A Survey of Artificial Neural Network in Wind Energy Systems

Improvement of the efficiency of photovoltaic system based on new maximum power point tracking (MPPT) algorithms is the most promising solution due to its low cost and its easy implementation without equipment updating Many MPPT methods with fixed step size have been developed However, when atmospheric conditions change rapidly , the performance of conventional algorithms is reduced In this paper, a new variable step size Incremental Conductance IC MPPT algorithm has been proposed Modeling and simulation of different operational conditions of conventional Incremental Conductance IC and proposed methods are presented The proposed method was developed and tested successfully on a photovoltaic system based on Flyback converter and control circuit using dsPIC30F4011 Both, simulation and experimental design are provided in several aspects A comparative study between the proposed variable step size and ﬁxed step size IC MPPT method under similar operating conditions is presented The obtained results demonstrate the efficiency of the proposed MPPT algorithm in terms of speed in MPP tracking and accuracy

Simulation and experimental design of a new advanced variable step size Incremental Conductance MPPT algorithm for PV systems

Renewable energy technique is gaining more interest due to increasing demand and threat zero carbon foot prints. The energy from wind has a high potential as a source of energy. The growing demand of wind energy tends to produce a quality output power in terms of grid integration. An appropriate controller is required to control the power produce by the wind energy. A decent number of research publications reports had reviewed maximum power point tracking (MPPT), grid side controller (GSC) and machine side controller (MSC) associated with wind energy conversion system (WECS). However survey on pitch angle based control has not been focused exclusively in any such reviews. A concise review of pitch angle controller, maximum power extraction control and grid synchronisation controller is analysed in this paper. Thus, this paper presents a comprehensive review of overall control strategies for wind energy conversion control. The review paper is intended to provide a suitable reference for further research in the field of wind energy.

Recent developments of control strategies for wind energy conversion system

In this research, a robust feedback linearization technique is studied for nonlinear processes control. The main contributions are described as follows: 1) Theory says that if a linearized controlled process is stable, then nonlinear process states are asymptotically stable, it is not satisfied in applications because some states converge to small values; therefore, a theorem based on Lyapunov theory is proposed to prove that if a linearized controlled process is stable, then nonlinear process states are uniformly stable. 2) Theory says that all the main and crossed states feedbacks should be considered for the nonlinear processes regulation, it makes more difficult to find the controller gains; consequently, only the main states feedbacks are utilized to obtain a satisfactory result in applications. This introduced strategy is applied in a fuel cell and a manipulator.

Robust feedback linearization for nonlinear processes control

Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine

In order to be economically competitive, various control systems are used in large scale wind turbines. These systems enable the wind turbine to work efficiently and produce the maximum power output at varying wind speed. In this paper, an adaptive control based on radial-basis-function neural network (NN) is proposed for different operation modes of variable-speed variable-pitch wind turbines including torque control at speeds lower than rated wind speeds, pitch control at higher wind speeds and smooth transition between these two modes The adaptive NN control approximates the nonlinear dynamics of the wind turbine based on input/output measurements and ensures smooth tracking of the optimal tip-speed-ratio at different wind speeds. The robust NN weight updating rules are obtained using Lyapunov stability analysis. The proposed control algorithm is first tested with a simplified mathematical model of a wind turbine, and then the validity of results is verified by simulation studies on a 5 MW wind turbine simulator.

Julian Ehlers

Papers

Adaptive Control of a Variable-Speed Variable-Pitch Wind Turbine Using Radial-Basis Function Neural Network