Review and Improvement of Several Optimal Intelligent Pitch Controllers and Estimator of WECS via Artificial Intelligent Approaches - Complex System Modelling and Control Through Intelligent Soft Computations - page 492

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

Hadi Kasiri, Hamid Reza Momeni and Mohammad Saniee Abadeh

Abstract Wind turbines in megawatt classi cation ordinarily rotate at variable

speed in wind farm. Therefore turbine operation must be managed in order to
maximize the conversion ef

ciency 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 Arti

fl

cial 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 dis-
turbance 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 Arti

cial Intelligent (AI) methods extraction system outperform the best
and earliest methods in controlling the output during wind fl fluctuation.