Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

MATEC Web of Conferences. 2015;22:01050 DOI 10.1051/matecconf/20152201050

 

Journal Homepage

Journal Title: MATEC Web of Conferences

ISSN: 2261-236X (Online)

Publisher: EDP Sciences

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials | Technology: Engineering (General). Civil engineering (General)

Country of publisher: France

Language of fulltext: French, English

Full-text formats available: PDF

 

AUTHORS

Yuan Pu
Mao Jianlin
Xiang Fenghong
Liu Lian
Zhang Maoxing

EDITORIAL INFORMATION

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Instructions for authors

Time From Submission to Publication: 6 weeks

 

Abstract | Full Text

BP neural network (Back-Propagation Neural Network, BP-NN) is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.