An Innovative Electromechanical Joint Approach for Contact Pair Fault Diagnosis of Oil-Immersed On-Load Tap Changer

Abstract

This paper presents a novel fault diagnosis method for oil-immersed on-load tap changers (OLTC) to address the issue of limited diagnostic accuracy. The proposed method combines the analysis of mechanical vibration signals and high-frequency current signals from the contact pair, aiming to improve the precision of fault diagnosis. To begin with, an experimental platform was used to simulate the OLTC contact, enabling the collection of mechanical vibration signals and high-frequency current signals under different operational states. These signals underwent wavelet packet transform for denoising, followed by correlation analysis to investigate their interrelationships across various states. Features were then extracted and analyzed using ensemble empirical mode decomposition and the Hilbert–Huang transform. Subsequently, a support vector machine (SVM) was employed to analyze both the mechanical vibration signal and high-frequency current signal, facilitating the classification of the OLTC contact state. The results demonstrated that the joint analysis of electrical and mechanical signals provided a comprehensive representation of the actual contact state under different conditions. The SVM classification achieved an error below 10% in predicting the values of the two signal types, validating the efficiency and feasibility of the proposed fault diagnosis method for OLTC contacts. The findings presented in this paper offer valuable insights for on-site fault diagnosis of practical OLTCs.

OLTC Fault detection Based on Acoustic Emission and Supported by Machine Learning

“…The research results presented in this article confirm the possibility of diagnosing OLTC faults using EA signals. The effectiveness in determining the OLTC defects was similar to the results presented in the literature [21][22][23]. However, our research used a measurement database containing a much larger number of defects occurring during OLTC operation.…”

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

“…It should be noted that a few works have evaluated an OLTC's condition based on machine learning techniques and vibration signals [28,29]. To the best of the author's knowledge, deep learning, which has been used to achieve state-of-the-art performance in a wide range of problems, mimicking the learning process of the human brain, is being used for the first time in OLTC monitoring based on vibro-acoustic signal analyses.…”