  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.
