Data mining for oil-insulated power transformers: an advanced literature survey

Authors:
Yann-Chang Huang;Chao-Ming Huang;Huo-Ching Sun
Affiliations:
Department of Electrical Engineering, Cheng Shiu University, Kaohsiung, Taiwan;Department of Electrical Engineering, Kun San University, Tainan, Taiwan;Department of Electrical Engineering, Cheng Shiu University, Kaohsiung, Taiwan
Venue:
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Year:
2012

Citing 7
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Abstract

Knowledge discovery in database and data mining (DM) have emerged as high profile, rapidly evolving, urgently needed, and highly practical approaches to use dissolved gas analysis (DGA) data to monitor conditions and faults in oil-immersed power transformers. This study reviews different DM approaches to oil-immersed power transformer maintenance by discussing historical developments and presenting state-of-the-art DM methods. Relevant publications covering a broad range of artificial intelligence methods are reviewed. Current approaches to the latter method are discussed in the field of DM for oil-immersed power transformers. In this paper, various DM approaches are discussed, including expert systems, fuzzy logic, neural networks, classification and decision, and hybrid intelligent-based diagnostic systems that apply the DGA database. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.