A REFINED GENETIC ALGORITHM FOR FAULT SECTION ESTIMATION IN POWER SYSTEMS USING THE TIME SEQUENCE INFORMATION OF CIRCUIT BREAKERS

1996 ◽  
Vol 24 (8) ◽  
pp. 801-815 ◽  
Author(s):  
FUSHUAN WEN ◽  
ZHENXIANG HAN
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Time Sequence ◽  
Sequence Information ◽  
Circuit Breakers

Expert system for fault section estimation of power systems using time-sequence information

1992 ◽  
Vol 14 (2-3) ◽  
pp. 225-232 ◽  
Author(s):  
Chunling Yang ◽  
Hiroshi Okamoto ◽  
Akihiko Yokoyama ◽  
Yasuji Sekine
Keyword(s):  
Expert System ◽  
Power Systems ◽  
Time Sequence ◽  
Sequence Information

Genetic algorithm-based method for contingency screening in power systems

1996 ◽  
Vol 116 (2) ◽  
pp. 99-111 ◽  
Author(s):  
Hiroumi Saitoh ◽  
Yutaka Takano ◽  
Junichi Toyoda
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Contingency Screening

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

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