基于ACGAN和CGWO-LSSVM的变压器故障诊断方法的研究
Shan Guan1, Tong-Yu Wu2, Hai-Qi Yang1
1School of Mechanic Engineering, Northeast Electric Power University, Jilin, 132012, China.
Scientific reports
|July 31, 2024
概括
本研究介绍了一种使用ACGAN和CGWO-LSSVM的先进变压器故障诊断方法. 该方法提高了诊断的准确性,特别是在不平衡的数据集,达到97.66%的准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 变压器故障诊断对于电力系统可靠性至关重要.
- 小而不均分布的故障样本导致误判和精度低.
- 现有的方法与不平衡的数据集作斗争.
研究的目的:
- 为不平衡的数据集开发一个强大的变压器故障诊断方法.
- 提高诊断准确度,降低误判率.
- 为了解决传统故障诊断技术的局限性.
主要方法:
- 使用辅助分类器生成对抗网络 (ACGAN) 来增强不平衡样本的数据.
- 采用非编码比率和内核主要组件分析 (KPCA) 用于溶解气体特征结构和特征融合.
- 使用立方灰狼优化 (CGWO) 算法优化最小方位支向量机 (LSSVM) 参数.
主要成果:
- 实现了97.66%的诊断准确率,低误报率.
- 与IGOA-LSSVM,ICHOA-LSSVM和PSO-LSSVM方法相比,已经证明了更高的性能.
- 有效地解决了由于样本分布小而不均而导致的错误判断和低准确性问题.
结论:
- 拟议的ACGAN和CGWO-LSSVM方法为变压器故障诊断提供了一个优越的解决方案.
- 这种技术非常适合在不平衡变压器数据集上的多分类故障诊断.
- 该方法显著提高了电力系统的诊断可靠性和准确性.
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