通过可解释AI识别充电桩异常并定位原因
Explainable Anomaly Detection for Electric Vehicles Charging Stations
- 用孤立森林检测充电行为异常
- 通过DIFFI方法找出关键异常特征
- 适合智能电网运维与故障溯源场景
电动汽车充电桩是支持可再生能源交通转型的关键基础设施，其可靠性与效率依赖于对充电行为异常的有效检测。然而，在实际应用中，识别异常背后的原因同样重要。为此，本研究针对电动车充电设施，采用无监督异常检测技术，并结合可解释人工智能方法提升结果的可解释性，以揭示异常的根本原因。基于真实传感器与充电会话数据，使用孤立森林进行异常检测，并利用深度隔离森林特征重要性（DIFFI）方法识别导致异常的关键特征。该方法在实际工业案例中进行了验证，有效提升了异常诊断的透明度与实用性。
原文摘要 · Abstract (English)
Electric vehicles (EV) charging stations are one of the critical infrastructures needed to support the transition to renewable-energy-based mobility, but ensuring their reliability and efficiency requires effective anomaly detection to identify irregularities in charging behavior. However, in such a productive scenario, it is also crucial to determine the underlying cause behind the detected anomalies. To achieve this goal, this study investigates unsupervised anomaly detection techniques for EV charging infrastructure, integrating eXplainable Artificial Intelligence techniques to enhance interpretability and uncover root causes of anomalies. Using real-world sensors and charging session data, this work applies Isolation Forest to detect anomalies and employs the Depth-based Isolation Forest Feature Importance (DIFFI) method to identify the most important features contributing to such anomalies. The efficacy of the proposed approach is evaluated in a real industrial case.
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