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The Application Research of Fault Diagnosis Technique of Operating Transformers Based on Artificial Neutral Network

Author: XuHaiYing
Tutor: ChenWeiGen；YangZhiGuo
School: Chongqing University
Course: Electrical Engineering
Keywords: Neural Network Condition Monitoring Dissolved gas analysis PD pattern recognition
CLC: TM407
Type: Master's thesis
Year: 2005
Downloads: 285
Quote: 0
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Abstract


This paper introduces the theory of neural network , BP neural network is applied to oil -based dissolved gas analysis (DGA) of transformer fault diagnosis and ART and BP neural network for partial discharge (PD) pattern recognition , to achieve operational status line transformer monitoring and fault diagnosis. Paper first discusses the neural network model and learning algorithm, according to the basic principle of BP neural network proposed two input methods BPNN diagnosis . Trained neural network can contain H 2 , CO gas components such as seven kinds of monitoring data for processing , and then gives a diagnosis. The new approach with the traditional IEC three-ratio method comparison showed that the neural network diagnostic method is superior . Partial discharge signals for analog and acquisition, by simulating the five kinds of electrodes PD forms , gives the corresponding experimental results . Introduces a statistical operator to extract the PD signal feature , a good reflection of the PD phase distribution characteristics of neural networks can be used as input feature vector . ART2 original algorithm for difficult applications very complex situation , proposed a simplified SART nets to deal with as the statistical operator input vector data . Application examples show that the neural network can well identify PD mode.

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CLC: > Industrial Technology > Electrotechnical > Transformers, converters and reactors > General issues > Maintenance,repair
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