Review and Improvement of Several Optimal Intelligent Pitch Controllers and Estimator of WECS via Artificial Intelligent Approaches

Abstract

Wind turbines in megawatt classification ordinarily rotate at variable speed in wind farm. Therefore turbine operation must be managed in order to maximize the conversion efficiency below rated power and reduce loading on the drive-train. In addition, to control the energy captured throughout operation above and below rated wind speed, researchers particularly employ pitch control of the blades. Thus, we could manage the energy captured throughout operation above and below rated wind speed using pitch control of the blades. This chapter suggests six new plans to conquer wind fluctuation problems based on a new Nero Fuzzy and Nero Fuzzy Genetic Controller where the fuzzy knowledge based are tuned automatically by Genetic Algorithm (GA) as known Tuned Fuzzy Genetic System (TFGS). Additionally In this Chapter, a new hybrid control has been trained that Wind Energy Conversion System (WECS) has optimal performance. This method contains a Multi-Layer Perceptron (MLP) Neural Network (NN) (MLPNN) and a Fuzzy Rule extraction from a Trained Artificial Neural Network using Genetic Algorithm (FRENGA). Proposed Hybrid method recognizes disturbance wind with sensors and it generates desired pitch angle control by comparison between FRENGA and MLPNN results. One of them has better signal control is selected to send to pitch blade controller. Consequently Proposed strategies reject wind disturbance in Wind Energy Conversion Systems (WECSs) input with pitch angel control generation. Consequently, proposed approaches have regulated output aerodynamic power and torque in the nominal range. Results indicate that the new proposed Artificial Intelligent (AI) methods extraction system outperform the best and earliest methods in controlling the output during wind fluctuation.