[논문]Power Transformer Diagnosis Using a Modified Self Organizing Map

Lee J. P.; Ji P. S.; Lim J. Y.; Kim S. S.

Power Transformer Diagnosis Using a Modified Self Organizing Map 원문보기

KIEE international transactions on power engineering, v.5A no.1, 2005년, pp.40 - 45

Lee J. P. (Dept. of Electrical Engineering, Chungbuk National University) , Ji P. S. (Dept. of Electrical Engineering, Chungju National University) , Lim J. Y. (Dept. of Electrical Engineering, Daeduk College) , Kim S. S. (Dept. of Electrical Engineering, Chungbuk National University)

Abstract ▼ AI-Helper

Substation facilities have become extremely large and complex parts of electric power systems. The development of condition monitoring and diagnosis techniques has been a very significant factor in the improvement of substation transformer security. This paper presents a method to analyze the cause, the degree, and the aging process power transformers by the Self Organizing Map (SOM) method. Dissolved gas data were non-linearly transformed by the sigmoid function in SOM that works much the same way as the human decision making process. The potential for failure and the degree of aging of normal transformers are identified by using the proposed quantitative criterion. Furthermore, transformer aging is monitored by the proposed criterion for a set of transformers. To demonstrate the validity of the proposed method, a case study is performed and its results are presented.

주제어

AI 본문요약
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제안 방법

In this paper, a new approach based on SOM (Self Organizing Map) is introduced to analyze the causes, the degree and the process of aging. The input layer of SOM is changed to obtain a decision making rule that is similar to the human's.
In this research, input data are non-linearly transformed using a sigmoid function in the input layer. It can be approached much like the decision making process of a human, just as the membership function in the fuzzy theory.
This paper has proposed a method to analyze the cause, the degree, and the process of aging of power transformers by the self organizing map (SOM). Dissolved gas data were non-linearly transformed by a sigmoid fiinction to determine SOM, which is similar to the process of human determination.

대상 데이터

Following the non-linear transformation process, SOM is trained. The training data consist of 963 patterns. The output layer as a 2 dimensionalplane made of 10 by 10 neurons.

참고문헌 (16)

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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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