Application of Parzen Window estimation for incipient fault diagnosis in power transformers. Issue 4 (1st December 2018)

• Record Type: Journal Article
• Authors: Islam, Md Mominul; Lee, Gareth; Hettiwatte, Sujeewa Nilendra
• Is Part Of: High voltage. Volume 3:Issue 4(2018)
• Page Start: 303
• Page End: 309
• Publication Date: 2018-12-01
• Subjects: learning (artificial intelligence) -- support vector machines -- neural nets -- power engineering computing -- fault diagnosis -- power transformers -- probability -- set theory; incipient fault diagnosis -- power transformers -- dissolved gas analysis -- Parzen window estimation -- transformers fault categories -- DGA -- Rogers ratio -- duval triangles method -- combustible hydrocarbon gas -- probability -- support vector machine -- artificial neural network -- rough sets analysis -- extreme learning machine; High voltages -- Periodicals; 621.3191
• DOI: 10.1049/hve.2018.5061
• Languages: English
• ISSNs: 2397-7264

Abstract

Abstract : Accurate faults diagnosis in power transformers is important for utilities to schedule maintenance and minimises the operation cost. Dissolved gas analysis (DGA) is one of the proven and widely accepted tools for incipient fault diagnosis in power transformers. To improve the accuracy and solve the cases that cannot be classified using Rogers' Ratios, IEC ratios and Duval triangles methods, a novel DGA technique based on Parzen window estimation have been presented in this study. The model uses the concentrations of five combustible hydrocarbon gases: methane, ethane, ethylene, acetylene and hydrogen to compute the probability of transformers fault categories. Performance of the proposed method has been evaluated against different conventional techniques and artificial intelligence‐based approaches such as support vector machines, artificial neural networks, rough sets analysis and extreme learning machines for the same set of transformers. A comparison with other soft computing approaches shows that the proposed method is reliable and effective for incipient fault diagnosis in power transformers.