利用机器学习检测和解释脆弱电网中的故障
Detecting and interpreting faults in vulnerable power grids with machine learning
摘要：
不定期的电力扰动会给用户和电网运营商带来严重后果。为了防范此类事件，有必要确定配电网络中断的原因。在这项工作中，我们专注于挪威社区在北极的电网，经历了几个故障，其来源是未知的。首先，我们构建了一个数据集，包括相关的气象数据和信息记录的电能质量计的当前电能质量。然后，我们采用机器学习技术来预测故障的发生。实验结果表明，线性和非线性分类器都取得了良好的分类性能。这表明所考虑的电能质量和天气变量很好地解释了电力扰动。解释分类器的决策过程为理解干扰的主要原因提供了有价值的见解。传统的特征选择方法只能指出哪些是平均而言主要解释数据集中故障发生的变量。除了提供这样的全局解释外，识别解释每个单独故障的特定变量集也很重要。为了应对这一挑战，我们采用了一种最新的技术来解释深度学习模型的决策过程，称为集成决策。所提出的方法可以获得详细的见解发生的特定故障，这是有价值的配电系统运营商实施战略，以防止和减轻电力扰动。
英文摘要：
Unscheduled power disturbances cause severe consequences both for customers and grid operators. To defend against such events, it is necessary to identify the causes of interruptions in the power distribution network. In this work, we focus on the power grid of a Norwegian community in the Arctic that experiences several faults whose sources are unknown. First, we construct a data set consisting of relevant meteorological data and information about the current power quality logged by power-quality meters. Then, we adopt machine-learning techniques to predict the occurrence of faults. Experimental results show that both linear and non-linear classifiers achieve good classification performance. This indicates that the considered power-quality and weather variables explain well the power disturbances. Interpreting the decision process of the classifiers provides valuable insights to understand the main causes of disturbances. Traditional features selection methods can only indicate which are the variables that, on average, mostly explain the fault occurrences in the dataset. Besides providing such a global interpretation, it is also important to identify the specific set of variables that explain each individual fault. To address this challenge, we adopt a recent technique to interpret the decision process of a deep learning model, called Integrated Gradients. The proposed approach allows to gain detailed insights on the occurrence of a specific fault, which are valuable for the distribution system operators to implement strategies to prevent and mitigate power disturbances.
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