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.

本文提出了一种基于逆累积分布的在线异常检测算法，用于现有实时基础设施中低延迟检测，适用于不可预测的数据模式，并在两个实际微电网操作数据案例中展示了易于使用、快速计算和可部署的优势。