An integrated method of ANFIS and Dempster-Shafer theory for fault diagnosis of power transformer

• Published in: IEEE transactions on dielectrics and electrical insulation Vol. 25; no. 1; pp. 360 - 371
• Main Authors: Kari, Tusongjiang, Gao, Wensheng, Zhao, Dongbo, Zhang, Ziwei, Mo, Wenxiong, Wang, Yong, Luan, Le
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adaptive nuero fuzzy inference system; Adaptive systems; Artificial neural networks; Consistency; Dempster Shafer theory; Diagnostic systems; dissolved gas analysis; Fault detection; Fault diagnosis; Fuzzy logic; Fuzzy systems; Gas analysis; Noise measurement; Partial discharges; Power transformers; Reliability theory; transformer fault diagnosis; Transformers
• DOI: 10.1109/TDEI.2018.006746
• ISSN: 1070-9878

Dissolved gas analysis (DGA) approach is extensively applied to detect incipient faults of power transformers. This paper presents a novel DGA method for power transformer incipient fault diagnosis based on integrated adaptive neuro fuzzy inference system (ANFIS) and Dempster-Shafer Theory (DST). Four out of seven common conventional methods which are studied and compared for better consistency and accuracy, are used to develop new ANFIS based fault diagnosis models. To promote fault diagnostic performance further and make fault decision process more reliable and reasonable, an improved DST is introduced to integrate outputs of each ANFIS based model, and to provide comprehensive and convincing fault diagnosis results. The fault diagnosis capability of the proposed method is validated by a reported fault dataset and 10-fold cross validation experiment. The performance of the proposed method is compared with conventional approaches and ANFIS based models which demonstrate that the proposed method is superior to other methods and is more effective and stable for power transformer fault diagnosis with high accuracy and consistency.