Abnormal Power Usage Detection: A Metrics‐Based Scheme for Low Sampling Data

ABSTRACT Abnormal power usage detection is vital for power system security and efficient energy use. Existing methods are hampered by low sampling rates, insufficient feature extraction, and limited accuracy, which impede cost effective large scale deployment and raise privacy concerns. This paper proposes a low frequency electrical characteristic based abnormal power usage detection scheme. First, an abnormal power usage detection model is constructed by coupling active power and power factor under low frequency measurement conditions of existing smart meters. Second, the scheme embraces non‐intrusive load monitoring (NILM) to safeguard user privacy and markedly reduce computational burden. Validation based on smart meter data indicates that the proposed scheme offers a more efficient, simpler, and more cost‐effective solution for deploying advanced NILM‐based metering systems in residential settings.