通过整体机器学习模型在输电线路中进行强大的故障检测和分类
Tahir Anwar1, Chaoxu Mu1, Muhammad Zain Yousaf2,3
1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
Scientific reports
|January 20, 2025
概括
本研究介绍了RF-LSTM Tuned KNN用于先进的输电线路故障检测. 这种新的方法达到99.96%的准确性,显著提高了电网的可靠性和稳定性.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
背景情况:
- 输电线路对于发电至关重要,但容易发生故障.
- 故障会破坏电力供应,并造成安全隐患.
研究的目的:
- 开发一种用于精确检测和分类输电线路故障的新方法.
- 提高电网的稳定性和可靠性.
主要方法:
- 分析传输线路各阶段的电压和电流模式.
- 机器学习算法的评估:随机森林 (RF),K-最近邻居 (KNN) 和长短期记忆 (LSTM) 网络.
- 组合方法的建议:RF-LSTM 调节KNN.
主要成果:
- 在多标签故障分类中,RF-LSTM Tuned KNN实现了99.96%的准确性.
- 在二进制分类中,KNN和RF表现出高精度 (分别为99.85%和99.72%).
- 拟议的整体方法在检测准确性和稳定性方面优于单个算法.
结论:
- 该RF-LSTM调节KNN方法在传输线路故障检测方面取得了重大进展.
- 这种方法为提高电网可靠性和稳定性提供了宝贵的见解.
- 这些发现有助于确保更具弹性和安全的电力供应.
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