Online Power Quality Anomaly Capture Based on Adaptive Feature Functionals and Fixed-Point Conditioning

The large-scale integration of renewable energy loads has made distribution network waveforms strongly non-stationary. In addition to conventional harmonics and voltage deviations, power quality events with highly random triggering and extremely short durations occur frequently, posing a serious threat to precision manufacturing and high-reliability power supply scenarios. Existing detection methods, however, are either sensitive to window length and threshold settings or depend on offline labeled samples, and are therefore difficult to adapt to online anomaly discovery under continuous sampling. To address these problems, this paper proposes an online detection method based on fixed feature functionals and an adaptive normal reference. In this method, the feature centers, scales, and reliability weights are estimated only from normal windows in the training partition, and the alarm threshold is calibrated on a labeled validation partition, forming a site-adaptive online detector fitted on normal data and calibrated on a validation set. During operation, each new window is first scored against the normal reference at the previous instant, and the reference is updated only when the score falls below the update gate threshold. Experiments show that the proposed method achieves an event-level precision of 91.93%, a recall of 91.11%, and an F1 of 91.52%, providing higher detection sensitivity than comparable algorithms.