ELM 기반의 지능형 알고리즘과 퍼지 소속함수를 이용한 유입변압기 고장진단 기법

다국어 초록 (Multilingual Abstract)

Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can’t diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 용존가스 분석법을 이용한 유입변압기 진단방법
• 3. 지능형 알고리즘과 퍼지 융합규칙을 이용한 유입변압기 진단기법
• 4. 실험결과
• 5. 결론
• References