DocumentCode :
3192128
Title :
Improved Genetic Algorithm Approach for Multi-Objective contingency constrained Reactive Power Planning
Author :
Durairaj, S. ; Devaraj ; Kannan, P.S.
Author_Institution :
Department of Electrical Engineering, Arulmigu Kalasalingam College of Engg., Krishnankoil-626 190, Tamilnadu, e-mail: rajsdr@rediffmail.com
fYear :
2005
fDate :
11-13 Dec. 2005
Firstpage :
510
Lastpage :
515
Abstract :
Reactive Power Planning (RPP) is one of the important tasks in the operation and control of power system. This paper presents a Genetic Algorithm (GA) based approach for solving the contingency-constrained reactive power optimization problem. Voltage bus magnitude, transformer tap setting and reactive power generation of capacitor bank are the control variables. A binary-coded GA with tournament selection, two point crossover and bit-wise mutation is used to solve this complex optimization problem. In the proposed algorithm, some modifications are applied to the original GA in order to take into account the discrete nature of transformer tap setting and capacitor bank. The proposed approach has been evaluated with four different objective functions namely, loss minimization, voltage profile improvement, voltage stability enhancement and total cost minimization. Voltage stability level of the system is defined based on the L-index of load buses. The optimal locations of the VAR sources are also identified using the GA based algorithm. The proposed algorithm has been tested on IEEE 30-bus and IEEE 57-bus test systems and successful results have been obtained.
Keywords :
Genetic Algorithm; L-Index; Line loss; Reactive power planning; Voltage stability; Control systems; Genetic algorithms; Power generation; Power system control; Power system planning; Power systems; Reactive power; Reactive power control; Stability; System testing; Genetic Algorithm; L-Index; Line loss; Reactive power planning; Voltage stability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
INDICON, 2005 Annual IEEE
Print_ISBN :
0-7803-9503-4
Type :
conf
DOI :
10.1109/INDCON.2005.1590223
Filename :
1590223
Link To Document :
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