Novel Moving Average Filter for Detecting RMS Voltage Step Changes in Triggerless PQ Data

• Published in: IEEE transactions on power delivery Vol. 33; no. 6; pp. 2920 - 2929
• Main Authors: Bastos, Alvaro Furlani, Lao, Keng-Weng, Todeschini, Grazia, Santoso, Surya
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activation; Adaptive filters; Capacitor switching; Capacitors; Data reduction; Electric potential; Filtration; Manufacturing; moving average and median filters; power quality; Reactive power; rms voltage profile; Steady-state; Switches; Switching; Transient analysis; triggerless data; Variations; Voltage control; Voltage measurement; voltage step change; Waveforms
• ISSN: 0885-8977; 1937-4208
• DOI: 10.1109/TPWRD.2018.2831183

The voluminous amount of raw waveform data recorded by triggerless power quality monitors contain conspicuous and inconspicuous disturbance events. Data reduction and detection techniques are needed to efficiently extract useful information hidden in the raw data and identify power quality disturbances. The overall objective of this study is to use step changes in the rms voltage profile as an alternative triggering feature for automatically detecting switching events. The full characterization of the event is based on processing a small portion of the voltage waveform selected around the detected rms voltage step change. A filtering method is proposed to smooth out rapid fluctuations in the rms voltage profile during steady-state operation, while preserving the sharp edges caused by rms voltage step changes. Once the rms voltage profile has been filtered, adaptive limits based on the median absolute deviation are computed for detecting rms voltage step changes. The effectiveness of the proposed technique is evaluated using triggerless voltage waveforms to detect capacitor switching events. The use of the filtered rms voltage profile allows accurate detection of capacitor energizing and de-energizing events, while more than 50% of the detections in the unfiltered profile correspond to false-positives.