基于INFO-RF的故障诊断和分析方法用于巴斯巴.
Chen Xue1, Jian Zhu2, Haiou Cao3
1State Grid Jiangsu Electric Power Co., Ltd., Yangzhou Power Supply Company, Yangzhou, 225009, China. xchen232@tutamail.com.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种INFO-RF模型,用于精确的总线轴故障诊断和电力系统中电阻预测. 该方法通过提高故障识别准确度,显著提高了电网安全性和稳定性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 网格中的人工智能
背景情况:
- 电力系统的规模和智能正在迅速扩大.
- 准确和及时的总线杆故障诊断对于电网稳定性至关重要.
- 对于复杂的电力系统,现有的方法需要改进.
研究的目的:
- 开发一个先进的方法,用于总线条故障诊断和阻力预测.
- 提高智能电网故障分析的准确性和效率.
- 通过精确的故障识别,提高电力系统运行的可靠性.
主要方法:
- 建立了一个双杆动力系统模拟模型.
- 使用根均方 (RMS) 提取了关键的电量 (差电流,总线连接电流,电压).
- 矢量的加权平均值 (INFO) 算法优化了随机森林 (RF) 超参数,以改善预测.
主要成果:
- 在测试组中,INFO-RF模型获得了98.472%的准确性.
- 该模型准确地识别了总线条故障类型,并预测了故障阻力.
- 性能明显超过了BP神经网络,GRNN和决策树等传统方法.
结论:
- INFO-RF模型为总线条故障诊断提供了一个高度准确和有效的解决方案.
- 这种方法为电力系统的故障定位和维护提供了至关重要的支持.
- 该方法有助于确保日益复杂的电网的安全和稳定运行.
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