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Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features

Author

Listed:
  • Fataneh Dabaghi-Zarandi

    (Canada Research Chair Tier 1 in Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

  • Vahid Behjat

    (Canada Research Chair Tier 1 in Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

  • Michel Gauvin

    (Hydro-Québec’s Research Institute (IREQ), Varennes, QC J3X 1S1, Canada)

  • Patrick Picher

    (Hydro-Québec’s Research Institute (IREQ), Varennes, QC J3X 1S1, Canada)

  • Hassan Ezzaidi

    (Canada Research Chair Tier 1 in Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

  • Issouf Fofana

    (Canada Research Chair Tier 1 in Aging of Oil-Filled Equipment on High Voltage Lines (ViAHT), Department of Applied Sciences (DSA), University of Quebec at Chicoutimi (UQAC), Saguenay, QC G7H 2B1, Canada)

Abstract

An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In recent years, Hydro Quebec initiated a project to monitor the OLTC’s condition in power transformers using vibro-acoustic signals. A data acquisition system has been installed on real OLTCs, which has been continuously measuring their generated vibration signal envelopes over the past few years. In this work, the multivariate deep autoencoder, a reconstruction-based method for unsupervised anomaly detection, is employed to analyze the vibration signal envelopes generated by the OLTC and detect abnormal behaviors. The model is trained using a dataset obtained from the normal operating conditions of the transformer to learn patterns. Subsequently, kernel density estimation (KDE), a nonparametric method, is used to fit the reconstruction errors (regarding normal data) obtained from the trained model and to calculate the anomaly scores, along with the static threshold. Finally, anomalies are detected using a deep autoencoder, KDE, and a dynamic threshold. It should be noted that the input variables responsible for anomalies are also identified based on the value of the reconstruction error and standard deviation. The proposed method is applied to six different real datasets to detect anomalies using two distinct approaches: individually on each dataset and by comparing all six datasets. The results indicate that the proposed method can detect anomalies at an early stage. Also, three alarms, including ignorable anomalies, long-term changes, and significant alterations, were introduced to quantify the OLTC’s condition.

Suggested Citation

  • Fataneh Dabaghi-Zarandi & Vahid Behjat & Michel Gauvin & Patrick Picher & Hassan Ezzaidi & Issouf Fofana, 2024. "Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features," Energies, MDPI, vol. 17(7), pages 1-20, March.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1665-:d:1367529
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    References listed on IDEAS

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    1. Alexander Dokumentov & Rob J. Hyndman, 2015. "STR: A Seasonal-Trend Decomposition Procedure Based on Regression," Monash Econometrics and Business Statistics Working Papers 13/15, Monash University, Department of Econometrics and Business Statistics.
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