Open AccessApplication of Parzen Window estimation for incipient fault diagnosis in power transformers
Author(s) -
Islam Md Mominul,
Lee Gareth,
Hettiwatte Sujeewa Nilendra
Publication year - 2018
Publication title -
high voltage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.732
H-Index - 20
ISSN - 2397-7264
DOI - 10.1049/hve.2018.5061
Subject(s) - dissolved gas analysis , transformer , artificial neural network , computer science , support vector machine , electric power system , reliability engineering , engineering , artificial intelligence , pattern recognition (psychology) , power (physics) , voltage , transformer oil , electrical engineering , physics , quantum mechanics
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.