塔克尔基于分解的特征选择和SSA优化的多核SVM用于变压器故障诊断
1School of Automation, Shenyang Aerospace University, Shenyang 110136, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了一种智能框架,用于电力变压器故障诊断,使用先进的功能工程和一个子搜索算法优化的支持矢量机器. 该方法显著提高了变压器状况监测的准确性和电网可靠性.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 电力变压器故障诊断对于电网可靠性至关重要.
- 传统的溶气分析 (DGA) 方法在特征表示和高维数据方面存在困难.
- 现有的技术在复杂的故障分类场景中往往缺乏准确性.
研究的目的:
- 开发一个智能诊断框架,用于准确的功率变压器故障分类.
- 克服DGA数据的特征工程和维度减小方面的局限性.
- 通过优化机器学习模型,提高故障诊断系统的性能.
主要方法:
- 系统特征工程将DGA数据从5D扩展到12D,使用IEC 60599比率和统计描述符.
- 张量分解 (塔克) 将特征维度从12降低到7,保留了95%的区分信息.
- 小搜索算法 (SSA) 优化了一个多核支持向量机器 (MKSVM) 与 RBF,多项式和 sigmoid 内核.
主要成果:
- 拟议的框架在七个故障类别中实现了98.33%的分类准确性.
- 与Kernel PCA,深度学习和组合方法相比,表现出优异的性能.
- 在维度缩小过程中成功保留了重要的歧视性信息.
结论:
- 综合框架为变压器状态监控提供了可靠和准确的解决方案.
- 特性工程,张量分解和SSA优化的MKSVM的协同方法提高了诊断能力.
- 这一进步有助于提高电力系统的可靠性和运行安全.
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