提出可解释的电力系统异常检测模型，同时定位原因并分析异常类型。
Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systems
- 基于因果微分方程构建统一框架，融合异常检测与解释。
- 在电力系统中实现高精度检测，且减少对标注数据依赖。
- 能自动识别异常源头、类型和形态，适合电网安全场景。
异常检测与根因分析对保障电力系统等网络物理系统的安全与韧性至关重要。现有时间序列异常检测的机器学习模型多为黑箱，仅输出二元结果，缺乏对异常类型和来源的解释。为此，我们提出功率可解释因果常微分方程（PICODE）网络，一种统一的因果驱动架构，可联合完成异常检测及其解释，包括根因定位、异常类型分类与异常形状表征。实验表明，PICODE在电力系统中实现了具有竞争力的检测性能，同时提升了可解释性，并降低了对标注数据或外部因果图的依赖。我们提供了理论结果，证明异常函数的形状与提取的因果图权重变化之间存在一致关系。
原文摘要 · Abstract (English)
Anomaly detection and root cause analysis (RCA) are critical for ensuring the safety and resilience of cyber-physical systems such as power grids. However, existing machine learning models for time series anomaly detection often operate as black boxes, offering only binary outputs without any explanation, such as identifying anomaly type and origin. To address this challenge, we propose Power Interpretable Causality Ordinary Differential Equation (PICODE) Networks, a unified, causality-informed architecture that jointly performs anomaly detection along with the explanation why it is detected as an anomaly, including root cause localization, anomaly type classification, and anomaly shape characterization. Experimental results in power systems demonstrate that PICODE achieves competitive detection performance while offering improved interpretability and reduced reliance on labeled data or external causal graphs. We provide theoretical results demonstrating the alignment between the shape of anomaly functions and the changes in the weights of the extracted causal graphs.
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