Variable Speed Wind Turbine Research Articles

MPPT control for PMSG based wind turbine: Adaptive dynamic programming approach

This paper proposes an online adaptive dynamic programming-based controller for the variable-speed wind turbine system, ensuring it tracks the maximum power point under uncertain conditions. This control scheme is composed of two components: the adaptive optimal control component and the high-order disturbance observer-based adaptive sliding mode control component. The adaptive optimal control part is designed using the online adaptive dynamic programming technique, which is one of the algorithms of the reinforcement learning method. This control component has the role of achieving the optimal characteristics for the nonlinear system. Likewise, the high-order disturbance observer is established to estimate system uncertainties and external disturbances. These approximated disturbances are then fed to the adaptive sliding mode control component for system disturbance compensation. As a result, the system, which is affected by the unknown disturbances, achieves robust optimal performance. In addition, the convergence of the adaptive laws and the stability of the overall system are ensured through the Lyapunov theory. Finally, comparative simulations are conducted to validate the advantages of the proposed control scheme in comparison to the existing methods.

Sensorless DFIG System Control via an Electromagnetic Torque Based on MRAS Speed Estimator

The main goals of this research are to develop a method for obtaining the rotor position and speed in a doubly fed induction generator (DFIG) without using sensors in a variable-speed wind turbine installation. The considered method is based on the Model Reference Adaptive System (MRAS). According to this method, electromagnetic torque is used as an error variable for the adaptation process in order to refine the estimate. A good assessment is very important when trying to put into place any strategy that can control the behavior of a DFIG. This method of estimation functions by comparing the actual performance of the DFIG with that of a reference model and adjusting the system parameters to reduce any mismatch between the two. One notable advantage of this developed estimator is its stability across a broad range of speeds. Additionally, it is designed to exhibit resilience in the face of uncertainties in machine parameters. The proportional integral (PI) gains for the MRAS estimator are determined via pole placement. To assess and validate the entire DFIG model and the sensorless estimation method, comprehensive simulations are carried out using MATLAB/Simulink.

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

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

MPPT Optimal Torque Control Strategy for a PMSG-based Wind Turbine Emulator

A wind turbine emulator is a valuable tool, which enables real-time physical simulation of a wind turbine behavior using a dedicated experimental test setup, providing a controlled environment for research and development purposes. One significant advantage of using this tool is its ability to test control algorithms designed to maximize power extraction from the wind turbine. This paper continues previous work on developing a small-scale variable-speed wind turbine emulator based on a Permanent Magnet Synchronous Generator for stand-alone applications (Benhacine et al., 2024). This paper presents enhancements made to the emulator. These improvements facilitate the assessment of a maximum power point tracking control strategy designed to optimize power extraction from the wind turbine.

Adaptive passive fault tolerant control of DFIG-based wind turbine using a self-tuning fractional integral sliding mode control

Controlling variable wind speed turbine (VWT) system based on a doubly-fed induction generator (DFIG) is a challenging task. It requires a control law that is both adaptable and robust enough to handle the complex dynamics of the closed control loop system. Sliding mode control (SMC) is a robust control technology that has shown good performance when employed as a passive fault-tolerant control for wind energy systems. To improve the closed control loop of VWT based on DFIG with the aim of improving energy efficiency, even in presence of nonlinearities and a certain range of bounded parametric uncertainties, whether electrically or mechanically, an adaptive passive fault tolerant control (AP-FTC) based on a self-tuning fractional integral sliding mode control law (ST-FISMC) developed from a novel hyperbolic fractional surface is proposed in this paper. ST-FISMC introduces a nonlinear hyperbolic function into the sliding manifold for self-tuning adaptation of control law, while fractional integral of the control law smooths discontinuous sign function to reduce chattering. Additionally, this work introduces an adaptive observer, developed and proved based on a chosen Lyapunov function. This observer is designed to estimate variations in electrical parameters and stator flux, ensuring sensorless decoupling in indirect field- oriented control (SI-FOC) of DFIG. Lyapunov theory is also used to prove stability of states vectors in closed control loop with presence of bounded parameters uncertainties or external disturbances. Simulation results show that the proposed approach offers better performance in capturing optimal wind energy, as well as the ability to regulate active/reactive power and high resilience in presence of occurring parameter uncertainties or external disturbances.

Sliding mode control based on maximum power point tracking for dynamics of wind turbine system

This article presents a proportional-integral sliding mode control (PI-SMC) approach for a two-mass variable speed wind turbine (VSWT) system. Most studies on wind turbines typically focus mainly on the electromagnetic part of the generators, or even on the high-speed part, considering the shaft stiffness as negligible. However, the generator torque is actually driven by the aerodynamic torque, and a two-mass system like the one studied here plays the role of a transmission element for this power. To address this challenge, the problem of low power generation resulting from wind speed variability is tackled by designing a PI-SMC control law, capable of controlling the mechanical turbine model that optimizes power and torque by tracking the maximum power point (MPPT) for rotational speed and aerodynamic power. To validate the developed theoretical results, an application of the wind turbine system is simulated in Matlab/Simulink, for a particular case. The control used is capable of satisfying the dynamic performance of the systems.

PSO-optimized sensor-less sliding mode control for variable speed wind turbine chains based on DPIG with neural-MRAS observer

PSO-optimized sensor-less sliding mode control for variable speed wind turbine chains based on DPIG with neural-MRAS observer

Improved machine learning-based pitch controller for rated power generation in large-scale wind turbine

In variable speed and variable pitch large-scale wind turbines, the pitch controller plays a crucial role in optimizing power output near the rated value during wind speeds that exceed the rated threshold. Nevertheless, the erratic nature of wind speeds poses challenges to the pitch controller’s efficacy, leading to a decline in generator power. So, there has been a growing interest among researchers in the development of machine learning-based pitch controllers. This paper introduces an improved recurrent radial basis function neural network and its parameters were tuned using the modified particle swarm optimization algorithm to enhance neural network performance. The proposed controller is validated in a benchmark wind turbine and comparative analysis are conducted against existing controllers in the literature. Through a series of comprehensive studies, the proposed controller consistently outperforms its counterparts, particularly in achieving power output close to the rated value.

Robust and intelligent power control for three-bladed horizontal axis wind turbine system using a real wind profile of the northern Morocco

In this work, a robust and intelligent control approach is developed for three-bladed horizontal axis variable-speed wind turbine (VSWT) operating under real climatic conditions (whether the WT is onshore or offshore). Below the rated power, the main purpose of the controller is to optimize the extraction of energy from wind and ensure the full exploitation of the installation, all while minimizing mechanical stress in the system in the presence of uncertainties and external disturbances. In order to achieve optimized wind energy capture without chattering problems or oscillatory phenomena, this study proposes combining the Integral Sliding Mode Control (ISMC) with an artificial neural network technique such as the Radial Basis Function (RBF) to enhance controller performances. Additionally, the RBF neural network (RBFNN) approach is implemented to estimate the wind turbine (WT) model uncertain part, allowing the use of a lower switching gain to achieve faster convergence and avoid steady-state error. Thus, by means of Lyapunov theory, the stability of the system with this controller can be demonstrated. Simulation results of the proposed Neural Network Integral Sliding Mode Control (NN-ISMC) controller are compared with the usual SMC and the standard ISMC. The comparative analysis indicate a superior efficiency of the developed control strategy in terms of decreasing tracking error and eliminating chattering behavior. Moreover, this approach provides a faster transient system response and higher robustness in presence of uncertainties compared to the other controllers.

Power regulation of variable speed multi rotor wind systems using fuzzy cascaded control

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.

Variable speed wind turbine based on doubly fed induction generator using genetic algorithms

Optimization of the control of doubly fed induction generators (DFIGs) is essential for many applications, such as renewable energy systems, industrial automation, and electric cars. Unfortunately, the dynamic and non-linear character of DFIG frequently makes it difficult for conventional control techniques to adjust, which results in less-than-ideal performance. To overcome these obstacles, this paper presents an optimized fuzzy speed control of a doubly fed induction wind generator using a genetic algorithm, which has more advantages than its counterpart PI speed controller. In this study, the modeling of generator in the Park's frame was presented, as well as its indirect vector control applied to the stator flux. Then, to guarantee tracking of the ideal operating point in real-time and to produce the most electricity possible for varying wind speeds, we used a fuzzy PI speed controller. To improve the sizing operation of this controller, we opted for the genetic algorithm technique combined with one of the local search methods, which facilitated the search and reduced the effort compared to the trial-and-error sizing method. Furthermore, this made it possible for the wind system to track the optimal power point maximum with good performance. The simulation results of the suggested control displayed by MATLAB-Simulink illustrate the effectiveness and adaptability of the proposed control scheme across different operating conditions. The analysis of the results showed good performance for speed, small voltage and current ripple when using the fuzzy PI speed controller with genetic algorithm technique. offering promising prospects for practical implementation in variable speed wind turbine applications.

Centralized control of large‐scale wind farm for system frequency stabilization of the power system

AbstractIn recent years, wind power generation has been promoted as a countermeasure to global environmental problems. However, the introduction of many wind power generators into the power system may cause system frequency fluctuations. This paper proposes a control method to reduce system frequency fluctuations by using the rotor kinetic energy (RKE) of a variable speed wind turbine (VSWT). When the frequency falls, this method suppresses fluctuations by consuming RKE to increase VSWT power generation, and when it rises, it decreases VSWT power generation by accumulating RKE. The effectiveness of this method is confirmed through simulations using PSCAD/EMTDC.

Three-phase model of SCIG-based variable speed wind turbine for unbalanced DSLF analysis

Steady state performances of the electric power distribution system are normally assessed or evaluated based on load flow analysis. To properly carry out the analysis, a valid steady state load flow model of each distribution system component, including the wind power plant (WPP), needs to be developed. The present paper proposes a method for modeling and integrating squirrel cage induction generator (SCIG)-based variable speed WPP into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on a single-phase T-circuit model of fixed speed WPP, which has successfully been applied to balanced electric power systems. In the present work, the single-phase T-circuit model is extended and modified to be used in steady state load flow analysis of three-phase unbalanced distribution systems embedded with SCIG-based variable speed WPP. Results of the case studies confirm the validity of the proposed method.

The wind turbine's direct power control of the doubly-fed induction generator

The study suggests a comprehensive approach to modeling and controlling variable-speed wind turbine systems that utilize doubly fed induction generators (DFIGs). To make sure that energy is transferred efficiently between the DFIG rotor and the grid, a two-level inverter with perfect bidirectional switches is used. Using the tip speed ratio algorithm and taking into consideration the randomness in wind speed, the maximum power at the wind turbine is optimized. Then, the control strategy utilizes direct power control (DPC) due to its various advantages. The advantages of employing this control technique are manifold. Firstly, it eliminates the necessity for rotor current control loops. Secondly, it obviates the need for controllers such as PI controllers to manage torque and flux. Furthermore, it has yielded exceptional simulation results when implementing direct power control (DPC) within the MATLAB/Simulink environment, specifically in the context of a doubly fed induction generator (DFIG) wind power system.

Novel Fuzzy Logic Controls to Enhance Dynamic Frequency Control and Pitch Angle Regulation in Variable-Speed Wind Turbines

This study introduced a novel control approach based on fuzzy logic control (FLC) to enhance the frequency regulation capacity of variable-speed wind turbines (VSWTs). The proposed method integrates FLC within droop and inertia control loops. Real-time measurements of the system frequency and the rate of change of frequency (ROCOF) serve as inputs to the FLC, enabling the method to improve the frequency response by VSWTs. In addition, the method employs FLC for pitch angle frequency control, optimizing reserve power for frequency regulation under varying wind speed levels. The innovative aspect of this study lies in the simultaneous application of FLC to pitch angle frequency control and droop/inertia control, leading to the enhanced frequency regulation capability of VSWTs and smoother operation across a range of wind speeds. Compared with traditional methods, the proposed approach provides a comprehensive and effective solution to the challenges associated with frequency regulation in VSWTs. Through simulations across different wind speed scenarios, the proposed control method demonstrated the best performance among various mature methods, highlighting the efficacy of the proposed method on the frequency regulation of VSWTs under different wind speeds. This study’s findings highlight the potential of the proposed FLC-based method to optimize frequency regulation and contribute to more reliable and efficient wind energy systems.

Observer-based single-phase robustness sliding mode controller for the pitch control of a variable speed wind turbine

Observer-based single-phase robustness sliding mode controller for the pitch control of a variable speed wind turbine

Co-optimization of power and spinning reserve in multi-area electrical networks with wind farms

Integrating wind farms into electrical networks reconfigures energy matrices and poses new challenges to system operators. For instance, wind farm owners must now guarantee spinning reserves stipulated in grid codes. This prompts an urgent quest for optimizing power and reserve allocation in liberalized markets using advanced models. In this sense, this paper introduces an optimal power flow (OPF) model to co-optimize power and spinning reserve in multi-area electrical networks with wind farms. The granularity and layout of the wind plant are considered for a correct calculation of the spinning reserve in each variable speed wind turbine. Unlike classical OPF studies, where generator participation is free and mandatory in system reserves, this new approach considers both power and reserve as variable factors in the optimization process, which depend on reserve costs, system load, and wind speed. The approach is evaluated through comparative analyzes on two power networks: a small 12-bus, two-area network with two wind farms and a larger 100-bus, four-area network with four wind farms. Multi-period 12-hour look-ahead analyzes are conducted with variable conditions of demand and wind speed. The studies demonstrate the advantages of the new model compared to the classical OPF, showcasing its ability to optimize power dispatch with wind farms actively contributing to the spinning reserve requirements of the power network.

Wind speed estimation and maximum power point tracking using neuro-fuzzy systems for variable-speed wind generator

This paper proposes a novel method using a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize Maximum Power Point Tracking (MPPT) in variable-speed Wind Turbines (WT). The ANFIS algorithm, blending artificial neural networks and fuzzy logic, addresses issues with traditional wind speed sensors, such as cost, imprecision, and susceptibility to adverse weather conditions. An initial offline-trained ANFIS is suggested to understand turbine power characteristics, and subsequently estimate varying wind speed, addressing strong nonlinearity due to WT aerodynamics and wind speed fluctuations. A second ANFIS efficiently tracks the maximum power point, overcoming limitations of linear controllers. Implemented in Matlab/Simulink for a 3.5 kW WT, the approach demonstrates effectiveness, precision, and faster response time in wind speed estimation and accurate MPPT compared to alternatives. A notable advantage is its independence from instantaneous wind speed measurement, providing a cost-effective solution for wind energy systems.

A chaos game optimization algorithm-based optimal control strategy for performance enhancement of offshore wind farms

This paper presents a novel application of the chaos game optimization (CGO) algorithm to optimally design PI controllers for power electronic interface circuits of offshore wind farms (OWF) consisting of a permanent magnet synchronous generator powered by a variable-speed wind turbine. The OWF is linked to the network via a high-voltage direct current (HVDC) transmission system. The CGO metaheuristic method is employed to fine-tune voltage source converter (VSC)-based HVDC transmission systems’ proportional-integral controller gains. The study explores multiple strategies to extract the highest power from the system while ensuring stability under symmetrical and unsymmetrical fault conditions. The CGO algorithm consistently yields superior results to other algorithms, improving system regaining and stability post disturbances. Consequently, the technique enhances the dynamic and transient stability of the OWF. The detailed study is implemented using MATLAB/Simulink.

Hardware-in-the-loop simulation to validate the fractional-order neuro-fuzzy power control of variable-speed dual-rotor wind turbine systems

New and efficient command strategies and algorithms are needed in the field of wind turbine energy generation to ensure power quality according to international standards. However, conventional linear strategies are still used to command doubly-fed induction generators (DFIGs). Thus, inadequate power quality is obtained, which can cause network-level disturbances. This Hardware-in-the loop (HIL) study presents a new command scheme for a DFIG-based dual-rotor wind turbine (DRWT) system. The new command is a combination of a neuro-fuzzy algorithm and fractional-order control that overcomes the declining power quality of the DFIG-DRWT system. The new strategy is based on using pulse width modulation to generate command pulses for the rotor-side converter of DFIG. In the designed command system, the reference value of the active power is determined using maximum power point tracking to provide efficient power conversion performance under different operating conditions. First, MATLAB software is used to implement the fractional-order neuro-fuzzy (FONF) control technique, and the behavior of the proposed strategy is studied in comparison to that of the traditional direct power command (DPC) technique. A comparison is performed in terms of the ripple minimization ratio, durability, overshoot, steady-state error, reference tracking, and current quality. The results of the comparison show the high performance of the FONF technique. Second, the FONF technique is implemented in HIL test using the dSPACE 1104 Card, where two different forms of wind speed are used to study the characteristics of the proposed strategy and confirm its effectiveness and performance in HIL test compared to DPC technique. The experimental results obtained in the two tests confirm the efficiency, effectiveness, and high performance of the proposed FONF technique compared to DPC technique in improving the energy system characteristics, and the simulation obtains the same results.