Title page for ETD etd-08102000-21510032


Type of Document Dissertation
Author Wang, Zhenyuan
URN etd-08102000-21510032
Title Artificial Intelligence Applications in the Diagnosis of Power Transformer Incipient Faults
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Liu, Yilu Committee Chair
Broadwater, Robert P. Committee Member
De La Ree Lopez, Jaime Committee Member
Nussbaum, Maury A. Committee Member
VanLandingham, Hugh F. Committee Member
Keywords
  • Power Transformer
  • Fault Diagnosis
  • Artificial Intelligence
  • Dissolved Gas-in-oil Analysis (DGA)
  • Neural Network
  • Fuzzy System
  • Expert System
Date of Defense 2000-08-08
Availability unrestricted
Abstract
This dissertation is a systematic study of artificial intelligence (AI) applications for the diagnosis of power transformer incipient fault.

The AI techniques include artificial neural networks (ANN, or briefly neural networks - NN), expert systems, fuzzy systems and multivariate regression.

The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). A literature review showed that the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC) and the key gas method, have limitations such as the "no decision" problem. Various AI techniques may help solve the problems and present a better solution.

Based on the IEC 599 standard and industrial experiences, a knowledge-based inference engine for fault detection was developed. Using historical transformer failure data from an industrial partner, a multi-layer perceptron (MLP) modular neural network was identified as the best choice among several neural network architectures. Subsequently, the concept of a hybrid diagnosis was proposed and implemented, resulting in a combined neural network and expert system tool (the ANNEPS system) for power transformer incipient diagnosis. The abnormal condition screening process, as well as the principle and algorithms of combining the outputs of knowledge based and neural network based diagnosis, were proposed and implemented in the ANNEPS. Methods of fuzzy logic based transformer oil/paper insulation condition assessment, and estimation of oil sampling interval and maintenance recommendations, were also proposed and implemented.

Several methods of power transformer incipient fault location were investigated, and a 7×21×5 MLP network was identified as the best choice. Several methods for on-load tap changer (OLTC) coking diagnosis were also investigated, and a MLP based modular network was identified as the best choice. Logistic regression analysis was identified as a good auditor in neural network input pattern selection processes.

The above results can help developing better power transformer maintenance strategies, and serve as the basis of on-line DGA transformer monitors.

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  Appendix.pdf 102.30 Kb 00:00:28 00:00:14 00:00:12 00:00:06 < 00:00:01
  Chapter0.pdf 102.97 Kb 00:00:28 00:00:14 00:00:12 00:00:06 < 00:00:01
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  Chapter2.pdf 146.37 Kb 00:00:40 00:00:20 00:00:18 00:00:09 < 00:00:01
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  Chapter6.pdf 238.22 Kb 00:01:06 00:00:34 00:00:29 00:00:14 00:00:01
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  Chapter8.pdf 146.53 Kb 00:00:40 00:00:20 00:00:18 00:00:09 < 00:00:01
  Chapter9.pdf 69.32 Kb 00:00:19 00:00:09 00:00:08 00:00:04 < 00:00:01
  Reference.pdf 106.52 Kb 00:00:29 00:00:15 00:00:13 00:00:06 < 00:00:01

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