A Review of Control Techniques for Wind Energy Conversion System

Wind energy is the most efficient and advanced form of renewable energy (RE) in recent decades, and an effective controller is required to regulate the power generated by wind energy. This study provides an overview of state-of-the-art control strategies for wind energy conversion systems (WECS). Studies on the pitch angle controller, the maximum power point tracking (MPPT) controller, the machine side controller (MSC), and the grid side controller (GSC) are reviewed and discussed. Related works are analyzed, including evolution, software used, input and output parameters, specifications, merits, and limitations of different control techniques. The analysis shows that better performance can be obtained by the adaptive and soft-computing based pitch angle controller and MPPT controller, the field-oriented control for MSC, and the voltage-oriented control for GSC. This study provides an appropriate benchmark for further wind energy research

Bayesian reinforcement learning with MCMC to maximize energy output of vertical axis wind turbine

Optimization of energy output of small scale wind turbines requires a controller which keeps the wind speed to rotor tip speed ratio at the optimum value. An analytic solution can be obtained if the dynamic model of the complete system is known and wind speed can be anticipated. However, not only aging but also errors in modeling and wind speed prediction prevent a straightforward solution. This thesis proposes to apply a reinforcement learning approach designed to optimize dynamic systems with continuous state and action spaces, to the energy output optimization of Vertical Axis Wind Turbines (VAWT). The dynamic modeling and load control of the wind turbine are accomplished in the same process. The proposed algorithm is a model-free Bayesian Reinforcement Learning using Markov Chain Monte Carlo method (MCMC) to obtain the parameters of an optimal policy. The proposed method learns wind speed pro les and system model, therefore, can utilize all system states and observed wind speed pro les to calculate an optimal control signal by using a Radial Basis Function Neural Network (RBFNN). The proposed method is validated by performing simulation studies on a permanent magnet synchronous generator-based VAWT Simulink model to compare with the classical Maximum Power Point Tracking (MPPT). The results show signi cant improvement over the classical method, especially during the wind speed transients, promising a superior energy output in turbulent settings; which coincide with the expected application areas of VAWT

Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm

The primary focus of this paper is centered on the numerical analysis and optimal control of vertical axis wind turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically tackle small-scale wind turbines with permanent magnet synchronous generator, which are well-suited to local and compact production of electrical energy in small scale such as urban and rural infrastructure installations. Through this work, we formulate and implement an RL strategy using Markov chain Monte Carlo (MCMC) algorithm to optimize the long-term energy output of the wind turbine. Our MCMC-based RL algorithm is a model-free and gradient-free algorithm, where the designer does not have to know the precise dynamics of the plant and their uncertainties. The method specifically overcomes the shortcomings typically associated with conventional solutions including but not limited to component aging, modeling errors and inaccuracies in the estimation of wind speed patterns. It has been observed to be especially successful in capturing power from wind transients; it modulates the generator load and hence rotor torque load so that the rotor tip speed reaches the optimum value for the anticipated wind speed. This ratio of rotor tip speed to wind speed is known to be critical in wind power applications. The wind to load energy efficiency of the proposed method is shown to be superior to the classical maximum power point tracking method

The maximum power point tracking based-control system for small-scale wind turbine using fuzzy logic

This paper presents the research on small-scale wind turbine systems based on the Maximum Power Point Tracking (MPPT) algorithm. Then propose a new structure of a small-scale wind turbine system to simplify the structure of the system, making the system highly practical. This paper also presented an MPPT-Fuzzy controller design and proposed a control system using the wind speed sensor for small-scale wind turbines. Systems are simulated using Matlab/Simulink software to evaluate the feasibility of the proposed controller. As a result, the system with the MPPT-Fuzzy controller has much better quality than the traditional control system

A Survey of Artificial Neural Network in Wind Energy Systems

Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies

A survey of artificial neural network in wind energy systems

© 2018 Elsevier Ltd Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies