| Title |
Diagnosis Method for Power Transformer using Intelligent Algorithm based on ELM and Fuzzy Membership Function |
| Authors |
임재윤(Lim, Jae-Yoon) ; 이대종(Lee, Dae-Jong) ; 지평식(Ji, Pyeong-Shik) |
| DOI |
https://doi.org/10.5370/KIEEP.2017.66.4.194 |
| Keywords |
Fault diagnosis ; Power transformer ; Fuzzy membership ; ELM(Extreem Learning Machine) |
| 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. |