OPTIMIZATION OF COST OF ELECTRICITY GENERATED BY HYDRO POWER PLANT USING ARTIFICIAL NEURAL NETWORKS
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Date
2014
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Punjab Agricultural University, Ludhiana
Abstract
Economic operation and control of interconnected power systems involves the solution of
difficult optimization problems that require good computational tools. Evolutionary
Computation is an area of Computer Science that uses idea from biological evolution to solve
problems. Evolutionary computation is one such tool that has shown its ability in solving
complex problems. It can be implemented in various forms such as genetic algorithms. The
current work presents a prominence for the optimization of the cost of generating units of
hydro power plant by genetic algorithms. To optimize the cost of generated electricity, it is
important to ensure constant generation of electricity during a time period. A good quality of
the electric power system requires both the frequency and voltage to remain at standard values
during operation. The foremost task is to keep the frequency constant against the randomly
varying active power loads, which are also referred to as unknown external disturbance. The
objectives are to minimize the transient deviations in frequency and to ensure their steady
state errors to be zeros, so that constant power can be generated. Once optimized power will
be generated it will automatically optimize the cost of generated power because generated
power and cost of generated power are directly proportional to each other. Genetic algorithm
(GA) is used for optimization of integral gains and bias factors, which are applied to
automatic generation control (AGC). Tie-line bias KI and frequency bias parameter B are
optimized by using real coded GA. It is used to search for the optimal set of parameters (KI
and B), which in turn optimize the frequency factor. Using GA results are obtained for given
set of hydro units and optimized cost corresponding to each set of hydro generating unit.
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