Maximum Wind Energy Extraction Research Articles

Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model

The application of floating offshore wind turbines means better exploitation of offshore wind resources. However, the six-degree of freedom motion characteristics of floating platforms bring greater challenges to control system design. Based on the dynamic characteristics of semi-submersible floating offshore wind turbines, this paper establishes a simplified nonlinear dynamic model for control system design. On this basis, a complete framework for nonlinear model predictive control of maximum wind energy extraction for semi-submersible floating offshore wind turbines considering wind and wave disturbances is developed. Based on the previewed wind and wave, a dynamic optimization problem with both state and control constraints is constructed, considering maximum wind energy extraction and torque fluctuation. Then, an improved equilibrium optimizer is proposed to address the nonlinear non-convex dynamic optimization problems, which achieves a better trade-off between exploitation and exploration. Simulation results verify the superiority of the proposed nonlinear model predictive control framework via the improved equilibrium optimizer, and the influences of different control algorithms on the platform motion state, power coefficient, and equivalent wind speed are analyzed.

Optimization of wind energy extraction for variable speed wind turbines using fuzzy backstepping sliding mode control based on multi objective PSO

This study addresses the problem of designing a nonlinear feedback control strategy for horizontal axis variable speed wind turbines in the below-rated wind speed operating region (whether the wind turbine is offshore or onshore). The objective is to operate the wind turbines to maximum wind energy extraction while reducing the mechanical load. To overcome both issues, a robust nonlinear control strategy based on sliding mode control (SMC) is proposed which tries to seek for an improved performance. The proposed controller has been developed to compensate for negative consequences of external disturbances, measurement noises and unmodeled dynamics. This control strategy exploits a tracking controller that keeps an optimal value for the ratio between the rotor angular speed and the wind speed. For maximum wind energy extraction, the control algorithm employs the sliding mode state output feedback torque controller to provide robustness, combined with backstepping scheme to ensure better performance in the presence of matched and mismatched uncertainties. Next, a fuzzy logic system (FLS) approach is introduced to the backstepping sliding mode controller (BSMC) to achieve better mechanical loads prevention suffered by the transmission shaft by adjusting the parameters of the controller. Finally, multi-objective particle swarm optimization (MOPSO) algorithm is used to find the optimal values for the adjustable parameters of the proposed fuzzy BSMC (FBSMC). To validate the proposed control structure, FAST aeroelastic simulator is used. The obtained results illustrate that the presented control scheme has satisfactory performance.

Sensorless Optimal Control Strategy of PMSG in Wind Energy Conversion System

This paper presents a sensorless optimal control strategy of Permanent Magnet Synchronous Generator (PMSG) used in Wind Energy Conversion System (WECS). An approach to develop a simple reduced speed observer for PMSG is developed. The speed and rotor position estimation of the PMSG are obtained by only measuring phase voltage and current. Maximum wind energy extraction is achieved by running the wind turbine generator in variable-speed mode. The rotor speed is allowed to vary in sympathy with the wind speed by maintaining the tip speed ratio to the value that maximizes aerodynamic efficiency. Advantages of this mechanical sensorless control strategy for maximum power estimation are demonstrated by digital simulation of the WECS.

Integral sliding mode control for increasing maximum power extraction efficiency of variable-speed wind energy system

Maximum wind energy extraction remains a challenge for large-scale wind turbines due to their higher moment of inertia. In this respect, this study investigates the challenges of achieving a maximum power extraction facility from the variable speed wind turbine (VSWT) by using a sliding mode control (SMC) scheme. Firstly, a integral sliding manifold is designed to achieve maximum power extraction and reduce mechanical loads in large-scale wind turbines (WTs). Secondly, the sliding manifold’s reachability condition is derived based on the Lyapunov function, which guarantees the fast convergence rate of the error dynamics in-finite time. Further, the proposed control scheme in this study produces the efficient chatter-free electromagnetic torque reference to the generator by holding the blade pitch angle at zero. Specifically, the proposed approach can lead to better performance meanwhile reaching the maximum power capture from below rated wind speed. Finally, we demonstrate the feasibility and efficacy of the proposed method through a widely-recognized benchmark model of WT using MATLAB/SIMULINK.

Nonlinear Predictive Control Method for Maximizing Wind Energy Extraction of Variable Speed Wind Turbines under Turbulence

Generally, wind turbines are controlled by Maximum Power Point Tracking (MPPT) strategies in order to achieve maximum power extraction below its rated value. But it is very difficult to adjust the rotor speed according to the highly fluctuating wind speed accurately and quickly, due to the large inertia of wind turbines, and therefore, the efficiency of wind energy extraction will never reach its theoretical maximum value. To address this problem, a new method has been developed in this paper which is totally different from the known classical methods. In this paper a wind speeds prediction for maximum wind energy extraction (MWEE) of variable-speed wind turbines (VSWTs) is presented. A nonlinear predictive control is developed by solving a nonlinear optimization problem to generate the optimal generator torque sequence and consequently the previewed rotor speeds with maximum wind energy extraction. A detailed explanation has been provided of how this new method works through a detailed block diagram; accurate algorithm and flowchart. The proposed nonlinear predictive method takes full advantage and the MWEE objective is confirmed by the simulation results compared to the classical TSR methods.

Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm

Wind energy extraction for large-scale variable-speed wind turbines could be improved by nonlinear model predictive control. However, the latter entails a sequential global optimization problem with a nonconvex cost function that brings about the heavily computational burden and impedes its real-time application. In this paper, a novel nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm is proposed for maximum wind energy extraction of wind turbines. A dynamic optimization problem with both state and control constraints is constructed, and the single-objective nonlinear function is formulated by using a weighting factor to integrate the extracted wind energy and the generator torque variation within a prediction period of several seconds. On this basis, a complete framework of the nonlinear model predictive control via intelligent algorithm is developed to offer a new paradigm for the design and implementation of the nonlinear model predictive control for wind turbines. Specifically, a new Yin-Yang grey wolf optimization algorithm is proposed, in which the concept of balance between cooperation and competition inspired by Yin-Yang-pair optimization is adopted to achieve the efficient convergence and global optimum. Simulation results verify the superiority of the proposed nonlinear model predictive control via the new Yin-Yang grey wolf optimization algorithm.

Multi-objective output feedback control strategy for a variable speed wind energy conversion system

This paper deals with the problem of sensorless output feedback control for wind energy conversion systems (WECSs). The considered system consists of a synchronous aero-generator connected to the grid through an AC/DC/AC converter. Maximum wind energy extraction is achieved by running the wind turbine generator in a variable-speed mode without using a wind velocity sensor. This work establishes a different approach to estimate the rotor position, speed, and turbine torque. Furthermore, the proposed technique aims to provide an on-line estimation of the grid voltage and pulsation, considered generally as accessible. To overcome these issues, a high gain observer is designed. It provides estimates of all state variables, only from the current and voltage measurements. An integral sliding mode controller is then synthesized based on the estimation provided by the observer, with the view of ensuring the MPPT objective. Several simulations show that all control objectives are achieved and the output feedback performances enjoy robustness properties.

New perspectives on maximum wind energy extraction of variable-speed wind turbines using previewed wind speeds

Maximum wind energy extraction remains a challenge for the large-scale wind turbines due to their increasing moment of inertia. In this study, a novel nonlinear predictive control method using previewed wind speeds is proposed to address the issue of maximum wind energy extraction for variable-speed wind turbines. Different from the conventional structure, the proposed nonlinear predictive control is based on the discrete prediction model established by a large time step. By doing so, the number of time discrete points is significantly reduced and the quantity of the solution set becomes numerable. As a result, the implementation complexity and computational burden are able to be relieved, and the real-time implementation becomes practical and easy to carry out. The proposed method is demonstrated on two models of wind turbines with different scales, and its performance is compared with the industrial controller. Comparison results show that the increased energy extraction efficiency gained by the proposed method is more than 1% on the two turbine models under different wind conditions, and thus the superior capability in extracting wind energy promises potential industrial applications. Meanwhile, the expense is the considerable increment of torque variation, which can be relieved by revising the control parameters. The presented method provides new insights into the design of the control methods for wind turbines equipped with advanced measurement devices.

Maximum Wind Energy Extraction by Using Neural Network Estimation and Predictive Control of Boost Converter

This paper proposes a new method to extract maximum energy from wind turbine systems. The artificial neural network (ANN) is used to estimate the wind speed based on the rotor speed and the output power. In addition to ANN, a predictive controller is used to maximize the efficiency of the boost converter. The method has been developed and analyzed by utilizing a turbine directly driven permanent-magnet synchronous generator (PMSG). The simulation results verify the performance of the proposed method. Results show that this method maximizes wind energy extraction with more accuracy and fastness.

Development of Novel Robust Regulator for Maximum Wind Energy Extraction Based upon Perturbation and Observation

This paper develops a robust regulator design approach to maximum power point tracking (MPPT) of a variable-speed wind energy conversion system (WECS) under the concept of perturbation and observation. The proposed perturb and observe regulators (PORs) rooted on the sliding mode method employs the optimal power curve (OPC) to realize MPPT operations by continuously adjusting rotor speeds and the duty cycles, which can ensure control performance against system parameter variations. The proposed PORs can detect sudden wind speed changes indirectly through the mechanical power coefficient, which is used to acquire the rotor speed reference by comparing it with the optimal power constant. For the speed and duty cycle regulation, two novel controllers based on the proposed POR, i.e., an MPPT controller and a speed controller, are devised in this research. Moreover, by applying the small-signal analysis on a nonlinear wind turbine system, the convergence of the proposed speed controller is proven for the first time based on the Lyapunov theory, and meanwhile, a single-pole transfer function, to describe the effect of duty cycle variations on rotor speeds, is designed to ensure its stability. The proposed strategy is verified by simulation cases operated in MATLAB/Simulink and experimental results performed from a 0.5-kW wind turbine generator simulator.

Modeling, simulation and control of wind turbine driven doubly-fed induction generator with matrix converter on the rotor side

In this paper, a wind energy conversion system, which consists of a variable speed wind turbine with doubly-fed induction generator (DFIG) fed by a matrix converter is considered. The stator of the wind turbine driven generator is directly connected to the grid, while the rotor is connected via slip-rings to the output of a matrix converter. The matrix converter is supplied from the grid and replaces the conventional two back-to-back converters used for the control of a DFIG. Modeling of the energy conversion system considers super-synchronous and sub-synchronous operating conditions, which are achieved by means of the matrix converter. In order to decouple the active and reactive power, stator field oriented control is applied. Speed mode control is adopted for maximum wind energy extraction, provided that the wind speed and pitch angle of the turbine are known for each sampling period. Consequently, a 2-D lookup table calculating the reference speed by means of interpolation/extrapolation is introduced. Reactive power control is performed so that the stator reactive power is kept to zero. Promising simulation results demonstrating the control performance of the wind energy conversion system are presented.

A comparative study between three sensorless control strategies for PMSG in wind energy conversion system

This paper presents a comparative study of sliding mode, artificial neural network and model reference adaptive speed observers for a speed sensorless permanent magnet synchronous generator (PMSG) in wind energy conversion system (WECS). Wind velocity and position sensorless operating methods for wind generation system using observer are proposed only by measuring phase voltages and currents. Maximum wind energy extraction is achieved by running the wind turbine generator in variable-speed mode. In addition the three speed observers are compared to verify the robustness against parameter variations.

Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction

The wind power production spreading, also aided by the transition from constant to variable speed operation, involves the development of efficient control systems to improve the effectiveness of power production systems. This paper presents a data-driven design methodology able to generate a Takagi-Sugeno-Kang (TSK) fuzzy model for maximum energy extraction from variable speed wind turbines. In order to obtain the TSK model, fuzzy clustering methods for partitioning the input-output space, combined with genetic algorithms, and recursive least-squares optimization methods for model parameter adaptation are used. The implemented TSK fuzzy model, as confirmed by some simulation results on a doubly fed induction generator connected to a power system, exhibits high speed of computation, low memory occupancy, fault tolerance, and learning capability.