研究变压器故障诊断模型与特征提取的研究
Yongcan Zhu1, Zhenyan Guo1, Xiaoxuan Zhan2
1College of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, China.
The Review of scientific instruments
|November 25, 2024
概括
本研究介绍了一种人工蜂鸟算法 (AHA) 优化的内核主要组件分析 (KPCA) 和极端学习机器 (ELM) 用于变压器故障诊断. 新的AHA-KPCA-ELM方法显著提高了诊断准确度,达到95.73%.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的变压器故障诊断算法的准确性很低.
- 优化特征提取和分类模型对于增强诊断性能至关重要.
研究的目的:
- 开发一种新且高度准确的变压器故障诊断方法.
- 利用人工蜂鸟算法 (AHA) 来优化内核主要组件分析 (KPCA) 和极端学习机器 (ELM).
主要方法:
- 使用的气体度比特征用于变压器故障诊断.
- 应用了AHA来优化KPCA内核参数,创建了一个AHA-KPCA特征提取模型.
- 雇佣AHA优化ELM输入权重和隐藏层偏差用于AHA-ELM分类模型.
- 输入AHA-KPCA提取的特征进入AHA-ELM模型进行故障分类.
主要成果:
- 拟议的AHA-KPCA-ELM方法实现了95.73%的诊断准确性.
- 这种准确性超过了传统的智能诊断方法和现有的先进算法.
- 该方法有效地提取关键特征,并高精度地分类变压器故障.
结论:
- AHA-KPCA-ELM方法在变压器故障诊断方面表现出卓越的有效性.
- 使用AHA优化KPCA和ELM显著提高了诊断准确度.
- 这种新的方法为可靠的电力变压器监控提供了一个有希望的解决方案.
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