集成基于学习的传输线故障分类使用相位测量单元 (PMU) 数据与可解释的AI (XAI)
Simon Bin Akter1, Tanmoy Sarkar Pias2, Shohana Rahman Deeba1
1Department of Electrical & Computer Engineering, North South University, Dhaka, Bangladesh.
PloS one
|February 12, 2024
概括
阶段测量单元 (PMU) 数据增强了输电线路故障分类. 机器学习模型,特别是组合模型,在使用电压,电流,频率和相角数据识别故障类型时,实现了超过99%的准确性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 机器学习应用 机器学习应用
背景情况:
- 阶段测量单元 (PMU) 数据为输电线路故障研究提供了对电网状态的新见解.
- 传统的电压和电流大小对于精确的故障分析是不够的,需要额外的参数.
研究的目的:
- 开发和评估机器学习模型,以使用全面的PMU数据准确地对输电线路故障进行分类.
- 调查集合学习和可解释AI (XAI) 在改善故障检测和可解释性方面的有效性.
主要方法:
- 使用ePMU DSA工具和Matlab Simulink生成合成传输线数据,包括电压,电流,频率和相角.
- 训练并比较单个机器学习模型 (决策树,随机森林,K-NN) 和使用软投票的整体模型.
- 应用可解释AI (XAI) 来解释模型预测和验证IEEE 14总线系统上的性能.
主要成果:
- 单个模型实现了高的交叉验证准确度 (DT: 99.84%,RF: 99.83%,K-NN: 99.76%).
- 整体模型表现出卓越的性能,交叉验证准确率为99.88%.
- 可解释的人工智能提供了关于不同参数对故障分类的影响的见解.
结论:
- 开发的整体模型有效地以高准确度对输电线路故障进行分类.
- 综合PMU数据和先进的机器学习技术显著提高了故障检测能力.
- 可解释的人工智能增强了故障分类模型的理解和可靠性.
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