24-04-2014, 03:44 PM
WIND GENERATION SYSTEM - A COMPREHENSIVE SURVEY REPORT
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Abstract
The rapidly increasing contribution of wind electric
power generation around the world has motivated a need to
develop more widely applicable methodologies for evaluating the
actual benefits of adding wind turbines to traditional power
generating systems. This paper mainly focuses on various recent
techniques used in wind generation system in order to enhance
better performance and high efficiency of the system.
INTRODUCTION
The global electrical energy consumption is rising and there
is steady increase of the demand on power generation. So,
alternative energy source investment is becoming more
important now days. Wind power generation systems are
recently getting lot of attention, because they are most cost
competitive, environmental clean and safe renewable power
source. Recent evolution of power semiconductors and variable
frequency drive technology has aided the acceptance of variable
speed generation systems. In spite of additional cost of power
electronics and control, the total energy captured in a variable
speed wind turbine system is larger and therefore the lifecycle
cost is lower than with fixed speed drives. Denmark was the
first country to erect windmill and the top five countries in the
world to have the highest installed wind power capacity are
Germany, Spain, Denmark, U.S.A and India respectively.
The main objective of this paper is to focus on various recent
techniques used in wind power generation system.
INTELLIGENCE CONTROL OF WPGS
A new methodology is proposed to control horizontal axis
WPGS using neural network. The proposed control system
consists of neural network inverse and forward identifiers,
which are used to model 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 pitch angle actuators [30]. It deals with the
development of artificial neural network (ANN) wind power
forecaster and the integration of wind power forecast results
into unit commitment (UC) scheduling considering forecasting
uncertainty by the probabilistic concept of confidence interval.
The main aim of the wind power forecaster is to balance the
cost savings and the risk on system reliability [31],[32]. In this
paper neural network principles are applied for wind speed
estimation and robust control of maximum wind power
extraction against potential drift of wind turbine power
coefficient curve. This control system will deliver maximum
electric power to customer with light weight, high efficiency
and high reliability without mechanical sensors. This control
method is applicable to both wind turbine directly driven
PMSG and a cage induction machine wind generation system
(CIWG) [33], [34].This paper also deals with the stabilization
of power system with WPGS by active power control of a
number of micro superconducting magnetic energy storage
(SMES) units. Here a performance index is defined with the
oscillations of synchronous generator taken into account. In
order to obtain the optimum allocation of micro SMES units of
predetermined number, genetic algorithm (GA) is adopted
based on performance index [35]. This paper analyzes the
control strategy and performance of an HVDC link based on
voltage sourced converters (VSC), which is controlled by using
fuzzy logic principles. The system can feed power to weak or
even dead network under fluctuations and big variations of the
input power. The fuzzy logic based control of the system helps
to optimize efficiency of HVDC link under disturbances or
fluctuations [36].
CONCLUSION
Wind power technology has become one of the most
promising renewable energy sources in the recent past. With
increasing demand of wind energy, several research areas arose.
The main aim of this paper is to survey the recent different
techniques used in WPGS in order to enhance better
performance and high efficiency of the wind power generation
system.