access icon free Multiple incipient fault classification approach for enhancing the accuracy of dissolved gas analysis (DGA)

© The Institution of Engineering and Technology
Received 19/04/2018, Accepted 12/03/2019, Revised 02/02/2019, Published 14/03/2019

Multiple incipient faults are practically known to exist in transformers. They tend to produce suddenly changing ratio limits in ratio-based methods or oscillation of fault location in graphical methods. In consequence, the energy associated with them lies in-between low and high severity single faults. Hence multiple fault detection needs to be addressed appropriately which may otherwise pose the serious constraints during transformer condition monitoring. In this study, novel and intelligent classification approach is proposed to upgrade the classical dissolved gas analysis (DGA) technique to cater the requirement of multiple fault diagnosis. This consists of Duval-triangle-based optimised fuzzy inference system and neural network models sensitive to both single and multiple incipient faults. Both models have been rigorously trained and tested using dataset credited to field and literatures to achieve high fault recognition and isolation rates, alternatively low false detection and no-detection rates. Both parameters are combined into single index to determine the accuracy in terms of F1 score which is evaluated to be >97%. The diagnostic ability of the scheme is highly promising and can improve reliability of transformer fault forecasting by DGA.

Inspec keywords: fault location; power transformers; condition monitoring; fuzzy reasoning; power engineering computing; fault diagnosis; neural nets

Other keywords: ratio-based methods; low severity single faults; novel; multiple incipient fault classification approach; transformer condition monitoring; fault location; intelligent classification approach; multiple incipient faults; Duval-triangle-based optimised fuzzy inference system; multiple fault detection; alternatively low false detection; isolation rates; multiple fault diagnosis; suddenly changing ratio limits; classical dissolved gas analysis technique; high severity single faults; transformer fault forecasting; graphical methods; DGA; high fault recognition

Subjects: Power engineering computing; Transformers and reactors; Knowledge engineering techniques; Neural computing techniques

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