NDLI: Fault Diagnosis of Power Equipment Based On Dissolved Gas Analysis And LS Fusion Combining Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Ganyun Lv Xiaodong Wang
Copyright Year	2007
Description	Author affiliation: Zhejiang Normal Univ., Jinhua (Ganyun Lv; Xiaodong Wang)
Abstract	In this paper, a new method for power equipment fault diagnosis is presented based on a least square (LS) fusion combining neural network and dissolved gas analysis (DGA). Contents of five characteristic gases obtained by DGA are preprocessed through a special dada dealing process, and 6 features for fault diagnosis are extracted. Then five child back- propagation (BP) artificial neural networks (ANNs) with different structure are applied to diagnosis the fault respectively. The diagnosing results of the child ANNs are fused by the LS weighted fusion algorithm. The fault is identified based on the fused results at last. Compared with single neural network, the LS fusion combining network can identify fault type safely when the fault is deceptive, however, a single neural network may fail in this case. Furthermore, the combining neural network is more reliable than single neural network. The test results of power transformer fault diagnosis proved the conclusions.
Starting Page	154
Ending Page	158
File Size	247228
Page Count	5
File Format	PDF
ISBN	9780769528755
DOI	10.1109/ICNC.2007.379
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Fuzzy logic Gases Uncertainty Fuses Neural networks Artificial neural networks Dissolved gas analysis Power system reliability Power transformers
Content Type	Text
Resource Type	Article

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