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 495

chapter is in the famous frame works in the aim to introduce of the mechanics for
best growth used from this source energy.
In this chapter the main goal of the controllers is to slow decrement rotor speed
swing function and electrical power while minimizing the control activating loads.
The blade pitch angel and the generator torque are available control inputs. Since
wind speeds over high, its nominal count, power is fixed to the turbines rated output

by altering place the control objective from maximum power catch (Boukhezzar
et al. 2007 ).
Linear controllers have been widely used for power regulation (

xed) thought
the control of blade pitch angel (Hand 1999 ;Ma 1997 ), suggested PI, PID pitch
controllers. LQ, LQG control techniques have also been created in Wermter and
Sun ( 2000 ).
Output power of wind turbine generator (WTG) is not stable due to wind speed
changes. To decrease the unpleasant effects of the power system introducing
WTGs, there are a number of available reports on output power control of WTGs
detailing various researches based on pitch angle control, variable speed wind
turbines, energy storage systems, and so on.
Kaneko et al. ( 2010 ) presents an incorporated control technique for a WF to
diminish frequency deviations in a small power system. The WF achieves the
frequency control with two control schemes: load estimation and short-term ahead
wind speed prediction. For load estimation in the small power system, a minimal-
order observer is used as disturbance observer. The estimated load is utilized to
determine the output power command of the WF. To regulate the output power
command of the WF according to wind speed changing, short-term ahead wind
speed is predicted by using least-squares method. The predicted wind speed adjusts
the output power command of the WF as a multiplying factor with fuzzy reasoning.
A typical Wind Energy Conversion System (WECS) is authoritative (mighty) of
changing rotational speed and blade pitch angel is given as a block chart in Fig. 1 .
Several purposes of these rule extraction methods are data discover knowledge
acquisition for symbolic AI systems and improved autarchy for data mining
applications (Pao 1989 ; Jianjun et al. 2006 ). Six popular techniques, Genetic Fuzzy
System GFS, Fuzzy Rule Extraction from Neural Network using Genetic Algorithm
(FREGNA), Hybrid technique and Nero Fuzzy Genetic Controller where the fuzzy
knowledge based are tuned automatically by Genetic Algorithm (GA) as known
Tuned Fuzzy Genetic System (TFGS), refuses wind disturbance in WECS input
with pitch angel control generation and estimation parameters.
This chapter is organized as follows: in Sect. 2 related work has extended.
A brief modeling of the wind turbine characteristic has been presented in Sect. 3 .
A simpli ed mathematical model is derived. Then multi Layer Percepron (MLP)

neural network and Radial Basis Function networks (RBF) have been discussed in
Sect. 4 and we will indicate the control objectives of this work. Section 5 starts with
a brief description of some Genetic Fuzzy System (GFS) techniques and protocols
of rules. Thus Rule extraction and the FRENGA controller in pitch and torque is
then presented in Sects. 7 and 8 . Section 9 extended Hybrid Optimal control
strategy and

finally Tuned Fuzzy Genetic System (TFGS) proposed in Sect. 10 .