Anomaly detection methods are part of the systems where rare events may endanger an operation's profitability, safety, and environmental aspects. Although many state-of-the-art anomaly detection methods were developed to date, their deployment is limited to the operation conditions present during the model training. Online anomaly detection brings the capability to adapt to data drifts and change points that may not be represented during model development resulting in prolonged service life. This paper proposes an online anomaly detection algorithm for existing real-time infrastructures where low-latency detection is required and novel patterns in data occur unpredictably. The online inverse cumulative distribution-based approach is introduced to eliminate common problems of offline anomaly detectors, meanwhile providing dynamic process limits to normal operation. The benefit of the proposed method is the ease of use, fast computation, and deployability as shown in two case studies of real microgrid operation data.

翻译：异常检测方法属于稀有事件可能危及运营盈利能力、安全性和环境影响的系统组成部分。尽管迄今为止已开发出众多先进的异常检测方法，但其部署仍局限于模型训练时的工况条件。在线异常检测具有适应数据漂移和变点的能力——这些变化在模型开发阶段可能未得到表征，从而延长系统服务寿命。本文针对需要低延迟检测且数据中不可预测地出现新模式的现有实时基础设施，提出一种在线异常检测算法。该方法引入基于在线逆累积分布的方法，在消除离线异常检测器常见问题的同时，为正常工况提供动态过程限。通过两个真实微电网运行数据案例研究表明，所提方法具有易用性、计算速度快和可部署性的优势。