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Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Wind energy is being integrated into the grid as a renewable energy source to meet the world's electricity needs. Grid-connected wind turbines are often disrupted by grid fault problems. Fault ride-through (FRT) ability has become the most important grid connection necessity for wind energy conversion systems (WECS). In the event of a voltage dip fault, the low voltage ride-through (LVRT) capacity is an imperative key to successful grid integration. This paper proposes a dynamic voltage restorer (DVR) controlled through an artificial neural network (ANN) to improve the LVRT capability of a grid-connected wind turbine (WT) based permanent magnet synchronous generator (PMSG). The DVR injects series voltage into the system through a series-connected transformer. The DVR can then restore the voltage to the pre-fault value. The injection transformer is connected to the line linking the PMSG-based wind turbine output to the utility grid. Design and simulation of the low voltage ride-through applied to symmetrical and asymmetrical fault conditions were performed in MATLAB/Simulink software. Simulation results approve that the performance of the technique fully demonstrates its effectiveness and practicality. [ABSTRACT FROM AUTHOR]

In this paper, a novel data-driven control algorithm based on model-free adaptive control is presented, addressing general discrete-time single-input single-output nonlinear systems, approximated by an equivalent dynamic linearization model using pseudo-partial derivatives. The closed loop stability is proved, showing that the tracking error asymptotically vanishes. Moreover, the proposed approach has been applied to a 5 MW wind turbine, considering as control target the efficiency optimization issue when the turbine operates under medium wind speed conditions. Validation of the technique has been performed, testing the overall control system by simulation using the tool FAST developed by the National Renewable Energy Laboratory (NREL). [ABSTRACT FROM AUTHOR]

Off-grid wind energy systems play a pivotal role in providing clean and sustainable power to remote areas. However, the intermittent nature of wind and the absence of grid connectivity pose significant challenges to maintaining consistent power quality. The wind energy conversion system plays a central role in tapping renewable energy from wind sources. Operational parameters such as rotor and stator currents, output voltages of rectifiers and converters, and grid phase voltage variations are crucial for stable power generation and grid integration. Additionally, optimizing power conversion output through voltage gain analysis in boost converters is essential. Moreover, ensuring electricity quality via total harmonic distortion reduction in inverters is vital for grid compatibility. Goal. Enhancing the power quality of grid-integrated wind energy conversion systems. Methods. The proposed topology is implemented in MATLAB/Simulink with optimized control strategies for enhancing power quality in off-grid wind energy systems. Results. Control strategies with a grid-connected wind energy conversion system yields substantial improvements in power quality. This includes effectively mitigating voltage fluctuations and harmonics, resulting in smoother operation and reduced disturbances on the grid. Practical value. The proposed topology has proven to be extremely useful for off grid-integrated wind system. References 18, table 1, figures 11. [ABSTRACT FROM AUTHOR]

In close connection with the issue of global security, the issue of ensuring energy resources has been put on the table of consumers and producers of various forms of energy, researchers, but especially politicians and social planners. Ensuring energy independence was put into question after the onset of the oil crisis in 1973, thus the various energy sources and their associated technologies are gradually being replaced by new technologies that integrate renewable energy sources, capable of taking over at an increased level of efficiency the energy requirement. [ABSTRACT FROM AUTHOR]

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Summary As wind energy is sustainable, pollution‐free, easily available, and free of cost, it has become an efficient source of renewable energy for electricity generation. But, the problem with wind energy is that it varies with time, seasons, and location. This makes the Wind Energy Conversion System (WECS) unstable as it frequently needs to match the load demands. The balance in power generation by wind energy is essential since it has to be connected to various grids. So, this unbalanced energy production can affect the stability of the associated power grids as well. It also results in expensive regulatory measures, storage options, and load shedding. So, the stable operation of the WECS is highly essential to adapt it as a trustable source of electricity production. The stable operation of the WECS requires a robust and advanced system for control. Better control of the wind power extracting model is achieved by controlling the Maximum Power Point Tracking (MPPT) and blade pitch. So, an Adaptive MPPT and Blade Pitch Controller (BPC) for the WECS have been developed in this article, with the support of a hybrid optimization algorithm. In order to enhance the working principles of this controller, two effective algorithms such as Sand Cat Swarm Optimization (SCSO) and Galactic Swarm Optimization (GSO) are integrated and named Hybrid Sand Cat Galactic Swarm Optimization (HSC‐GSO). With the help of the recommended HSC‐GSO, the functionality of the controller is enhanced and also at the same time this algorithm helps to optimize the three gains in the Proportional Integral Differential (PID) controller of both MPPT and BPC, respectively. Moreover, with the support of the proposed HSC‐GSO the damping oscillations in the WECS output power and voltage are minimized. In the end, the numerical analysis is conducted for the presented system by comparing it with the traditional techniques. From the overall result analysis, the stability of the recommended adaptive WECS is 97, which is higher than the conventional algorithms such as DHOA, SCSO, GSO, and DA. Thus, it has been proved that the proposed HSC‐GSO algorithm for the parameters optimization in the PID controller of MPPT and the PID controller of BPC attains high robustness, increased steady‐state stability, and efficient transient response than the traditional techniques. [ABSTRACT FROM AUTHOR]

Recent research generally reports that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hence, modern sustainable energy systems need to implement a stringent power management strategy to achieve the maximum possible green electricity production while reducing costs. Due to the above-mentioned characteristics of sustainable energy sources, power management systems have become increasingly sophisticated nowadays. For addressing the analysis, scheduling, and control problems of future sustainable power systems, conventional model-based methods are completely inefficient as they fail to handle irregular electric power disturbances in renewable energy generations. Consequently, with the advent of smart grids in recent years, power system operators have come to rely on smart metering and advanced sensing devices for collecting more extensive data. This, in turn, facilitates the application of advanced machine learning algorithms, which can ultimately cause the generation of useful information by learning from massive data without assumptions and simplifications in handling the most irregular operating behaviors of the power systems. This paper aims to explore various application objectives of some machine learning algorithms that primarily apply to wind energy conversion systems (WECSs). In addition, an enhanced proportional integral (PI) (2DoF) algorithm is particularly introduced and implemented in a doubly fed induction generator (DFIG)-based WECS to enhance the reliability of power production. The main contribution of this article is to leverage the superior qualities of the PI (2DoF) algorithm for enhanced performance, stability, and robustness of the WECS under uncertainties. Finally, the effectiveness of the study is demonstrated by developing a virtual reality in a MATLAB-Simulink environment. [ABSTRACT FROM AUTHOR]

The widespread integration of wind turbines poses voltage stability challenges to power systems. To enhance the ability of wind power systems to actively support grid voltage, grid-forming control techniques are increasingly being employed. However, current research primarily focuses on voltage stability challenges at the point of common coupling in wind power systems, lacking thorough investigation into system voltage response characterization. This paper establishes the voltage response model of a grid-forming wind power system. Based on this model, mathematical derivation and theoretical analysis are conducted, and the effect factors of the voltage at the point of common coupling are investigated. Furthermore, a voltage stabilization method is explored by adjusting the above effect factors. Finally, based on the MATLAB/Simulink platform, the simulation verification of each effect factor is carried out. The results indicate that voltage response characterization obtained by the theoretical analysis and simulation is similar and that the proposed method is valid. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid type-2 fuzzy sliding mode control approach for regulating active and reactive power exchanged with the utility grid by a doubly-fed induction generator in a wind energy conversion system. The main objective of this hybridization is to eliminate the steady-state chattering phenomenon inherent in sliding mode control while improving the transient delays caused by type-2 fuzzy controllers. In addition, the proposed control approach has proven to be successful in coping with varying generator parameters and exhibited good reference tracking. An in-depth comparative study with state-of-the-art advanced control techniques is also the focus of the present paper. The comparative study has three objectives, namely: a qualitative comparative study that aims to compare response times and reference tracking capabilities; a quantitative evaluation that takes into account time-integrated performance criteria; and finally, robustness capabilities. The simulation results, carried out in the Matlab/Simulink environment, have demonstrated the effectiveness and best performance of the proposed hybrid type-2 fuzzy sliding mode control with respect to other advanced techniques included in the comparison study. [ABSTRACT FROM AUTHOR]

This study presents the coupling-memory-sampled data control (CMSDC) design for the Takagi–Sugeno (T-S) fuzzy system that solves the stabilization issue of a surface-mounted permanent-magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). A fuzzy CMSDC scheme that includes the sampled data control (SDC) and memory-sampled data control (MSDC) is designed by employing a Bernoulli distribution order. Meanwhile, the membership-function-dependent (MFD) H ∞ performance index is presented, mitigating the continuous-time fuzzy system's disturbances. Then, by using the Lyapunov–Krasovskii functional with the MFD H ∞ performance index, the data of the sampling pattern, and a constant signal transmission delay, sufficient conditions are derived. These sufficient conditions are linear matrix inequalities (LMIs), ensuring the global asymptotic stability of a PMSG-based WECS under the designed control technique. The proposed method is demonstrated by a numerical simulation implemented on the PMSG-based WECS. Finally, Rossler's system demonstrates the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work. [ABSTRACT FROM AUTHOR]

Integrated gate commutated thyristors (IGCTs) are critical components in high-voltage, high-current, and high-power conversion systems, particularly in offshore wind energy systems. However, the working environment of offshore wind energy conversion systems is extremely harsh. In this article, we propose an active damage control approach aiming at enhancing the reliability of the conversion system. By employing electro-thermal modeling for the equipment of the offshore wind energy conversion system, the junction temperature and fatigue damage of IGCT are simulated during the operation process. Using the improved model predictive current control (MPCC) method, active damage control effectively regulates the switching frequency of IGCT. IGCTs are symmetrically distributed on each leg of the converter, so the lifespan of the two IGCTs on each leg is also considered to be similar. This method balances the life of the IGCTs on the three legs of the converter and optimizes their utilization to the maximum extent. These measures effectively enhance the reliability of the conversion system and lower the operation and maintenance cost of high-power IGCT converters. The effectiveness of the proposed method is validated by co-simulation results by ANSYS and MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Summary: This study addresses a significant research gap in the field of wind energy, focusing on the underutilized potential of synchronous reluctance generators (SynRG) in wind power conversion systems (WPCS). Despite notable advancements in wind energy controls, the capabilities of SynRG within these systems have not been fully explored. In response, we introduce an innovative model‐free control (MFC) approach, termed intelligent proportional (iP), specifically designed and optimized for WPCS equipped with SynRG. This research encompasses the modeling, control, and simulation of SynRG‐based WPCS, aiming to optimize system performance and enhance the quality of grid power supply. The proposed vector control (VC) method utilizes a MFC approach (VC‐iP) to address the limitations inherent in traditional VC methods with proportional‐integral (PI) controllers (VC‐PI). Although widely used for their simplicity, VC‐PI methods often lead to power fluctuations and decreased power and current quality, consequently reducing overall system efficiency. In contrast, VC‐iP markedly enhances power quality and system efficiency, especially in situations involving fluctuating power demand and variable wind speeds. The efficacy of VC‐iP is demonstrated through simulations in two distinct test scenarios that include variations in power demand and random wind speed fluctuations. The results affirm the superiority of VC‐iP in maintaining stable, high‐quality power output from WPCS. This method notably minimizes fluctuations, improves current quality, reduces harmonic distortion and steady‐state error, ensures reliable grid integration, and contributes to a more efficient and sustainable energy system. [ABSTRACT FROM AUTHOR]

Currently, both regulated and deregulated power trading exist in China’s power system, which has caused imbalanced funds in the electricity market. In this paper, a simulation analysis of the electricity market with wind energy resources is conducted, and the calculation methods of unbalanced funds are investigated systematically. In detail, the calculation formulas of unbalanced funds are illustrated based on their definition, and a two-track electricity market clearing model is established. Firstly, the concept of the dual-track system is explained, and the specific calculation formulas of various types of unbalanced funds are provided. Next, considering the renewable energy consumption, the market clearing model based on DC power flow is constructed and solved; by combining fitting methods of mid- and long-term curves, the unbalanced funds are calculated based on clearing results and formulas. [ABSTRACT FROM AUTHOR]

Introduction. The integration of Renewable Energy Sources (RESs), particularly Wind Energy Conversion Systems (WECS), is vital for reducing reliance on fossil fuels and addressing climate change. However, this transition poses challenges, including ensuring grid stability in the face of intermittent RESs. Compliance with grid codes is crucial, with a focus on Low Voltage Ride Through (LVRT) capability. Problem. The intermittent nature of RESs, specifically in Permanent Magnet Synchronous Generator (PMSG) based WECS, presents challenges to grid stability during voltage dips. Goal. To enhance voltage stability and LVRT capability in PMSG-based WECS by integrating a Dynamic Voltage Restorer (DVR) with an energy storage device. This involves regulating the input DC voltage to the DVR using a type 2 fuzzy controller, adapting intelligently to changing conditions. Methodology. DVR, powered by an energy storage device, is strategically integrated with WECS. A type 2 fuzzy controller regulates the DC voltage to DVR. The rectified WECS output undergoes processing through an isolated flyback converter. A 31-level Cascaded H-Bridge Multilevel Inverter (CHBMLI) with PI control ensures high-quality AC output. Results. The validation of developed system is executed using MATLAB/Simulink revealing a reduced Total Harmonic Distortion (THD) value of 1.8 %, ensuring significance in LVRT capability. Originality. The strategic integration of DVR with PMSG-based WECS, addresses the LVRT challenges. The use of type 2 fuzzy controller for intelligent voltage regulation and a sophisticated multilevel inverter contributes to the uniqueness of proposed solution. Practical value. The developed system provides benefits by ensuring reliable LVRT capability in PMSG-based WECS with reduced THD of 1.8 % indicating improved grid compatibility. References 26, tables 5, figures 20.

Offshore wind farms (OWFs) play a crucial role in producing renewable energy in modern electrical power systems. However, to ensure that these facilities operate smoothly, they require robust control systems. As a result, this paper employed the newly developed Walrus Optimization algorithm (WaOA) to optimize the design parameters of fractional-order proportional-integral-derivative (FOPID) controllers in the power electronic interface circuits of the studied wind energy conversion system (WECS). In contrast to conventional optimization techniques like GA and PSO, the suggested approach proves more effective. The paper validates the WaOA application in optimizing FOPID controllers within a WECS comprising two, onshore and offshore, VSC stations at the two ends of an HVDC transmission system connecting OWFs to the mainland. The study shows that the WaOA outperforms GA and PSO, improving system stability and enabling quick recovery after disturbances. The study carried out using MATLAB/Simulink highlights the significance of newly recently introduced optimization techniques to ensure efficient and reliable operation of offshore wind energy systems, thereby expediting the transition to sustainable energy sources. [ABSTRACT FROM AUTHOR]

In this paper, as the main contribution, three sensorless control structures are presented for the control of the grid-connected PMSG-based Wind Turbine (WT) employing boost converter and diode rectiffer as the machine-side converter. Then, detailed control structures of the boost converter and grid-side converter at the three mentioned control strategies are extracted, and next, features of the abovementioned control strategies are investigated and compared against wind speed variation and grid voltage dip. The boost converter, in the first control strategy, controls the generator speed at the Maximum Power Point Tracking (MPPT) mode, and in the second control strategy, it regulates the Permanent Magnet Synchronous Generator (PMSG) active power to its set point value. Also, the boost converter, in the third control strategy, adjusts the voltage of the dc-link capacitor to its set point value. In this paper, steady-state performance and transient/LVRT behavior of the WT system are examined for each mentioned control strategy in the MATLAB-Simulink environment. It is shown that WT steady-state responses are relatively identical for all three control strategies. However, as an interesting result, at the fault conditions, the third control strategy has superior performance, and thus, the WT fault ride-through behavior enhances significantly with the third control structure without any hardware protection. [ABSTRACT FROM AUTHOR]

This article presents a robust finite control set predictive scheme for a stand-alone squirrel cage induction generator (SCIG) drive. This technique is considered an alternative to the drive system due to the inclusion of system nonlinearities and fast dynamic response. The control objective in the distributed generation environment is to fix the output voltage to follow the stand-alone requirement. The strategy establishes optimized switching instants for cost function minimization for both source and load converter control and diminished cross-coupling amid active and reactive power during transient scenarios. The scheme is designed to achieve the minimal effect caused by the parameter uncertainties. During source and load changes, this work will also address the maintenance of dc-link voltage, machine, and load variables at the set value, supported by machine and load-end converter control to achieve stand-alone load objectives. In addition, the presented scheme is also tested with random variation of speed to check the efficacy of the control configuration. The drive performance is evaluated by simulation using MATLAB/Simulink environment. Comprehensive real-time findings obtained from a scaled laboratory test bench using dSPACE-1104 are provided to verify the feasibility of the predictive solution. • Distinct design approach of predictive control for SCIG based drive system feeding stand-alone load. • The developed generator side converter strategy controls the active power flow from the SCIG by regulating the dc-link voltage. • The performance of the control scheme is found to be much superior under load transients, and input power variation. • The uncertainty in the values of the machine parameters is also tested and system offer better dynamic performance. • Demonstration of MATLAB Simulation and, then validation with Real-Time results. [ABSTRACT FROM AUTHOR]

Due to their numerous advantages, wind turbines with changeable speed power production systems are increasingly being used. This study proposes the architecture and specifications of an induction turbine-generating wind system, as well as a PI controller-based DC voltage link regulator and optimal torque control system. The suggested control technique as stated in this paper allows the system to function in grid-linked mode as well as the islanded mode and also in parallel with other power generation units, loads, FACTS devices, etc. This paper involves the analysis of different test cases in grid-connected mode as well as an islanded mode with a PV generation unit, passive loads, induction motor loads, and other wind energy generation systems. The paper also includes an analysis of the response of the system with the switching on and off of the other units. MATLAB 2013a thereby validates the system findings and analysis. [ABSTRACT FROM AUTHOR]

Today's environmental concerns, particularly those related to global warming, have sparked a drive for the usage of renewable energy sources. One of the most significant sources of renewable energy is wind energy and wind energy conversion system are preferred to harvest wind energy. Due to the growing sophistication of wind energy conversion systems, new strategies based on advanced analytics are needed. In this study, reinforcement learning implemented in wind energy has been reviewed, the most popular approaches for various applications are identified, and it has been shown that reinforcement learning may be utilized in place of traditional approaches. According to the application, the techniques are examined and divided into four groups: optimal control, prediction and forecasting, optimization, and other techniques. Consequently, many literature has reported that, on an average, reinforcement learning has improved performance by 5% to 20% than existing methods. Moreover, around 85% of the 153 references included in this article were published after 2018. The purpose of the work is to provide a basis for future research on reinforcement learning applied in wind energy that may be crucial to energy sustainability. The report also addresses the discussion on the reinforcement learning current state, limitations, and future scope. [ABSTRACT FROM AUTHOR]

This article proposes the use of a deep reinforcement learning method—and precisely a variant of the deep deterministic policy gradient (DDPG) method known as twin delayed DDPG, or TD3—for maximum power point tracking in wind energy conversion systems that use permanent magnet synchronous generators (PMSGs). An overview of the TD3 algorithm is provided, together with a detailed description of its implementation and training for the considered application. Simulation results are provided, also including a comparison with a model‐based control method based on feedback linearization and linear‐quadratic regulation. The proposed TD3‐based controller achieves a satisfactory control performance and is more robust to PMSG parameter variations as compared to the presented model‐based method. To the best of the authors' knowledge, this article presents for the first time an approach for generating both speed and current control loops using DRL for wind energy conversion systems. [ABSTRACT FROM AUTHOR]

Introduction. The integration of Renewable Energy Sources (RESs), particularly Wind Energy Conversion Systems (WECS), is vital for reducing reliance on fossil fuels and addressing climate change. However, this transition poses challenges, including ensuring grid stability in the face of intermittent RESs. Compliance with grid codes is crucial, with a focus on Low Voltage Ride Through (LVRT) capability. Problem. The intermittent nature of RESs, specifically in Permanent Magnet Synchronous Generator (PMSG) based WECS, presents challenges to grid stability during voltage dips. Goal. To enhance voltage stability and LVRT capability in PMSG-based WECS by integrating a Dynamic Voltage Restorer (DVR) with an energy storage device. This involves regulating the input DC voltage to the DVR using a type 2 fuzzy controller, adapting intelligently to changing conditions. Methodology. DVR, powered by an energy storage device, is strategically integrated with WECS. A type 2 fuzzy controller regulates the DC voltage to DVR. The rectified WECS output undergoes processing through an isolated flyback converter. A 31-level Cascaded H-Bridge Multilevel Inverter (CHBMLI) with PI control ensures high-quality AC output. Results. The validation of developed system is executed using MATLAB/Simulink revealing a reduced Total Harmonic Distortion (THD) value of 1.8 %, ensuring significance in LVRT capability. Originality. The strategic integration of DVR with PMSG-based WECS, addresses the LVRT challenges. The use of type 2 fuzzy controller for intelligent voltage regulation and a sophisticated multilevel inverter contributes to the uniqueness of proposed solution. Practical value. The developed system provides benefits by ensuring reliable LVRT capability in PMSG-based WECS with reduced THD of 1.8 % indicating improved grid compatibility. References 26, tables 5, figures 20. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

This article compares traditional and intelligent Maximum Power Point Tracking (MPPT) algorithms for photovoltaic (PV) powered DC Shunt Motors. The study evaluates five MPPT methods under varying DC loads and finds that intelligent algorithms perform well under rapidly changing solar radiation conditions. However, they struggle to track the maximum power point when the temperature changes due to inadequate data for training. The analysis focuses on the time required by each method to reach peak power under different conditions, and the advantages and disadvantages of each algorithm are discussed. The article provides valuable insights into the behavior of MPPT algorithms in real-world scenarios and aims to contribute to the advancement of PV system optimization and control. [Extracted from the article]

This paper proposes a jellyfish search algorithm (JSA)-based offline method to design a DC voltage controller of AC/DC front-end converter (FEC) in a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). First, a multi-objective tuning problem is formulated using a full-order converter model to achieve maximum utilization of converter parameters in controller design. Next, a normalized mono-objective problem is developed using a utopia-tracking approach to reduce the computational complexity in design. Finally, the mono-objective problem is solved using JSA to enhance the controller performance by obtaining optimal gain values for the controller. A comparative study is performed with other metaheuristic optimization techniques to justify the use of JSA in this application. The results confirm that the accuracy of the JSA-based approach is higher, whereas the execution time of the method is lower than the other methods. Furthermore, to validate the performance of the proposed approach, a DFIG-based WECS is modeled and simulated using OPAL-RT real-time simulator. A grid voltage-oriented vector control is used as the overall control algorithm for the FEC. Unlike the conventional pole-zero cancellation method, the proposed approach provides no voltage overshoot during voltage development. Compared to the published literature, it also ensures 40.65% lesser voltage deviation without any overcurrent, irrespective of wind speed and load variations. [ABSTRACT FROM AUTHOR]

This research paper presents a new approach to address power quality concerns in microgrids (MGs) by employing a superconducting fault current limiter (SFCL) and a fuzzy-based inverter. The integration of multiple power electronics converters in a microgrid typically increases total harmonic distortion (THD), which in turn results in power quality issues. Moreover, the intrinsic variability of solar and wind power sources gives rise to fluctuations in power generation, which consequently leads to power oscillations and voltage sag within microgrids that rely on renewable energy. This research paper introduces a technical approach to achieve power balance in renewable-based microgrids (MGs) by utilizing a fuzzy logic-controlled (FLC) pulse width modulation (PWM) inverter. The Microgrid (MG) consists of a hybrid photovoltaic (PV) system and a wind energy conversion system (WECS) that utilizes a permanent magnet synchronous generator (PMSG). The system employs an optimal torque-controlled maximum power point technique (MPPT) algorithm to optimize power output. The battery energy storage system (BESS) is employed to facilitate power provision during critical scenarios or to ensure a stable power output for fluctuating loads. The SFCL is utilized as a device for voltage compensation in cases of voltage sag incidents. The effectiveness of the suggested control strategy is assessed by performing a comparative analysis of total harmonic distortion (THD) in the load voltage, with and without the utilization of a SFCL. The results suggest that the incorporation of an FLC inverter-SFCL-BESS system effectively mitigates THD and voltage sag, according to the requirements set by the IEEE 519 standards. The research study is carried out by using the MATLAB/SIMULINK software environment. [ABSTRACT FROM AUTHOR]

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS. [ABSTRACT FROM AUTHOR]

The occurrence of Sub-Synchronous Resonance (SSR) phenomena can be attributed to the interaction that takes place between wind turbine generators and series-compensated transmission lines. The Doubly-Fed Induction Generator (DFIG) is widely recognized as a prevalent generator form employed in wind energy conversion systems. The present paper commences with an extensive exposition on modal analysis techniques employed in a series of compensated wind farms featuring Doubly Fed Induction Generators (DFIGs). The system model encompasses various components, including the aerodynamics of a wind turbine, an induction generator characterized by a sixth-order model, a secondorder two-mass shaft system, a series compensated transmission line described by a fourth-order model, controllers for the Rotor-Side Converter (RSC) and the Grid-Side Converter (GSC) represented by an eighth-order model, and a first-order DC-link model. The technique of eigenvalue-based SSR analysis is extensively utilized in various academic and research domains. The eigenvalue technique depends on the initial conditions of state variables to yield an accurate outcome. The non-iterative approach, previously employed for the computation of initial values of the state variables, has exhibited issues with convergence, lack of accuracy, and excessive computational time. The comparative study evaluates the time-domain simulation outcomes under different wind speeds and compensation levels, along side the eigenvalue analysis conducted using both the suggested and non-iterative methods. This comparative analysis is conducted to illustrate the proposed approach efficacy and precision. The results indicate that the eigenvalue analysis conducted using the proposed technique exhibits more accuracy, as it aligns with the findings of the simulations across all of the investigated instances. The process of validation is executed with the MATLAB program. Within the context of the investigation, it has been found that increasing compensation levels while simultaneously decreasing wind speed leads to system instability. Therefore, modifying the compensation level by the current wind speed is advisable. [ABSTRACT FROM AUTHOR]

The electrification of remote islands has long been a subject of research interest, primarily because of their historical reliance on fossil fuels, leading to a significant carbon footprint. Recent advancements in wave energy converters offer a promising avenue to make these islands more self‐sustainable while considerably reducing carbon emissions. However, the persistent issue of voltage dips due to weaker grids continues to pose a challenge. This study introduces a multi‐mode converter control strategy with the goal of electrifying remote islands, employing a linear generator‐based wave energy converter in a unified electrical model. Various scenarios, including voltage dips and mainland grid disconnection, are simulated using MATLAB/Simulink. The study demonstrates the converter's ability to transition swiftly and smoothly in response to these scenarios, ensuring an uninterrupted power supply. Furthermore, the analysis indicates that the power quality at the point of common coupling remains well within acceptable standards. [ABSTRACT FROM AUTHOR]

In this work, an optimal control scheme based on the Maximum Power Point tracking for the Wind Energy Conversion System using Permanent Magnet Synchronous Generator (PMSG) is proposed and also modeled. The system studied in this paper consists of wind energy system powering a battery using a buck-boost converter as an interface. By modifying the buck-boost duty cycle, we vary the reflected voltage at PMSG, and accordingly its speed. It is controlled with Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) approach. The overall system is simulated with MATLAB/SIMULINK. [ABSTRACT FROM AUTHOR]

Wind power systems, which are currently being constructed for the electricity worldwide market, are mostly based on Doubly Fed Induction Generators (DFIGs). To control such systems, multilevel converters are increasingly preferred due to the well-known benefits they provide. This paper deals with the control of a standalone DFIG-based Wind Energy Conversion System (WECS) by using a three-level Neutral-Point-Clamped (NPC) converter. The frequency and magnitude of the stator output voltage of the DFIG are controlled and fixed at nominal values despite the variable rotor speed, ensuring a continuous AC supply for three-phase loads. This task is achieved by controlling the DFIG rotor currents via a PI controller combined with a new Simplified Direct Space Vector Modulation strategy (SDSVM), which is applied to the three-level NPC converter. This strategy is based on the use of a line-to-line three-level converter space vector diagram without using Park transformation and then simplifying it to that of a two-level converter. The performance of the proposed SDSVM technique in terms of controlling the three-level NPC-converter-based standalone WECS is demonstrated through simulation results. The whole WECS control and the SDSVM strategy are implemented on a dSPACE DS 1104 board that drives a DFIG-based wind system test bench. The obtained experimental results confirm the validity and performance in terms of control. [ABSTRACT FROM AUTHOR]

The focus of this study is on modeling and management of a Wind Energy Conversion System (WECS) based on a Hybrid Excitation Synchronous Generator (HESG). Using a wind simulator, two controllers, CRONE and H∞, are evaluated for Maximum Power Point Tracking (MPPT) and optimal rotation speed. The results show the CRONE controller's higher tracking capability, and robustness tests investigate the influence of parametric uncertainty. Space harmonic effects, commutation effects, and turbine shaft flexibility are all addressed in the sophisticated models. The introduction of Fractional Order Proportional‐Integral (FOPI) control for MPPT is a game changer. Although fractional calculus‐based accuracy and robustness are uncommon in WECS, they show promise for emissions reduction and increased energy efficiency. This work validates a dependable control approach inside an isolated HESG‐based WECS's power‐maximizing range. Extensive investigation of the parameters impacting FOPI control efficiency yields useful insights for effective MPPT control. A variable‐speed wind turbine (WT) is linked to a HESG through a multiplier, with a controller controlling generator coil excitation voltages and a rectifier connecting the WECS to a load. A thorough 3 kW HESG electrical model that includes generator space harmonics and converter commutation effects is among the contributions. For HESG‐based WECS stability, frequency analysis finds important resonant and anti‐resonant frequencies. These issues are addressed by a FOPI control technique, which ensures system stability and performance. The necessity of exact frequency analysis in HESG‐based WECS is highlighted by a step‐by‐step controller parameter tuning approach. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative algorithm-based control model for improving the doubly fed induction generator (DFIG) system’s low-voltage ride-through (LVRT) capabilities. The Mountain Gazelle optimization (MGO) algorithm is combined with an adaptive neuro-fuzzy inference system (ANFIS)-based proportional-integral (PI) regulator, called the MGOANFIS-PI approach. In the proposed method, the data set of an MGO-based PI regulator is used after rectification to train the MGOANFIS controller, so updating the ANFIS technique improves MGO’s searching behavior. The implemented control method ensures LVRT capability on grid-tied wind conversion plants based on DFIG in case of a malfunction or a voltage drop. Multiple metrics relating to LVRT are monitored in the proposed system, comprising current, active, and reactive power and voltages. The objective to be achieved, which is resolved through the cascaded MGOANFIS-based PI scheme, is defined. Based on the collected data, ANFIS performs and anticipates the optimal control signal from converters on the rotor and network sides. The MGOANFIS-PI methodology effectively handles system performance and voltage instability during four fault circumstances. The LVRT functionality of the DFIG system and the power quality are enhanced with the assistance of the proposed methodology. Simulations are carried out using MATLAB/Simulink framework. Two test systems are examined: the IEEE 39 bus and the 9 MW wind power plant (WPP) test systems. The WPP’s ability to withstand grid voltage sags is analyzed as part of an effectiveness assessment. The relevance and efficacy of the proposed MGOANFIS-PI method are evaluated, and proved the better performance of the cascaded MGO-based ANFIS-PI controller when compared to PI controllers optimized by comparing it with other well-known optimization techniques, i.e., Gorilla troop optimizer, particle swarm optimization, and genetic algorithm. In the three-phase IEEE 39 bus system and the 9 MW test scheme, detailed wind power plants could persevere in the face of every fault condition.

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities. [ABSTRACT FROM AUTHOR]

Rotor angle stability, which involves electromechanical oscillation damping and control, is very important in maintaining the stability of modern power grid systems. Renewable energy sources like wind energy are undergoing massive integration into modern power grid systems to meet energy demands and decarbonize power grid systems of carbon emissions from fossil fuel generators. To enable increased integration of wind renewable energy sources, precise models are needed for research and analytical purposes. Wind renewable energy is generated through a wind energy conversion system (WECS); one such conversion system is the doubly fed induction generator (DFIG) system. In this study, a precise model of a DFIG-WECS was modeled and integrated into the IEEE's two-area Kundur power test system, which represents the available power grid system, and is also a multimachine power system using the Matlab/Simulink 2023 software. A damping controller known as the power system stabilizer (PSS), whose optimal parameters were obtained using artificial eco-system optimization (AEO), was also incorporated into the integrated power grid system to control and damp electromechanical oscillations. The results showed that the PSS damping controller effectively damped electromechanical oscillations in the integrated power grid system. [ABSTRACT FROM AUTHOR]

In this paper, a holistic nonlinear state-space model of a system with multiple converters is developed, where the converters correspond to the wind turbines in a wind farm and are equipped with grid-following control. A novel generalized methodology is developed, based on the number of the system's converters, to compute the equilibrium points around which the model is linearized. This is a more solid approach compared with selecting operating points for linearizing the model or utilizing EMT simulation tools to estimate the system's steady state. The dynamics of both the inner and outer control loops of the power converters are included, as well as the dynamics of the electrical elements of the system and the digital time delay, in order to study the dynamic issues in both high- and low-frequency ranges. The system's stability is assessed through an eigenvalue-based stability analysis. A participation factor analysis is also used to give an insight into the interactions caused by the control topology of the converters. Time domain simulations and the corresponding frequency analysis are performed in order to validate the model for all the control interactions under study. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective H2/H ∞ maximum power tracking control of a variable speed wind turbine to minimize the H2 tracking error and ensure the H ∞ model reference-tracking performance, simultaneously. The optimal condition is obtained via a boost converter use, which adapts the load impedance to the wind turbine generator. Thus, based on the fuzzy T-S model, a multi-objective Maximum Power Point Tracking (MPPT) controller is developed, ensuring maximum power transfer, despite wind speed variation and system uncertainty. To specify the optimal trajectory to follow, a TS reference model is proposed taking as input the optimal rectified DC current. The conditions of stability and stabilization are expressed in terms of linear matrix inequality (LMI) for uncertain and disturbed T-S models leading to determining the controller gains. Finally, an example of MPP tracking applied to a Wind Energy Conversion System (WECS) illustrates the effectiveness of the proposed fuzzy control law. [ABSTRACT FROM AUTHOR]

This paper investigates the development of a novel analytic approach for computing Unity Magnitude (UM) shapers that deviates from established numerical methodologies. The experimental validation on a test bench confirms the practicality and benefits of the suggested UM shaper technique. The study extends the use of UM shapers to improve the control of wind conversion systems (WCSs), particularly those including hybrid excitation synchronous generators (HESGs), demonstrating their adaptability and versatility. Experimental validation guarantees real-world application, confirming the suggested UM shapers' trustworthiness. Strict management is still required to assure the system's efficiency and dependability. In reality, the dynamic equations of a turbine, as well as those of an HESG, are substantially nonlinear; most system parameters are very uncertain; and, finally, a WCS is always impacted by disturbance sources such as load variations, harmonics, and mechanical vibrations. Robust control measures must be used to overcome these issues. A CRONE controller (Robust Fractional Order Control) of the second generation is created. A comparative study performed on the Simulink platform reveals substantial gains brought about by UM shapers in real-world circumstances. The study demonstrates the effectiveness of UM-shaped inputs in mechanical stabilization and Maximum Power Point Tracking (MPPT), emphasizing both theoretical soundness and practical advantages. The analytic equations for UM shapers in undamped and damped systems, offered together with a real-time algorithm, contribute to the optimization of wind conversion systems. [ABSTRACT FROM AUTHOR]

This paper presents the control strategies and performance analysis of doubly fed induction generator (DFIG) for grid-connected wind energy conversion system (WECS). The wind power produces environmentally sustainable electricity and helps to meet national energy demand as the amounts of non-renewable resources are declining. The development of the WECS can be seen with development in power electronics technology. Currently, two types of major generator used in WECS these are DFIG and the permanent magnet synchronous generator (PMSG). Both variable oprational speed are used in wind turbine. Because its high performance, reliability, controllability and high-power and voltage capabilities, multilevel converters have become a popular option for multi-MW WECSs. This paper focuses on the application of back-to-back modular multilevel converters in a DFIG and PMSG-based WECS. A comparative performance analysis of the two systems based on power quality, active, reactive power, and modular multilevel converter dynamics is presented. The comparison is based on simulation results created in MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

Hybrid Energy Systems (HESs) are offering several advantages over conventional generation systems because of the rising need for Renewable Energy Resources (RERs) and its high capability and flexibility of achieving the desired performance for both urban and rural areas. The main goals of HESs are to improve the system performance by increasing its flexibility, reliability and reducing generation instability. In the current work, a hybrid Photovoltaic/Wind Energy Conversion System (PV /WECS) is proposed to supply and support the utility grid. The generated power from the system is greatly responding to the change in both the wind speed and PV irradiance level, and as a result impacting the DC-Bus voltage of the system converters. Therefore, precise, fast and optimum controllers are required for the PV/WECS system to ensure the extraction and generation of stable maximum power during weather variations along with maintaining the DC-Link voltage at the desired value. Further, this study proposes a novel Linear Active Disturbance Rejection Control (LADRC) combined with a hybrid Salp Particle Swarm Algorithm (SPSA) to a grid-connected PV/WECS for extracting the Global Maximum Power (GMP), regulating the DC-Link voltage, and enhancing the active and reactive powers control. The capability of the proposed SPSA-LADRC based-MPPT approach is examined according to the tracking time, accuracy and response to internal and external disturbances and then comparing its performance with other techniques such as SPSA-SMC, SPSA-PID and SPSA-PI controllers respectively. The findings revealed that the LADRC can track and keep the DC-Link voltage as the desired reference value,1500 V, respond much faster than the PI, SMC, and PID controllers as well as keep the THD of the injected current into the grid at a low level of 2.25%. Finally, the performance of the proposed system is experimentally verified using Hardware-In-Loop (HIL). The obtained findings verify the capability and accuracy of the system and its great potential in the management of HESs. • A novel hybrid SPSA based LADRC is designed for grid-connected PV/WECS. • The SPSA-LADRC performance was investigated under wind speed and irradiance variation. • It regulates the DC-Link voltage, improves GP tracking speed and dynamic performance. • The Hardware-In-Loop experiment validates the implementation feasibility. [ABSTRACT FROM AUTHOR]

In this study, a method for fault detection of a permanent magnet synchronous generator (PMSG) wind turbine is proposed. The dynamic model of the wind energy conversion system is simulated and the system's fault is diagnosed using a proposed terminal sliding mode observer (TSMO). The nonlinear state-space equations of the closed-loop wind turbine are derived, which include a robust adaptive controller that is implemented for maximum power capture and a PI controller that is designed for pitch angle control. Then, utilising a new approach, a TSMO is designed for estimating the states and faults in a finite time. The stability and convergence of the states and fault estimation error are investigated via the Lyapunov stability theory. A $ 3kw $ 3 kw PMSG wind turbine is simulated and using TSMO the states are estimated and a blade imbalance fault (BIF) fault is detected. The state and fault estimation errors converge to zero in a finite time. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate a wind power system utilizing a doubly fed induction generator (DFIG). In this configuration, the stator is directly connected to the grid, while the rotor is linked to the grid via a back-to-back converter. The primary aim is to develop a decoupled control system for the DFIG to improve power quality. To achieve this goal, we introduce a robust control technique as a means to control the reactive and active power of DFIG. This technique is known as sliding mode control. Furthermore, we propose a model-reference adaptive system estimator based on proportional and integral controllers (PI-MRAS) for sensor-less control of DFIG. This estimator is designed to accurately approximate the rotor resistance. The proposed control strategies enhance the performance of the wind energy conversion system, particularly considering variations in the machine's parameters. Simulation results demonstrate the high performance and robustness of control strategies. [ABSTRACT FROM AUTHOR]

The prominent trend in wind turbine technology centers on the adoption of direct-drive permanent magnet synchronous generators (DD-PMSG), a choice driven by their capacity to deliver superior efficiency through the elimination of gearboxes.This paper presents a comprehensive exploration of the design, modeling, and control aspects of a DD-PMSG intended for harnessing wind energy conversion. Initially, the geometrical design of the generator is meticulously carried out, adhering to predefined technical specifications and constraints. Subsequently, an in-depth internal modeling, focusing on the electromagnetic behavior of the designed generator, is executed using finite element analysis (FEA) through the Ansys Maxwell RMXpert software. Finally, the external modeling and control system integration of the designed generator, connected to the grid through power electronic converters, are simulated using the Matlab/Simulink software suite. The resulting findings underscore the efficacy and viability of the proposed generator for wind turbine applications, affirming its potential to enhance wind energy conversion systems. [ABSTRACT FROM AUTHOR]

Summary: Over the past few decades, global demand for renewable energy has been rising steadily. To meet this demand, there has been an exponential growth in size of wind turbines (WTs) to capture more energy from wind. Consequent increase in weight and flexibility of WT components has led to increased structural loading, affecting reliability of these wind energy conversion systems. Spatio‐temporal variation of rotor effective wind field acts as a disturbance to a WT system, hence, necessitating controllers that can cancel this disturbance. Additionally, assumptions made in extracting linear models for controller design lead to modeling errors resulting from changing operating conditions. Previous attempts have proposed robust controllers incorporating wind disturbance models. However, these controllers have been evaluated on smaller WTs, which experience lower structural loading than larger ones. Additionally, a majority these controllers are based on collective pitch control (CPC), hence do not address loading in the blades. To address these challenges, this contribution proposes an independent pitch‐based robust disturbance accommodating controller (IPC‐RDAC) for reducing structural loads and regulating generator speed in utility‐scale WTs. The proposed controller is designed using μ‐synthesis approach and is evaluated on the 5 MW National Renewable Energy Laboratory (NREL) reference WT. Its performance is evaluated against a gain‐scheduled proportional integral (GSPI)‐based reference open‐source controller (ROSCO) and a CPC‐based RDAC (CPC‐RDAC) controller, developed previously by the authors. Simulation results for various wind conditions show that the proposed controller offers improved performance in blade and tower load mitigation, as well a generator speed regulation. [ABSTRACT FROM AUTHOR]

In order to improve the quality of energy and reduce the harmonics produced by the power electronics converters, it is proposed and developed in this article the direct torque control, in which the flux and torque are estimated from the only measurable electrical quantities. The direct torque control DTC method, to enhance the dynamic and static performances as well as the robustness of the control of the Wind Energy Conversion System (DSIG). DTC is a control technique that exploits the possibility of imposing torque and flux on alternating current machines in a decoupled manner, once powered by a voltage inverter without current regulation made by a feedback loop, ensuring a decoupling, similar to that obtained from a vector control. The technique involved rapid torque response, insensitivity to parametric variation, in particular the machine's rotor time constant and systematic suitability for control without speed sensor. The main function of the generator side controller is to track the maximum power through controlling the rotational speed of the wind turbine using PI controller. The performance and the effectiveness of the proposed control system are tested via simulation results in terms of reference tracking, and robustness against parameters variations of the DSIG. Simulation results for 1.5 MW DSIG control show robust with respect to the parametric variation 2 Rs, 1,5 Rs et 0.5 Rs, and fast dynamic behavior of system, with the temps of response is 0.02 s, active power extracted 0.15 MW with lambda 9 and Cp 0,5 that the wind turbine can operate at its optimum power point for a wide range of wind speed and power quality can be greatly improved. [ABSTRACT FROM AUTHOR]