NDLI: Transformer fault diagnosis based on improved artificial fish swarm optimization algorithm and BP network

Content Provider	IEEE Xplore Digital Library
Author	Hong Yu Jie Wei Jin Li
Copyright Year	2010
Description	Author affiliation: Harbin Engineering University, Postdoctoral Workstation of Yunnan, Power Grid Corporation, Kunming, China (Hong Yu) \|\| Automation College, Harbin Engineering University, China (Jin Li) \|\| Production and Technology Department, Yunnan Power Grid Corporation, Kunming, China (Jie Wei)
Abstract	IEC three-ratio is an effective method for transformer fault diagnosis in the dissolved gas analysis (DGA). Considering the characteristic of three-ratio boundary is too absolute, fuzzy knowledge is utilized to preprocess. As the same time, for overcoming the deficiency of the back propagation (BP), an improved artificial fish swarm optimization (IAFSO) algorithm is used to optimize the weight and threshold of the BP. The global searching ability of the IAFSO approach is utilized to find the global optimization solution. It can overcome the slower convergence velocity and easily getting into local extremum of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the IAFSO algorithm is introduced to optimize the BP network. Then the IAFSO-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. Correctness and validity of this proposed method has also confirmed for transformer fault diagnosis.
Starting Page	99
Ending Page	104
File Size	758167
Page Count	6
File Format	PDF
ISBN	9781424476534
e-ISBN	9781424476565
DOI	10.1109/ICINDMA.2010.5538357
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automation fault diagnosis back propagation Dissolved gas analysis transformer Gas industry Power grids artificial fish swarm Particle swarm optimization Convergence Fault diagnosis Electrical equipment industry Marine animals Power engineering and energy dissolved gas analysis
Content Type	Text
Resource Type	Article

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