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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.

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Voltage sags and swells occur often in power systems; however determining the duration of these events is not straightforward. Seven methods for estimating the inception and recovery points (and consequently, the duration) of voltage sags and swells are surveyed: threshold rms voltage, waveform envelope, discrete wavelet transform, missing voltage, dq transformation, numerical matrix, and peak detector. Each method has its own strengths and weaknesses; however, no one method works on all conditions. An algorithmic approach to determining inception and recovery points is suggested. Such approach employs one method in combination with other methods based on the event characteristics.

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