Universal Waveshape-Based Disturbance Detection in Power Quality Data Using Similarity Metrics

The increasing deployment of triggerless power quality monitors provides valuable data about power systems and their components. However, efficient and accurate data mining techniques are necessary for parsing through large datasets and obtaining useful insights about the power system operation. While multiple studies can be found in the literature for detection of power quality events, they either detect only severe disturbances or are applicable to a specific subset of disturbances. This study proposes a universal approach for detecting power quality disturbances within large datasets regardless of the event root causes. It is based on the comparison between the shapes of multiple cycles of waveform data through similarity scores. Pre-processing techniques to enhance the detector accuracy are presented. They include correction of time misalignment between successive cycles caused by variations in the power system operating frequency, and reduction of load variation effects. The threshold values for detecting abnormal waveforms is time-adaptive, so that detection is based on local outliers. The proposed framework performance is assessed through multiple types of field datasets, and its superiority over other waveform abnormality detectors is empirically demonstrated. Moreover, current waveform is shown to be more suitable than that of voltage for novelty detection in power quality data.