基于DGA方法的变压器故障诊断智能系统.
Saad A Mohamed Abdelwahab1, Ibrahim B M Taha2, Rizk Fahim3
1Electrical Department, Faculty of Technology and Education, Suez University, P.O.BOX: 43221, Suez, Egypt. Saad.Abdelwahab@suezuniv.edu.eg.
Scientific reports
|March 11, 2025
概括
这项研究引入了变压器故障诊断智能系统 (TFDIS),以改善功率变压器故障检测. TFDIS集成了多种溶解气体分析 (DGA) 方法,实现了比单个技术更高的诊断准确性.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 人工智能的人工智能
背景情况:
- 电力变压器是关键基础设施;故障会给公用事业公司造成重大经济损失.
- 变压器故障源于对绝缘 (油和纸) 的电气,热和机械应力.
- 溶气分析 (DGA) 是故障检测的标准,但传统方法 (IEC 代码,罗杰斯比率,杜瓦尔三角) 的准确性有限.
研究的目的:
- 开发一个先进的智能系统,用于增强功率变压器故障诊断.
- 提高现有的溶气分析 (DGA) 方法的诊断准确度.
- 通过整合多种DGA技术,创建一个变压器故障诊断智能系统 (TFDIS).
主要方法:
- 开发了一个变压器故障诊断智能系统 (TFDIS).
- 来自四种DGA方法的综合输出:代码树2020,修改的IEC,罗杰斯比率和神经模式识别.
- 将综合系统的诊断准确度与单个方法进行了比较.
主要成果:
- 开发的TFDIS的诊断准确率达到了89.12%.
- 这一准确度超过了神经模式识别获得的最高个体准确率86.01%.
- 综合方法显著提高了诊断变压器故障的分析准确性.
结论:
- 变压器故障诊断智能系统 (TFDIS) 与个人DGA方法相比,提供了更高的故障诊断准确性.
- 将多个DGA技术集成到智能系统框架中,可以有效地进行可靠的功率变压器健康监测.
- TFDIS在确保电力系统可靠性和减少公用事业损失方面取得了重大进展.
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