将故障检测和分类集成到微电网中,使用监督机器学习,考虑到故障阻力不确定性.
Morteza Barkhi1, Javad Pourhossein2, Seyed Ali Hosseini1
1Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran.
Scientific reports
|November 18, 2024
概括
本研究介绍了使用支持矢量机器 (SVM) 的微电网 (MGs) 的自适应性保护方案. 该方法通过准确检测和分类故障,以99.75%的准确度确保可靠的电力,即使在不确定的可再生能源中.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
背景情况:
- 微电网 (MG) 为消费者提供了更高的可靠性,但由于间歇性的可再生能源 (RES) 而面临保护挑战.
- 在MG中非辐射运行需要强有力的保护方案,以考虑到可再生能源发电和网络条件的不确定性.
研究的目的:
- 开发适应性强,可靠的微电网保护方案.
- 解决因可再生能源的间歇性和网络不确定性所带来的挑战.
主要方法:
- 利用局部测量的数据分析,特别是对称元件的RMS值.
- 采用基于学习的方法,使用支持矢量机 (SVM) 进行故障检测和分类.
- 在MV测试网络上模拟了各种故障场景,操作模式,RES不确定性和负载水平.
主要成果:
- 在区分故障和正常条件时,达到99.75%的高准确率.
- 证明了拟议方法在准确识别不同系统模式方面的有效性.
- 验证了自适应保护方法在不同条件下始终保护MG的能力.
结论:
- 拟议的基于SVM的自适应保护方案为微电网提供了可靠的解决方案.
- 该方法只需要本地信息,不需要通信基础设施.
- 该方案在各种操作场景中提供了不受选择性约束的最佳保护解决方案.
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