基于PCA和SSA-LightGBM的浸油变压器故障诊断方法
Jizhong Wang1, Jianfei Chi1, Yeqiang Ding1
1State Grid Zhejiang Electric Power Co., Ltd., Hangzhou, China.
PloS one
|February 19, 2025
概括
本研究引入了一种新的故障诊断方法,用于油浸式变压器,使用主要组件分析和SSA-LightGBM. 优化的模型达到93.6%的准确性,显著改善了变压器故障检测.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 机器学习应用 机器学习应用
背景情况:
- 浸油变压器的复杂故障降低了诊断的准确性.
- 当前的诊断方法与复杂的故障模式作斗争.
研究的目的:
- 为浸油变压器开发准确有效的故障诊断方法.
- 通过先进的机器学习技术来提高诊断性能.
主要方法:
- 从浸油变压器收集的溶解气体数据.
- 构建了一个17维的故障特征矩阵并将其标准化.
- 应用主要组件分析 (PCA) 用于特征融合.
- 使用SSA-LightGBM开发和优化了一个变压器诊断模型.
- 通过十倍交叉验证验证模型.
主要成果:
- 在优化后,SSA-LightGBM模型实现了93.6%的平均故障诊断准确度.
- 优化的SSA-LightGBM显示比未优化的模型有3.6%的改进.
- SSA-LightGBM的表现优于GA-LightGBM (8.1%的改善) 和GWO-LightGBM (5.7%的改善).
结论:
- 拟议的SSA-LightGBM方法有效地提高了油浸式变压器的故障诊断性能.
- 与其他当代故障诊断模型相比,这种方法显示出更高的准确性.
- 集成PCA和SSA-LightGBM为变压器故障检测提供了一个强大的解决方案.
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