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

IF 3 4区工程技术 Q3 ENERGY & FUELS

Energies Pub Date : 2023-12-31 DOI:10.3390/en17010220

Andrzej Cichoń, Michał Włodarz

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引用次数: 0

Abstract

Power transformers are an essential part of the power grid. They have a relatively low rate of failure, but removing the consequences is costly when it occurs. One of the elements of power transformers that are often the reason for shutting down the unit is the on-load tap changer (OLTC). Many methods have been developed to assess the technical condition of OLTCs. However, they require the transformer to be taken out of service for the duration of the diagnostics, or they do not enable precise diagnostics. Acoustic emission (AE) signals are widely used in industrial diagnostics. The generated signals are difficult to interpret for complex systems, so artificial intelligence tools are becoming more widely used to simplify the diagnostic process. This article presents the results of research on the possibility of creating an online OLTC diagnostics method based on AE signals. An extensive measurement database containing many frequently occurring OLTC defects was created for this research. A method of feature extraction from AE signals based on wavelet decomposition was developed. Several machine learning models were created to select the most effective one for classifying OLTC defects. The presented method achieved 96% efficiency in OLTC defect classification.

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基于声发射并辅以机器学习的有载分接开关故障检测

电力变压器是电网的重要组成部分。它们的故障率相对较低，但一旦发生故障，排除故障的代价非常高昂。有载分接开关（OLTC）是电力变压器中经常导致设备停运的元件之一。目前已开发出许多方法来评估有载分接开关的技术状况。但是，这些方法要求变压器在诊断期间停止运行，或者无法进行精确诊断。声发射（AE）信号被广泛应用于工业诊断。对于复杂的系统来说，所产生的信号很难解释，因此人工智能工具正被越来越广泛地用于简化诊断过程。本文介绍了基于声发射信号创建在线有载分接开关诊断方法的可能性研究成果。为开展这项研究，我们创建了一个广泛的测量数据库，其中包含许多经常出现的有载分接开关缺陷。开发了一种基于小波分解的 AE 信号特征提取方法。创建了多个机器学习模型，以选择最有效的模型对 OLTC 缺陷进行分类。所提出的方法在 OLTC 缺陷分类中达到了 96% 的效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Energies ENERGY & FUELS-

CiteScore

6.20

自引率

21.90%

发文量

8045

审稿时长

1.9 months

期刊介绍： Energies (ISSN 1996-1073) is an open access journal of related scientific research, technology development and policy and management studies. It publishes reviews, regular research papers, and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.