIG or turbine. Moreover, the proposed control system is characterized by its simplicity, high performance, robustness, and ease of application. The implementation of CFPC management for 1.5 MW DFIG-MRWT was carried out in MATLAB environment considering a variable wind speed. The obtained results were compared with the direct power control (DPC) technique based on proportional-integral (PI) controllers (DPC-PI), highlighting that the CFPC technique reduced total harmonic distortion by high ratios in the three tests performed (25%, 30.18%, and 47.22%). The proposed CFPC technique reduced the response time of reactive power in all tests by ratios estimated at 83.76%, 65.02%, and 91.42% compared to the DPC-PI strategy. Also, the active power ripples were reduced by satisfactory proportions (37.50%, 32.20%, and 38.46%) compared to the DPC-PI strategy. The steady-state error value of reactive power in the tests was low when using the CFPC technique by 86.60%, 57.33%, and 72.26%, which indicates the effectiveness and efficiency of the proposed CFPC technique in improving the characteristics of the system. Thus this control can be relied upon in the future.

The precise estimation of solar PV cell parameters has become increasingly important as solar energy deployment expands. Due to the intricate and nonlinear characteristics of solar PV cells, meta-heuristic algorithms show greater promise than traditional ones for parameter estimation. This study utilizes the Puffer Fish (PF) meta-heuristic optimization method, inspired by male puffer fish's circular structures, to estimate parameters of a modified four-diode PV cell. The PF algorithm's performance is assessed against ten benchmark test functions, with results presented as mean and standard deviation for validation. Comparative analysis with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Rat Search Algorithm (RAT), Heap Based Optimizer (HBO), and Cuckoo Search (CS) algorithms highlights PF's superior performance, achieving optimal solutions with minimal error of 7.8947E-08. Statistical tests, including Friedman Ranking (1st) and Wilcoxon's rank sum (3.8108E-07), confirm PF's superiority. The circular structures of male puffer fish serve as an effective model for optimization algorithms, enhancing parameter estimation. Benchmark tests and statistical analysis consistently underscore PF's superiority over other meta-heuristic algorithms. Future research should explore PF's potential applications in solar energy and beyond.

Electric vehicles (EVs) are unquestionably the future of transportation due to their numerous advantages, such as their ability to address environmental issues, decrease dependence on fossil fuel, and enhance energy security. Attaining optimum performance, efficiency, and safety in EVs requires accurate control over both the speed and torque of the electric motor used. The Doubly Fed Induction Motor (DFIM) is classified as a motor that offers precise control over both its speed and torque. Additionally, it has some features that render it suitable for EV applications. This study contributes to advancing the field of new control systems for a DFIM used in EV applications. A key novelty of this work is the introduction of the Backstepping-Direct Torque Control (BSC-DTC) system for DFIM in EV applications. The suggested controller accurately tracks the specified speed set by the EV driver (reference speed) without any deviation, which is a key characteristic in modern EVs. Furthermore, the torque of the DFIM is regulated using the developed DTC method, BSC-DTC. To assess the capability of the BSC-DTC to effectively monitor the reference speed and achieve torque control, a comparison with conventional DTC is provided. Furthermore, an experimental implementation was conducted using the DS1104 board developed by dSPACE to validate the simulation results, highlighting the practical feasibility of the proposed system. Comprehensive modeling of the EV drivetrain was conducted and simulated utilizing the MATLAB-Simulink environment. The simulation findings demonstrate that the suggested control system offers superior performance for the EV system in terms of accurate reference speed tracking with zero tracking error and a slight overshoot of 0.1 km/h, as well as dynamic torque response. However, ripples in the electromagnetic torque provided by the motor have been observed. To address this issue, another innovative aspect of the work involves focusing future efforts on implementing control approaches based on artificial intelligence methodologies, opening new research and innovation avenues.

Identifying stator imbalance problems in induction machines (IMs) is crucial to preserving operating efficiency, reliability, and safety. This identification contributes to the IM’s overall health, prolongs its lifespan, and helps organizations meet regulatory and performance standards. Moreover, it enables the execution of maintenance tasks when required, improving resource use and decreasing shutdown periods. This research presents a Non-Destructive Technique (NDT) that utilizes a thermography-based machine learning algorithm for diagnosing Stator Imbalance Defects (SID) within IMs experimentally. This technique relies on assessing temperature distribution on the outer surface of the IM stator. Then, the evaluation of various statistical variables that provide valuable insights into the normality of temperature distribution within the designated area referred to as the Region of Interest (ROI) is presented. Initially, thermal images of the stator are captured under various operating conditions, including a healthy state, SID presence, and phase missing. Then, all thermal images are converted into thermographic data matrices for statistical analysis using the Machine Learning Algorithm Gaussian Naïve Bayes (GNB). This analysis aimed at classifying SID in IMs arising from deviations in the normalcy of temperature distribution within the ROI. To demonstrate the efficacy of the suggested GNB approach, a comparison with the Subspace Discriminant Classifier and Cubic Support Vector Machine is provided. The GNB algorithm exhibits exceptional accuracy of 95.2% in classification tasks while maintaining a minimal processing time, irrespective of the operational duration of the IM.

In the future, renewable energy sources will be of great importance in overcoming the problems of power generation using traditional sources, as their use contributes to protecting the environment from pollution and helps to raise human development. Therefore, it is necessary to search for a generation system that has high characteristics to raise the quality of energy and reduce the total cost of producing, transmitting, and consuming energy. This work proposes a new nonlinear direct reactive and active power control (DRAPC) for grid-connected double-powered asynchronous generators (DPAGs) in multi-rotor wind power (MRWP) systems. The designed DRAPC employs a modified synergetic control theory (MSCT) to regulate the DFPG-MRWP stator power. With out involving any synchronous coordinate transformations, the DRAPC-MSCT reduces the steady-state error (SSE), ripples and response time of stator powers while directly determining the required rotor control voltage. This technique fundamentally improves the transient performance compared to the DRAPC-SCT. Constant switching frequency is achieved as well by using pulse width modulation, which eases the designs of the power converters and AC harmonic filters.Through the use of three test cases (tracking test, robustness test, and variable-speed test using the Maximum Power Point Tracking), the potential of the DRAPC-MSCT to reduce system instability and uncertainty is evaluated. This comprises the ability of the controller to track DPAG powers references and improve performance during variations in system parameters and wind speed.Simulated results on a 1.5 MW grid-connected DPAG-MRWP are presented and compared with those of the DRAPC-SCT. The DRAPC-MSCT improves the reactive and active power ripples by 73.33% and 84.50%, respectively. Besides, improves the SSE of the reactive and active power by 51.61% and 69%, respectively compared to the DRAPC-SCT. The simulation results demonstrate the high performance and robustness of the DRAPC-MSCT for parametric variations of DPG-MRWP compared to the DRAPC-SCT.

Recent years have seen a marked increase in the world's energy needs. Numerous studies have been conducted to examine distributed generation (DG) utilizing renewable energy sources (RESs) in order to address this need. The number of environmental problems that are raised by the usage of traditional power plants is also decreased by these renewable sources. The ideal position and size of the RESs-DG significantly influence the bus voltage profile, power quality, and efficiency of Radial Distribution Systems (RDS) because of power losses. In this study, the use of wind energy systems as a DG source in RDS is investigated. One of the most common RESs used as DG sources, the ideal location and size for wind system, was chosen to demonstrate this enquiry. The goal of this optimization work, which used the Arithmetic Optimization Algorithm (AOA), was to increase system efficiency by minimizing power losses and improving the voltage profile and power quality. Two widely used RDS, including the IEEE 31 and 69 bus systems, have been used to evaluate how well the recommended technique has been implemented. Genetic Algorithm (GA) is offered to examine the efficacy of the recommended AOA. The findings show that the used AOA approach can pinpoint the appropriate size and positioning of a wind farm in order to reduce power loss, enhance voltage profile, and outperform other existing tactics with superiority over GA.

This work provides regulation of active and reactive power for asynchronous generator-based multi-rotor wind power (MRWP) systems using a synergetic control based on fractional order control theory. The traditional synergetic control theory achieves an undesirable chattering effect, which can damage the system. For this, it is relevant to combine the traditional synergetic control theory with fractional order control to avoid these drawbacks. Two control schemes are proposed, analyzed, and compared: traditional direct power control (DPC) and DPC based on fractional-order synergetic controllers, the latter needs only the information of the sliding surface. Their characteristics are compared in terms of current quality, active and reactive power reference tracking, robustness against generator parameter variations, and power ripple. Simulation results using Matlab software have demonstrated the characteristics and robustness of the suggested DPC based on fractional-order synergetic controllers compared to the DPC because of its efficacy in improving the quality of power/current.

The integration of wind energy systems into the electric grid has become inevitable despite the many problems associated with this integration. Most of these problems are due to variations in wind speed. The problems are for example oscillations in the power generated, which implies the lack of guarantee of obtaining the maximum energy and the ripple in the electromechanical torque of the generator. This work aims at mitigating these problems for wind energy conversion system-driven doubly-fed induction generator (DFIG), which is the main wind turbine utilized for energy applications. This mitigation is performed through direct reactive and active powers control of the DFIG using an artificial neural network. A DSP (Digital Signal Processor-dSPACE DS1104) was used to experimentally test the proposed strategy. The dynamic performances of the controlled generator are analyzed by using the designed intelligent control strategy in the case of variable wind speeds and upon sudden change of the active power demand. Based on the obtained experimental results, it can be said that the designed intelligent control strategy outperforms traditional methods like direct power (DPC) and vector control in terms of reducing the current harmonics, and torque ripples, and enhancing dynamic response.

Despite having higher maintenance costs than AC motors, DC motors had been widely employed in the industry due to their outstanding speed control capabilities. This employment increased due to the DC output of some renewable sources recently. This article introduces the speed control of DC motors using model reference adaptive control (MRAC). This control is achieved through regulating the armature voltage at different load changes. A comparison between the proposed adaptive controller and optimized PI controller using the elephant herding optimization (EHO) is presented. The PI controller parameters were optimality adjusted to minimize the integral absolute error, minimum overshoot, and minimum settling time. Computer simulations show that the suggested MRAC is preferable to a traditional optimized PI controller. In addition, the proposed controller is effective in regulating the DC motor over a broad range of operating speeds.

We present the ferroresonance overvoltage mitigation concerning the power systems of the grid-connected wind energy conversion systems (WECSs). WECS is considered based on a doubly-fed induction generator (DFIG). Ferroreso-nance overvoltage associated with a single-pole outage of the line breaker is mitigated by fast regulating the reactive power using the static compensator (STATCOM). STATCOM controller is introduced, in which two incorporated proportional-inte-gral (PI) controllers are optimally tuned using a modified flower pollination algorithm (MFPA) as an optimization technique. To show the capability of the proposed STATCOM controller in mitigating the ferroresonance overvoltage, two test cases are introduced, which are based on the interconnection status of the power transformer used with the grid-connected DFIGs. The results show that the ferroresonance disturbance can occur for the power transformers installed in the wind farms although the transformer terminals are interconnected, and neither side of the transformer is isolated. Furthermore, as a mitigation method of ferroresonance overvoltage, the proposed STATCOM controller succeeds in improving the system voltage profile and speed profile of the wind turbine as well as protecting the system components against the ferroresonance overvoltage.

Abstract Solar photovoltaic (PV) energy has met great attention in the electrical power generation field for its many advantages in both on and off-grid applications. The requirement for higher proficiency from the PV system to reap the energy requires maximum power point tracking techniques (MPPT). This paper presents an adaptive MPPT of a stand-alone PV system using an updated PI controller optimized by harmony search (HS). A lookup table is formed for the temperature and irradiance with the corresponding voltage at MPP (VMPP). This voltage is considered as the updated reference voltage required for MPP at each temperature and irradiance. The difference between this updated reference voltage at MPP and the variable PV voltage due to changing the environmental conditions is used to stimulate PI controller optimized by HS to update the duty cycle (D) of the DC–DC converter. The temperature, irradiance, and corresponding duty cycle at MPP are utilized to convert this MPP technique into an adaptive one without the PI controllers' need. An experimental implementation of the proposed adaptive MPPT is introduced to test the simulation results' validity at different irradiance and temperature levels.

Generating electric power using solar thermal systems is effective, particularly for countries with high solar potential. In order to decide on a relevant location to implement the solar tower plant and develop the mathematical model of a no-blocking heliostat field, a meteorological assessment was discussed in this paper. In addition, a parametric study was examined to evaluate the effect of the designed parameters (heliostat size, heliostat height from the ground, tower height, receiver aperture, and the minimum radius) on the solar field’s performance. The preliminary solar field was then compared to the final design using the optimal design parameters. The obtained results showed that “Tamanrasset City” satisfied the necessary conditions for implementing a solar tower plant. According to preliminary solar field generation, no heliostat blocked its neighbor with a blocking efficiency of 100%. An analysis of its performance revealed that the optimized solar field would be capable of producing 15, 6571 MW, operating at an optical efficiency of 76.95%, and the enhancement rate of both efficiency and power output was 8.1%.

Switched reluctance motors (SRMs) received major interest in several domains, e.g., in electric vehicles. This interest is due to the many advantages of SRMs, including operation at a wide range of speeds, high performances, low cost, robustness to run under degraded conditions, and controllability. One of the major aspects in the design and implementation of controllers for SRMs is the estimation of the motor parameters. An accurate estimate of these parameters is a challenge due to the highly nonlinear behavior of SRMs in addition to their magnetic saturated operating mode to maximize the energy transfer. This paper aims at estimating the parameters of SRM by developing a new SRM model using an analytical technique. The proposed technique is based on a parallel connection of several Hammerstein models that have polynomial nonlinearity. The model is driven by a swept-sine signal, and then finite element method analysis is performed to estimate the SRM parameters. The effectiveness of the proposed method is highlighted by numerical simulation. All these simulations were performed using MATLAB/SIMULINK.

Solar photovoltaic (PV) energy has met great attention in the electrical power generation field for its many advantages. The connection of the PV systems to the electrical network is performed through DC-DC converter and DC-AC inverter. This paper aims at enhancing the integration of the PV systems into the electrical network through controlling both the converter and the inverter. An adaptive reference PI (ARPI) controller for the inverter is introduced to enhance the system performance through supporting low voltage ride through (LVRT) capability, and smoothing the PV generated power fluctuations during variable environmental conditions. While the converter is used to achieve the maximum power point tracking (MPPT) of the PV system-based perturb and observe (P&O) algorithm at different environmental conditions. A comparison between the system performance using the proposed ARPI and an optimal PI controller optimized by harmony search (HS) is introduced to test the effectiveness of the proposed strategy.

This article introduces an optimal PI controller optimized by elephant herding algorithm (EHA) to control the grid-tied four-phase 8/6 switched reluctance generator (SRG). This controller is introduced for enhancing the low voltage ride through (LVRT) capability of the grid-tied SRG during faulty conditions that affect the SRG output voltage by regulating the off-delay angle of the generator. These faulty conditions include faults taken place at the point of connection (POC) of the SRG to the grid and wind speed variations. The proposed optimized PI controller regulates the off-delay angle of the generator to keep the voltage at the POC within acceptable limits. Maintaining these acceptable voltage limits during faulty conditions that keeps the generator in connection to the grid and makes the generator complying with the grid codes. Comparison between EHA optimization technique, cuckoo search optimization and particle swarm optimization (PSO) were presented to illustrate the effectiveness of the modern optimization technique used, EHA. An experimental verification of the proposed system is introduced by rewinding the stator and reshaping the rotor of three-phase induction machine to form four-phase, 8/6 SRG. Finite element method (FEM) based on ANSYS software is utilized to get the magnetic characteristics of the reconstructed 8/6 SRG after drawing the 3-D geometrical dimension in AUTOCAD.

No one denies the importance of renewable energy sources in modern power systems in terms of sustainability and environmental conservation. However, due to their reliance on environmental change, they are unreliable systems. This paper uses a Unified Power Flow Controller (UPFC) to enhance the reliability and performance of grid-tied renewable energy systems. This system consists of two renewable sources, namely photovoltaic cells (PV) and wind turbines (WTs). The UPFC was selected for its unique advantage in both active and reactive power control. The UPFC is controlled with an optimized Fractional Order Proportional–Integral–Derivative (FOPID) controller. The parameters of this controller were tuned using an Atomic Search Optimization (ASO) algorithm. Simulation results confirm the efficiency of the suggested controller in supporting the reliability and performance of the hybrid power system during some disturbance events including voltage sag, swell, and unbalanced loading. In addition, power quality can be improved through reducing the total harmonic distortion. It is worth mentioning that two maximum point tracking techniques had been included for the PV and WT systems separately. MATLAB/SIMULINK 2021a software was used to model the system.

This study proposes a simplified super-twisting algorithm (SSTA) control strategy for improving the power quality of grid-connected photovoltaic (PV) power systems. Some quality issues are considered in this study including the power factor, reducing the total harmonic distortion (THD) of current, compensating the reactive power, and injecting at the same time the energy supplied by the PV system into the grid considering non-linear load. This improvement is achieved by two topologies; controlling both the boost DC–DC converter and the DC–AC inverter that links the PV system to the grid. The DC–DC converter is controlled using proportional-integral (PI) and SSTA to maximize the power generated from the PV panel regardless of its normal and abnormal conditions, while the DC–AC inverter is employed to direct power control strategy with modified space vector modulation using the phase-locked loop (PLL) technique of a three-level neutral-point-clamped (NPC) inverter based on the proposed strategies (PI and SSTA). In addition, a shunt active power filter (SAPF) is used to connect the PV system to the AC grid and feed a non-linear load. To validate the simulation results presented in this paper using Matlab software, a comparative study between the PI controller and the SSTA is presented. The results show the effectiveness and moderation of the suggested SSTA technique in terms of feasibility, tracking performance, less power ripple, dynamic response, THD value, overshoot, steady-state error, and robustness under varying irradiation, temperature, and non-linear conditions.

The need for energy has significantly increased in the world in recent years. Various research works were presented to develop Renewable Energy Sources (RESs) as green energy Distributed Generations (DGs) to satisfy this demand. In addition, alleviating environmental problems caused by utilizing conventional power plants is diminished by these renewable sources. The optimal location and size of the DG-RESs significantly affect the performance of Radial Distribution Systems (RDSs) through the fine bus voltage profile, senior power quality, low power losses, and high efficiency. This paper investigates the use of PV (photovoltaic) and (Wind Turbine) WT systems as a DG source in RDSs. This investigation is presented via the optimal location and size of the PV and WT systems, which are the most used DG sources. This optimization problem aims to maximize system efficiency by minimizing power losses and improving both voltage profile and power quality using White Shark Optimization (WSO). This algorithm emulates the attitude of great white sharks when foraging using their senses of hearing and smell. It confirms the balance between exploration and exploitation to discover optimization that is considered as the main advantage of this approach in attaining the global minimum. To assess the suggested approach, three common RDSs are utilized, namely, IEEE 33, 69, and 85 node systems. The results prove that the applied WSO approach can find the best location and size of the RESs to reduce power loss, ameliorate the voltage profile, and outlast other recent strategies. Adding more units provides a high percentage of reducing losses by at least 93.52% in case of WTs, rather than 52.267% in the case of PVs. Additionally, the annual saving increased to USD 74,371.97, USD 82,127.257, and USD 86,731.16 with PV penetration, while it reached USD 104,872.96, USD 116,136.57, and USD 155,184.893 with WT penetration for the 33, 69, and 85 nodes, respectively. In addition, a considerable enhancement in the voltage profiles with the growth of PV and WT units was confirmed. The ability of the suggested WSO for feasible implementation was validated and inspected by preserving the restrictions and working constraints.

Developing countries' energy sector faces a multitude of challenges, ranging from inadequate generation to unstable grids. Power outages are among the most common issues, particularly in remote areas. Utilizing grid-tied green energy resources to address this issue and to cover for power outages from local grids. This article presents a cost-effective design of a grid-tied, hybrid green energy system (GES) consisting of wind, PV, and batteries considering the influence of the grid availability. A multi-objective, optimal techno-economic design, optimized by multi-objective particle swarm optimization technique is presented for the grid-tied GES linked to a small hamlet in the north of Egypt. The multi-objective function introduced in this work includes three objective functions which are, the Loss of Power Supply Probability (LPSP), the Cost of Energy (COE), and the System Surplus Energy Rates (SSER) considering the grid availability. The grid availability (GA) of 100% was considered as a base case and it was reduced to 70% with a step of 5%. The simulation consequences had cleared that the lowest and largest percentage values of SSER were obtained at GA of 85 %, and 70 %, respectively. When the value of SSER equal to 0.33%, the system design for solving the grid unavailability consists of 12 PVs, one WT, and 1420 batteries with COE of 0.145$/kWh and TNPC of 3,699,800 ($).

Connecting different renewable energy sources (RESs) to the electrical grids is presently being urged to fulfill the enormous need for electric power and to decrease traditional sources' ecological related issues, the so-called hybrid systems. Unfortunately, these hybrid systems suffer from the possible negative environmental impacts of the wind gusts in wind energy conversion systems (WECSs) that may degrade the overall system performance. Additionally, various severe faults may disconnect some RESs from the hybrid system, like three-phase faults. In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid. The STATCOM is stimulated by two PI controllers regulating the reactive power flow between the STATCOM and the hybrid system at PCC and, consequently, regulating the voltage at PCC. A metaheuristic optimizer optimally schedules these two PI controllers based on whale optimization algorithm (WOA). The impartial comparison between the WOA dynamic performance and the particle swarm optimization as another optimization algorithm verifies the efficiency of the WOA for the near-optimal gain scheduling of the PI controller gains.

Integrating renewable energy sources (RESs) into electrical power systems has gotten highly noticeable among researchers and those interested in electrical energy production due to the increase in energy demands, fossil fuel exhaustion, and ecological effects. PV-based renewable energy generation is one of the essential RESs that has appeared and had played a vital role in electrical power systems recently due to their advantages. In this regard, this paper presents a multi-objective computation problem for optimal siting and the design of grid-tied PV systems to achieve optimum generating reliability, considering some states of different generation probabilities. The proposed paper studies the evaluation of the grid-tied PV systems reliability, the states of generation probabilities, the generation buses availabilities, the capacities of the generation’s system in or out of service for each failure state, and the frequency and mean duration of generation failure states. The presented multi-objective computation problem is optimized using a modified adaptive accelerated particle swarm optimization (MAACPSO) algorithm. The effectiveness of the proposed method is demonstrated through IEEE_EPS_24_bus integrated with PV systems. Results revealed the ability of MAACPSO to solve the multi-objective optimization problem presented, consequently supporting the system reliability.

Frequency Response Analysis (FRA) is the most reliable technique currently used to evaluate the mechanical integrity of power transformers. While the measurement devices have been well developed over the past two decades, interpretation of the FRA signatures is still challenging regardless of the several papers published in this regard. This paper adds an attempt to understand the power transformer FRA signatures through experimental and simulation analyses. In this context, experimental FRA measurements are conducted on a 33/11 kV, 30 MVA transformer under various faults, including winding deformation, the short circuit turns, loss of clamping, and bushing fault. At the same time, the high-frequency transformer model that comprises series capacitance, self-inductance, series resistance, and mutual inductance is simulated using MATLAB / Simulink to compare simulation and experimental results. The correlation between physical circuit parameters and various faults facilitates a better understanding of each fault’s effect on the FRA signature. To quantify the impact of such faults, correlation coefficient, the absolute sum of logarithmic error, standard deviation, and sum square error are calculated with respect to the healthy signature at three frequency regions. Results show that using statistical coefficients over three frequency ranges of the FRA signature facilitates better fault identification and quantification.

Wind Turbine Generator (WTG) has become one of the most popular renewable-based power generation that is broadly connected to electricity grids worldwide. Till the year 2019, the total WTGs installed worldwide reached about 650.8GW. The WTG is interfaced to the electricity grid through power electronic converters with a proper control algorithm to facilitate a smooth power delivery as well as maintaining the system voltage and frequency stability during wind intermittency. However, power grids are usually subjected to load expansion which affects the stiffness of the grid and hence its stability. A weak electricity grid exhibits voltage instability that may affect the performance of WTGs and in some cases may lead to serious damages to the wind turbines and the entire system. In this paper, superconducting magnetic energy storage (SMES) technology based on fuzzy logic controller is implemented to effectively resolve this issue and improve the overall performance of WTGs. Hysteresis current and fuzzy logic-based control system is proposed to control the energy exchange between the SMES coil and the investigated system. Results show the effectiveness of the SMES to improve the overall system performance and along with the fault ride-through capability of the doubly-fed induction generator (DFIG).

This article proposes an Artificial Neural Network (ANN) controller of Dynamic Voltage Restorer (DVR) to improve the performance of a stand-alone hybrid renewable energy system that is feeding a new community located in Egypt. The hybrid system consists of three renewable energy sources, namely, solar PV cells, a wind turbines based-permanent magnet synchronous generator, and fuel cells. These three sources are tied to a common DC link by three boost converters, one for each source. The common DC link is connected to the AC side via a DC/AC inverter. The optimal size of the three proposed renewable sources is calculated using the HOMER software package. The DVR control is attained through regulating the load voltage at different anomalous working conditions. These conditions are three-phase fault, voltage sag/swell, and unbalanced loading. Two ANNs are utilized to adjust the IGBT pulses of the voltage source inverter (VSI) used to control DVR by regulating the D-Q axes voltage signals. These D-Q axes components at any loading condition represent the inputs to the two ANNs. The outputs of the two ANNs represent the IGBT pulses. The input/output data used for training ANNs are obtained by two optimized PI controllers, introduced for regulating the load voltage through DVR-VSI pulses at different abnormal operating conditions, and accordingly convert the static optimized PI controller into adaptive one based ANN. The system performance with the proposed ANN-DVR controller is enhanced through improving the current, voltage, and power waveforms of each generating source. With compensation of the faulty line voltage, the system retains an uninterrupted operation of the three renewable sources during fault events and consequently increases the low voltage ride through (LVRT) capability. Moreover, the total harmonic distortion is reduced.

Integrating renewable energy resources (RERs) has become the head of concern of the modern power system to diminish the dependence of using conventional energy resources. However, intermittent, weather dependent, and stochastic natural are the main features of RESs which lead to increasing the uncertainty of the power system. This paper addresses the optimal reactive power dispatch (ORPD) problem using an improved version of the lightning attachment procedure optimization (LAPO), considering the uncertainties of the wind and solar RERs as well as load demand. The improved lightning attachment procedure optimization (ILAPO) is proposed to boost the searching capability and avoid stagnation of the traditional LAPO. ILAPO is based on two improvements: i) Levy flight to enhance the exploration process, ii) Spiral movement of the particles to improve the exploitation process of the LAPO. The scenario-based method is used to generate a set of scenarios captured from the uncertainties of solar irradiance and wind speed as well as load demand. The proposed ILAPO algorithm is employed to, optimally, dispatch the reactive power in the presence of RERs. The power losses and the total voltage deviations are used as objective functions to be minimized. The proposed algorithm is validated using IEEE 30-bus system under deterministic and probabilistic conditions. The obtained results verified the efficacy of the proposed ILAPO for ORPD solution compared with the traditional LAPO and other reported optimization algorithms.

Today, the grid-connected photovoltaic (PV) power system plays a significant role in generating electrical energy by making use of the sun's rays, then converting it into usable electrical energy and connecting it to the electrical grid. This process can be done by connecting the PV panel to the DC/DC converter then there will be a controlled DC/AC inverter. This paper discusses a literature review about the PV system, types, advantages, and disadvantages, generating level ratings and levels, statistics, and comparisons between countries worldwide. In addition, a review of the control method presented in PV systems.

Photovoltaic (PV) systems are one of the promising renewable energy sources that have many industrial applications; one of them is water pumping systems. This paper proposes a new application of a PV system for water pumping using a three-phase induction motor while maximizing the daily quantity of water pumped while considering maximizing both the efficiency of the three-phase induction motor and the harvested power from the PV system. This harvesting is performed through maximum power point tracking (MPPT) of the PV system. The proposed technique is applied to a PV-powered 3 phase induction motor water pumping system (PV-IMWPS) at any operating point. Firstly, an analytical approach is offered to find the optimal firing pattern of the inverter (V-F) for the motor through optimal flux control. This flux control is presented for maximizing the pump flow rate while achieving MPPT for the PV system and maximum efficiency of the motor at any irradiance and temperature. The provided analytical optimal flux control is compared to a fixed flux one to ascertain its effectiveness. The obtained feature of the suggested optimal flux control validates a significant improvement in the system performances, including the daily pumped quantity, motor power factor, and system efficiency. Then converting the data from the first analytical step into an intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS is trained offline with the input (irradiance and temperature) while the output is the inverter pattern to enhance the performance of the proposed pumping system, PV-IMWPS.

Three-phase induction motors are the most prevalent AC motors used in many applications. Operation of these motors at load power less than that of the motor full-load power leads to significant power losses and reduces the motor's efficiency. To mitigate these losses and increase the efficiency of the three-phase induction motors at light loading conditions, energy-saving techniques should be utilized. This paper investigates a voltage control technique applied to light-loaded induction motors to save the energy using an inverter. The light load selected in this paper is a fan load of power load less than the motor full load power. Theoretical analysis, computed characteristics, and experimental verifications are presented to show the effectiveness of the proposed energy-saving technique.

Wind speed variations affect the performance of the wind energy conversion systems (WECSs) negatively. This paper addressed an advanced law of the backstepping controller (ABC) for enhancing the integration of doubly fed induction generator (DFIG)-based grid-connected WECS under wind range of wind speed. This enhancement was achieved through three control schemes, which were blade pitch control, rotor-side control, and grid-side control. The blade pitch control was presented to adjust the wind turbine speed when the wind speed exceeds its rated value. In addition, the rotor and grid-side converter controllers were presented for improving the direct current link voltage profile and achieving maximum power point tracking (MPPT) under speed variations, respectively. To evaluate the effectiveness of the proposed ABC control, a comparison between PI and sliding-mode control (SMC) was presented, considering the parameters of a 1.5 MW DFIG wind turbine in the Assilah zone in Morocco. Moreover, some changes in the DFIG parameters were introduced to investigate the robustness of the proposed controller under parameter uncertainties. Simulation results showed the capability of the proposed ABC controller to enhance the performance of the DFIG-WECS based on variable speed and variable pitch turbine, at both below and above-rated speed, leading to an error around 10−3 (p.u), with an ATE = 0.4194 in the partial load region; in terms of blade pitch control, an error of 2.10−4 (p.u) was obtained, and the DC-link voltage profile showed a measured performance of 5 V and remarkable THD value reduction compared to other techniques, with a measured THD value of 2.03%, 1.67%, and 1.46% respectively, in hyposynchronous, hypersynchronous, and pitch activation modes of operation. All simulations were performed using MATLAB/SIMULINK based on real wind profiles in order to make an exhaustive analysis with realistic operating conditions and parameters.

Recently, there was a great focus on integrating renewable energy sources (RESs) into electrical power systems (hybrid systems) due to their many environmental and economic advantages. The output of most of these RESs is DC; some power electronic devices, including inverters, must be used to integrate these RESs into the electrical grid. Any maloperation, faults, or improper control in these power electronic devices will enormously affect these hybrid systems’ performance. This paper aims to mitigate the misfire and fire-through faults that occur at the switching of the inverter that connects three renewable sources: PV, wind, and the fuel cell to the grid. This mitigation of such inverter faults (misfire and fire-through) is performed through optimal tuning of the PI controller driving a dynamic voltage restorer (DVR) connected at the system’s AC side. The optimization technique used is particle swarm optimization (PSO). While mitigating these two inverter faults using the PI-PSO controller for the DVR, improved system performance through voltages, currents, and powers waveforms is achieved. Besides, the three renewable sources were kept in continuous operation without disconnection from the system during these faults.

The industry has widely accepted Frequency Response Analysis (FRA) as a reliable method to detect power transformers mechanical deformations. While the FRA technique has been recommended in recent literature as a potential diagnostic method to detect internal faults within rotating machines, detailed feasibility studies have not been fully addressed yet. This paper investigates the feasibility of using the FRA technique to detect several short circuit faults in the stator winding of three-phase induction motors (TPIMs). In this regard, FRA testing is conducted on two sets of induction motors with various short circuit faults. Investigated faults include short circuits between two phases, short circuit turns within the same phase, phase-to-ground, and phase-to-neutral short circuit. The measured FRA signatures are divided into three frequency ranges: low, medium, and high. Several statistical indicators are employed to quantify the variation between faulty and healthy signatures in each frequency range. Experimental results attest the feasibility of the FRA technique as a diagnostic tool to detect internal faults in rotating machines, such as induction motors.

In this study, an optimization algorithm called chaotic turbulent flow of water-based optimization (CTFWO) algorithm is proposed to find the optimal solution for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a complicated, mixed-integer nonlinear optimization problem, comprising control variables which are discrete and continuous. The CTFWO algorithm is used to minimize voltage deviation (VD) and real power loss (P_loss) for IEEE 30-bus and IEEE 57-bus power systems. These goals can be achieved by obtaining the optimized voltage values of the generator, the transformer tap changing positions, and the reactive compensation. In order to evaluate the ability of the proposed algorithm to obtain ORPD problem solutions, the results of the proposed CTFWO algorithm are compared with different algorithms, including artificial ecosystem-based optimization (AEO), the equilibrium optimizer (EO), the gradient-based optimizer (GBO), and the original turbulent flow of water-based optimization (TFWO) algorithm. These are also compared with the results of the evaluated performance of various methods that are used in many recent papers. The experimental results show that the proposed CTFWO algorithm has superior performance, and is competitive with many state-of-the-art algorithms outlined in some of the recent studies in terms of solution accuracy, convergence rate, and stability.

Optimal power flow (OPF) is one of the most significant electric power network control and management issues. Adding unreliable and intermittent renewable energy sources to the electrical grid increase and complicates the OPF issue, which calls for using modern optimization techniques to solve this issue. This work presents the optimal location and size of some FACTS devices in a hybrid power system containing stochastic wind and traditional thermal power plants considering OPF. The FACTS devices used are thyristor-controlled series compensator (TCSC), thyristor-controlled phase shifter (TCPS), and static var compensator (SVC). This optimal location and size of FACTS devices was determined by introducing a multi-objective function containing reserve costs for overestimation and penalty costs for underestimating intermittent renewable sources besides active power losses. The uncertainty in the wind power output is predicted using Weibull probability density functions. This multi-objective function is optimized using a hybrid technique, gradient-based optimizer (GBO), and moth–flame optimization algorithm (MFO).

With the increase in doubly fed induction generator-based wind energy conversion systems (DFIG-WECS) worldwide, improving the fault ride-through (FRT) capability of the entire system has been given much attention. Enhancement of the FRT capability of a DFIG-WECS is conventionally realized by employing a flexible AC transmission system device with a proper control system. This paper presents a non-conventional method for the improvement of the FRT of DFIG-WECS, using a high-temperature superconducting coil interfaced with the DC-link of the rotor and stator side converters through a DC-chopper. A fractional-order proportional-integral (FOPI) controller is utilized to regulate the DC-chopper duty cycle in order to properly manage the power flow between the DC-link and the coil. Two optimization techniques, Harmony Search and Grey Wolf Optimizer, are employed to determine the optimum size of the superconducting coil along with the optimum parameters of the FOPI controller. The effectiveness of the two proposed optimization techniques is highlighted through comparing their performance with the well-known particle swarm optimization technique.

Real-time estimation of transmission line (TL) parameters is essential for proper management of transmission and distribution networks. These parameters can be used to detect incipient faults within the line and hence avoid any potential consequences. While some attempts can be found in the literature to estimate TL parameters, the presented techniques are either complex or impractical. Moreover, none of the presented techniques published in the literature so far can be implemented in real time. This paper presents a cost-effective technique to estimate TL parameters in real time. The proposed technique employs easily accessible voltage and current data measured at both ends of the line. For simplicity, only one quarter of the measured data is sampled and utilized in a developed objective function that is solved using the whale optimization algorithm (WOA) to estimate the TL parameters. The proposed objective function comprises the sum of square errors of the measured data and the corresponding estimated values. The robustness of the proposed technique is tested on a simple two-bus and the IEEE 14-bus systems. The impact of uncertainties in the measured data including magnitude, phase, and communication delay on the performance of the proposed estimation technique is also investigated. Results reveal the effectiveness of the proposed method that can be implemented in real time to detect any incipient variations in the TL parameters due to abnormal or fault events.

Frequency response analysis (FRA) is a method of monitoring a power transformer’s mechanical integrity. However, identifying the type of fault and its severity by comparing measured responses is still challenging and mostly relies on personnel expertise. This paper is taking one step forward to standardize the FRA interpretation process by proposing guidelines based on various international standards and FRA case studies. In this study, the FRA signature is divided into three regions: low-, mid- and high-frequency regions. The deviation from the fingerprint signature for various faults is classified into small, large, and no variations, based on the calculation of the correlation coefficient. The proposed guidelines are developed based on the frequency regions, and the level of variation is represented using a simple arrow method to simplify the interpretation process. A case study is conducted on a three-phase 11/0.433 kV, 500 kVA distribution transformer with a short circuit winding fault to validate the proposed guidelines.

This paper presents an adaptive PID Load Frequency Control (LFC) for power systems using Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Networks (ANN) oriented by Genetic Algorithm (GA). PID controller parameters are tuned off-line by using GA to minimize integral error square over a wide-range of load variations. The values of PID controller parameters obtained from GA are used to train both ANFIS and ANN. Therefore, the two proposed techniques could, online, tune the PID controller parameters for optimal response at any other load point within the operating range. Testing of the developed techniques shows that the adaptive PID-LFC could preserve optimal performance over the whole loading range. Results signify superiority of ANFIS over ANN in terms of performance measures.