Open AccessCombining and comparing various machine‐learning algorithms to improve dissolved gas analysis interpretation
Author(s) -
Senoussaoui Mohammed El Amine,
Brahami Mostefa,
Fofana Issouf
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.0059
Subject(s) - c4.5 algorithm , adaboost , machine learning , decision tree , algorithm , computer science , artificial intelligence , boosting (machine learning) , naive bayes classifier , dissolved gas analysis , ensemble learning , statistical classification , multilayer perceptron , interpretation (philosophy) , data mining , artificial neural network , support vector machine , engineering , programming language , transformer oil , voltage , electrical engineering , transformer
Since the discovery of dissolved gas analysis (DGA), it is considered as a leading technique for the diagnosis of liquid insulated power equipment. However, accurate analysis results can only be achieved if the measured gases closely reflect the actual equipment condition to enable an appropriate interpretation of these gases. In general, conventional techniques such as the ratio method, key gases, and Duval triangle combined or not with artificial intelligence techniques such as machine‐learning algorithms are used for DGA interpretation. Here, four well‐known machine‐learning algorithms are compared in terms of DGA fault classification – Bayes network, multilayer perceptron, k ‐nearest neighbour, and J48 decision tree. Moreover, the effect of applying ensemble methods such as boosting through the Adaboost algorithm and bootstrap aggregation (bagging) is analysed, and the performances of these algorithms are evaluated. The data for developing classification models was transformed into three forms, other than the raw data. The obtained results clearly presented the efficiency and stability of some algorithms such as the J48 tree and Bayes networks for DGA fault classification, in particular, when the data is appropriately pre‐processed. Moreover, the performance of these algorithms was found to consistently improve by integrating the concepts of multiple models or ensemble methods.