ArticleSensors (Basel, Switzerland)2023
Power Transformers OLTC Condition Monitoring Based on Feature Extraction from Vibro-Acoustic Signals: Main Peaks and Euclidean Distance.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MoEKAN: Multi-Scale Transformer-Based Gating KAN Experts Network for Time Series Forecasting.Sensors (Basel, Switzerland) · 2025Article
- U-AttentionFlow: A Multi-Scale Invertible Attention Network for OLTC Anomaly Detection Using Acoustic Signals.Sensors (Basel, Switzerland) · 2025Article
- On the Feasibility of Detecting Faults and Irregularities in On-Load Tap Changers (OLTCs) by Vibroacoustic Signal Analysis.Sensors (Basel, Switzerland) · 2024Article
- Analysis and Optimization of Contact Material Ablation under the Cumulative Effect of the Number of Breakings of OLTC.Materials (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
The detection of On-Load Tap-Changer (OLTC) faults at an early stage plays a significant role in the maintenance of power transformers, which is the most strategic component of the power network substations. Among the OLTC fault detection methods, vibro-acoustic signal analysis is known as a performant approach with the ability to detect many faults of different types. Extracting the characteristic features from the measured vibro-acoustic signal envelopes is a promising approach to precisely diagnose OLTC faults. The present research work is focused on developing a methodology to detect, locate, and track changes in on-line monitored vibro-acoustic signal envelopes based on the main peaks extraction and Euclidean distance analysis. OLTC monitoring systems have been installed on power transformers in services which allowed the recording of a rich dataset of vibro-acoustic signal envelopes in real time. The proposed approach was applied on six different datasets and a detailed analysis is reported. The results demonstrate the capability of the proposed approach in recognizing, following, and localizing the faults that cause changes in the vibro-acoustic signal envelopes over time.
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Registered trials
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