Abstract--This paper is a part of a research project to study
the dynamics and control of horizontal axis wind turbines. The
main objective of this work is to give contribution to the control
problem of horizontal axis wind power plants using neural
network. This work presents a new methodology to control wind
power plants. It employs an adaptive neural networks self-tuning
control system to control medium scale wind turbine system,
during different operating conditions. The proposed control
system consists of neural networks inverse and forward
identifiers, which are used to model the inverse and forward
dynamics of the system, respectively, and to adapt neural
controller parameters, and reference model that are used to
enhance trajectory training, and neural controller which is used
to generate control signal to the pitch angle actuator. The
proposed control system performed good simulation results, and
the latter show that the proposed control system is really a new
contribution in the area of control of horizontal axis wind turbine
power generation systems.
Keywords—Control, Identification, Neural Networks, Wind
Turbine, Wind Power.
I. INTRODUCTION
The main sources of electric power have been fuel-burning
power generators which use the energy from non-renewable
fuels to rotate a shaft connected to an electric generator. These
systems have seen vast improvements in the areas of
efficiency, emissions, and controllability because they have
always been the primary power sources.
The integration of wind power into utility systems is an
area that is attracting growing interest. The development of
renewable natural energy has attracted considerable interest in
recent years primarily due to concern about environment
pollution caused by the burning of fossils, and its continually
diminishing reserves [1]. Wind turbine generator system
provides an environmentally friendly, economically,
competitive and socially beneficial means of electricity
generation.
Much attention has been paid in recent times to the
generation of clean energy. These natural and clean sources of
energy need to have no by-products associated with their
operation [2]. Wind energy is gaining momentum in this field
of clean energy due to its relatively low cost. Wind turbines
complement the use of other electric power sources by
providing a least cost approach under certain conditions. In
addition, wind turbines need minimum maintenance.
The most special feature about wind turbines is the fact
that, unlike other generation systems, the power inflow rate is
not controllable [8]. In most generation systems, the fuel flow
rate, or the amount of energy, applied to the generator controls
the output voltage and frequency. The power fluctuates with
the variation of winds. The fact that one has no control over
the energy source input, the unpredictability of wind and the
varying power demand are more than enough concerns to
justify the need for a control system, which will regulate the
parameters of the wind energy conversion system that need to
be controlled for matched operation of the wind turbine.
II. WIND TURBINE CHARACTERISTICS
The kinetic energy, U of a parcel of air of mass m flowing
at upstream speed u in the axial direction (x-direction) of the
wind turbine is given by:
2 2
1 1
2 2
U mu Ax u (1)
where, A is the cross–sectional (swept) area of the wind
turbine in square meters, is the air density in kg/m3, and x is
the thickness of the wind parcel in meters.
The power in the wind P
w
, is the time derivative of the
kinetic energy and is given in (2), which represents the total
power available for extraction.
3
1
2
w
P Au (2)
As the wind passes over the turbine, the wind will lose
power equal to the power extracted by the turbine. The
extracted power is usually expressed in terms of the wind
turbine swept area A, because the upstream cross-sectional
area is not physically measurable as the cross-sectional area of
the wind turbine.
3 3
, 2 1 2
1 8 2 16 1
2 9 3 27 2
w ideal
P A u Au (3)
The factor 16/27 = 0.59 is called Betz coefficient. It shows
that an actual turbine cannot extract more than 59 percent of
the total power in an undistributed tube of air of the same area
(the cross-sectional area equal to the wind turbine swept area).
The fraction of power P
m
extracted from the available
power in the wind by practical turbines is expressed by the
coefficient of performance C
p
. The actual mechanical power
extracted can be written as:
3
1
2
m p p w
P C Au CP (4)
The value of Cp is highly non-linear and varies with the
wind speed, the rotational speed of the turbine, and the turbine
blade parameters such as pitch angle. The variable that
combines the effects of the rotational speed and the wind
speed is called tip speed ratio. The tip speed ratio , is defined
as the ratio between the rectilinear speed of the turbine tip,
t
R, and the wind speed u as given in (5).
NN Self-Tuning Pitch Angle Controller of
Wind Power Generation Unit
A. F. Bati, and S. K. Leabi
2019 142440178X/06/$20.00 ©2006 IEEE PSCE 2006