在负载影响下转换器变压器的语音印记故障诊断基于多策略改进的Mel频谱系数和时间卷积网络
1School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
一种新方法通过改进的Mel频谱系数 (MFCC) 和时间卷积网络来增强转换器变压器故障诊断. 这种方法达到99%的准确性,大大提高了有效诊断的识别和训练速度.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 传统的转换器变压器故障诊断面临着低识别准确度和有效诊断的挑战.
- 现有的方法很难解释诸如负载变化之类的关键因素.
研究的目的:
- 为转换器变压器提出一种新的,高度准确的故障诊断方法.
- 通过集成先进的信号处理和机器学习技术来提高识别准确性和诊断效率.
主要方法:
- 使用多策略改进的Mel频谱系数 (MFCC) 进行语音印记信号特征提取.
- 采用了改进的猎人猎物优化器 (IHPO) 与变化模式分解 (VMD) 进行信号消噪.
- 开发了一个改进的时卷积网络 (IHPO-ITCN),具有Mish激活功能,用于自适应的内核大小和层优化.
- 引入负载信号细分,以形成用于全面诊断的联合特征向量.
主要成果:
- 拟议的IHPO-ITCN模型实现了高达99%的故障识别精度.
- 与传统的语音印记诊断模型相比,在识别准确度方面取得了显著的改进.
- 在训练速度方面表现出卓越的性能,表明提高了效率.
结论:
- 这种新的方法在转换器变压器故障诊断方面取得了重大进展.
- 高精度和高效率使其适用于多重故障诊断中的现实应用.
- 集成先进的信号处理和自适应神经网络提供了一个强大的诊断解决方案.
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