用因果图+图神经网络精准识别电网故障并解释原因
Causality and Interpretability for Electrical Distribution System faults
- 构建电压电流等特征的因果图，用图神经网络分类故障
- 在电网故障数据集上达到99.44%准确率，优于现有模型
- 结合解释工具揭示关键影响特征，适合电力系统运维人员
因果分析有助于识别导致系统故障的关键变量，提升故障检测能力并增强系统可靠性。本文提出一种将因果推断与机器学习结合的新方法，利用基于图的模型对配电系统（EDS）故障进行分类。首先通过转移熵（TE）构建因果图，每个故障案例表示为图结构，节点代表电压、电流等特征，边表示特征间的动态影响关系。随后使用机器学习与GraphSAGE模型，从节点值和图结构中联合学习以预测故障类型。为提升可解释性，进一步引入GNNExplainer与Captum的集成梯度方法，定位对预测影响最大的关键特征节点，从而揭示故障可能成因。实验表明，该方法在EDS故障数据集上取得99.44%的准确率，优于现有先进模型。结合因果图与机器学习，不仅实现高精度故障预测，还能提供根因解释，是提升系统可靠性的实用工具。
原文摘要 · Abstract (English)
Causal analysis helps us understand variables that are responsible for system failures. This improves fault detection and makes system more reliable. In this work, we present a new method that combines causal inference with machine learning to classify faults in electrical distribution systems (EDS) using graph-based models. We first build causal graphs using transfer entropy (TE). Each fault case is represented as a graph, where the nodes are features such as voltage and current, and the edges demonstrate how these features influence each other. Then, the graphs are classified using machine learning and GraphSAGE where the model learns from both the node values and the structure of the graph to predict the type of fault. To make the predictions understandable, we further developed an integrated approach using GNNExplainer and Captums Integrated Gradients to highlight the nodes (features) that influences the most on the final prediction. This gives us clear insights into the possible causes of the fault. Our experiments show high accuracy: 99.44% on the EDS fault dataset, which is better than state of art models. By combining causal graphs with machine learning, our method not only predicts faults accurately but also helps understand their root causes. This makes it a strong and practical tool for improving system reliability.
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