Root-Mean-Square Profiles under Varying Power Frequency: Computation and Applications

Bastos, Alvaro Furlani; Santoso, Surya

doi:10.1109/pesgm40551.2019.8973762

Cited by 3 publications

(3 citation statements)

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“…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.…”

Section: The Rms Voltage Gradient Profile Detection Approachmentioning

confidence: 99%

“…The high computational burden involved in this approach is often pointed out as a drawback of having RMS profiles with such a high time resolution [13,14]. However, it has been shown that a recursive approach eliminates such issues [36]. In the recursive approach, the RMS value at instant k is computed as…”

Section: Rms Profile Computationmentioning

confidence: 99%

“…Varying system frequency, which results in incorrect RMS voltage computations, as N waveform samples do not correspond to an integer number of cycles [36,37]; • Small load variations, which create intermittent variations in the RMS voltage profile and have the potential to hinder the detection of the events of interest.…”

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confidence: 99%

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

Bastos

Santoso

2021

Energies

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In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both synthetic signals and field data measurements.

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Section: The Rms Voltage Gradient Profile Detection Approachmentioning

confidence: 99%

Section: Rms Profile Computationmentioning

confidence: 99%