An adaptive statistical method for power quality analysis

Digital fault recorders installed for monitoring current and/or voltage waveforms acquire and store vast amount of waveform data for post processing. Because of this, effective offline automated event detection from acquired data is necessary. In this work, we propose a new automatic event detection method which takes the acquired data and produces event flags at instances of events. The method is based on the statistical analysis of adaptive decomposition signals. The combination of an adaptive prediction filter-based subband decomposition structure with a rule-based histogram analysis block produced successful detection and localization results on our real-life power system transient data.