Accurate Identification of Point-on-Wave Inception and Recovery Instants of Voltage Sags and Swells

Bastos, Alvaro Furlani; Lao, Keng-Weng; Todeschini, Grazia; Santoso, Surya

doi:10.1109/tpwrd.2018.2876682

Cited by 24 publications

(16 citation statements)

References 21 publications

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“…It has been shown that these techniques are effective for detecting conspicuous disturbances (i.e., cases where the underlying system event causes transients in the voltage and/or current waveforms) [16]. On the other hand, they are unable to detect most inconspicuous disturbances [17,18], hindering their suitability for the processing of triggerless PQM datasets. Further, they might be sensitive to harmonics, sampling frequency and other user-selected parameters (such as a mother wavelet for the detector based on a wavelet transform).…”

Section: Disturbance Detection In Power System Datasetsmentioning

confidence: 99%

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: Disturbance Detection In Power System Datasetsmentioning

confidence: 99%

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

Bastos

Santoso

2021

Energies

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“…This transform has been successfully applied to detect rapid changes in voltage and/or current waveforms (e.g. switch transients and voltage sags) [3, 10]; however, it fails when the underlying system event does not create a disturbance in the waveforms [24]. Another issue with the wavelet transform is the increased computational burden if multiple decomposition levels are required, posing a serious limitation for real‐time operation of digital signal processor‐based instruments [25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The rms voltage sequence is denoted

V_{rms}

and its computation is implemented as shown in (1)

V_{rms} (][, \frac{k N}{2}) = \sqrt{\frac{1}{N} \sum_{p = false(k - 2 false) N / 2 + 1}^{k N / 2} v {false[p false]}^{2}}

for

k = 2, 3, \dots

, and where N is the number of samples per cycle of the voltage waveform data. The resulting discrete rms voltage profile assumes the shape of a stairstep graph, and its time resolution is half‐cycle [24, 29].…”

Section: Traditional Rms Voltage Step Change Detectormentioning

confidence: 99%

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

Bastos

Freitas²,

Todeschini

et al. 2019

IET Generation, Transmission & Distribution

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“…Other parameters such as the point-on-wave in both the initiation and the recovery have been the object of study in Reference [54]. Besides this, an interesting analysis is done by Bastos et al in Reference [55].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies

2019

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This paper presents a novel voltage sag topology that occurs during an unintentional islanding operation (IO) within a distribution network (DN) due to large induction motors (IMs). When a fault occurs, following the circuit breaker (CB) fault clearing, transiently, the IMs act as generators due to their remanent kinetic energy until the CB reclosing takes place. This paper primarily contributes to voltage sag characterization. Therefore, this novel topology is presented, analytically modelled and further validated. It is worth mentioning that this voltage sag has been identified in a real DN in which events have been recorded for two years. The model validation of the proposed voltage sag is done via digital simulations with a model of the real DN implemented in Matlab considering a wide range of scenarios. Both simulations and field measurements confirm the voltage sag analytical expression presented in this paper as well as exhibiting the high accuracy achieved in the three-phase model adopted.

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Accurate Identification of Point-on-Wave Inception and Recovery Instants of Voltage Sags and Swells

Cited by 24 publications

References 21 publications

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

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

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

Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies

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