通过双线逻辑回归方法在智能电网中建模故障.
Enrico De Santis1, Antonello Rizzi1
1Department of Information Engineering, Electronics and Telecommunications, University of Rome "La Sapienza", Via Eudossiana 18, Rome, 00184, Italy.
概括
本研究介绍了一种双线逻辑回归模型,用于准确和可解释的电网故障分析. 可解释的AI方法有助于对复杂系统进行预测性维护和风险评估.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 对事件识别的复杂系统进行建模具有挑战性,尤其是在平衡准确性和可解释性时.
- 现有的机器学习模型往往缺乏可解释性,这阻碍了它们在电网等关键基础设施中的应用.
研究的目的:
- 开发一种可解释且准确的机器学习模型,用于分析电网故障.
- 为了更好地了解故障现象,将外部事件与电网特征相关联.
- 通过可解释的AI范式来实现预测性维护,状态监测和风险评估.
主要方法:
- 使用数据驱动的方法与双线逻辑回归模型.
- 在特定的神经架构中将模型接地,创建一个白盒系统.
- 在真实世界的电网故障数据数据集上训练模型.
主要成果:
- 双线白盒模型在识别错误状态方面实现了与现有分类器相提并论的性能.
- 该模型的低计算复杂性促进了对故障现象和事件相关性的洞察.
- 对电网组件估计了一个漏洞向量,作为一个可解释的"标签".
结论:
- 拟议的双线模型为准确和可解释的电网故障分析提供了一个强大的工具.
- 它有效地揭示了外源原因和网格特征之间的关系信息.
- 该模型支持先进的应用程序,如预测性维护,风险评估和情景分析在可解释的AI框架内.
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