Detection of inconspicuous power quality disturbances through step changes in rms voltage profile

Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications

“…Multiple techniques have been proposed over the years for detecting disturbances in PQM data, and they are broadly classified into two categories: in the first category, the trigger mechanism is based on the magnitude of a time series (e.g., overvoltage, overcurrent, signal rate of rise and root-mean-square (RMS) voltage variations) [12] or employs timefrequency and time-scale transformations to decompose the signal into several subbands (e.g., short-time Fourier transform and wavelet transform) [13,14]; the second category is composed of methods based on prominent signal residuals, which are obtained through time-varying mathematical models (e.g., autoregressive (AR) models and Kalman filters) or direct data comparison (e.g., point-by-point or cycle-by-cycle comparison) [15].…”

“…Although waveforms collected by PQMs are valuable assets for power system analysis, these raw measurements might not directly provide useful information for disturbance identification and classification [12]. In fact, various power system events might not cause conspicuous disturbances in the PQM waveforms; instead, they are characterized by an abrupt step change in the RMS voltage profile.…”

“…Thus, RMS voltage step changes have been proposed as an alternative triggering feature to detect events (both conspicuous and inconspicuous) within PQM datasets [8,12]. This task, however, is complicated by the fact that the magnitude of these RMS voltage step changes is often quite small (even less than 0.5% of the nominal voltage).…”

“…The quantity pN controls which RMS voltage values are compared to each other; it is recommended to adopt p ≥ 2 [13] so that there is at least a one-cycle gap between the waveform samples used to compute V k and V k−pN . Otherwise, the magnitude of the RMS voltage gradient might be smaller than the true magnitude of the step change when those sets of waveform samples contain a mix of both event and non-event data, possibly causing the event to be undetected [12]. On the other hand, adopting p ≥ 2 guarantees that any waveform transients lasting less than one cycle will have dissipated and that at least one value in the ∆V profile captures the true magnitude of the step change.…”

“…Based on this discussion, we adopted δ step = 0.0018 pu, which follows the rule of thumb of setting the threshold as half of the minimum-expected step change [35]. This detection technique has been shown to achieve high accuracy, especially in cases where the signal-to-noise ratio of the RMS voltage profile is high (i.e., low noise levels) [12,36]. On the other hand, this detector fails if the RMS voltage profile has high noise levels or it has not been properly filtered.…”