Transformer Fault Prognosis Using Deep Recurrent Neural Network over Vibration Signals

Amin Zollanvari, Kassymzhomart Kunanbayev, Saeid Akhavan Bitaghsir, Mehdi Bagheri

Research output: Contribution to journalArticlepeer-review

117 Citations (Scopus)

Abstract

Vibration analysis is considered as a cost-efficient and nondestructive technique to monitor the transformer operating conditions and evaluate the transformer mechanical integrity. This method enables transformer fault prognosis before insulation catastrophic failure. In this work, the possibility of using deep neural networks in capturing the hidden patterns of vibration time series to predict the transformer underexcitation and overexcitation and interturn fault progress prediction in early stages is examined. This focus is warranted because deep learning techniques lend themselves to integrating the feature extraction into the predictive model construction stage. In this regard, deep recurrent neural network (RNN) architecture, including unidirectional and bidirectional gated recurrent units (GRUs) and long short-term memory (LSTM) models, are adopted. The constructed RNN for predicting excitation voltage exhibits a remarkable performance with a relative absolute error (RAE) of 0.56%. Predicting the interturn fault proved to be a more challenging problem and the constructed RNN for this purpose showed an RAE of 17.58%. The source code for implementing all constructed models is available at https://github.com/azollanvari/FaultPrognosisDL.

Original languageEnglish
Article number9205841
JournalIEEE Transactions on Instrumentation and Measurement
Volume70
DOIs
Publication statusPublished - 2021

Funding

Manuscript received March 7, 2020; accepted September 11, 2020. Date of publication September 25, 2020; date of current version November 20, 2020. This work was supported in part by Nazarbayev University Faculty Development Competitive Research Grant under Award SOE2018008 (Amin Zollanvari) and Award 090118FD5318 (Mehdi Bagheri). The Associate Editor coordinating the review process was Qiang Miao. (Corresponding author: Amin Zollanvari.) The authors are with the Department of Electrical and Computer Engineering, Nazarbayev University, Nur-Sultan 010000, Kazakhstan (e-mail: [email protected]).

Keywords

  • Deep Learning
  • recurrent neural network (RNN)
  • time series
  • transformer fault
  • vibration analysis
  • Machine Learning

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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