Gapless Power-Quality Disturbance Recorder

Silva, L. R. M.; Kapisch, Eder Barboza; Martins, Carlos H. N.; Filho, Luciano M. A.; Cerqueira, A. S.; Duque, Carlos A.; Ribeiro, Paulo F.

doi:10.1109/tpwrd.2016.2557280

Cited by 27 publications

(17 citation statements)

References 25 publications

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“…This section presents the algorithm developed for filtering the rms voltage profile. The filter input consists of the rms voltage profile computed as in (2), and stored in the vector V input , while the output filtered rms voltage profile is stored in V f ilt rms . The filtering process is performed by sliding a wsamples (filter length) long window through the entire input signal.…”

Section: Methodsmentioning

confidence: 99%

“…This approach allows capturing all possible disturbance events, even if they are not accompanied by conspicuous transients [1]. However, such method inevitably results in voluminous amount of data [2], [3]. Novel triggering methods are necessary to parse through the voluminous data and detect conspicuous and inconspicuous disturbance events.…”

Section: Introductionmentioning

confidence: 99%

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

Bastos

Lao

Todeschini

et al. 2018

IEEE Trans. Power Delivery

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bastos

Lao

Todeschini

et al. 2018

IEEE Trans. Power Delivery

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“…Power quality (PQ) disturbances have been a topic of interest to researchers and utilities for decades. Concerns with PQ problems tend to intensify with the increasing use of non‐linear loads, electronic‐based equipment, and inverter‐interfaced distributed generation (especially photovoltaics) [1]. The widespread deployment of PQ monitors by multiple utilities and application of signal processing techniques allow further developments in the field of PQ disturbance analysis [2].…”

Section: Introductionmentioning

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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“…In this way, the signal must be stored to a proper analysis. The storage of the voltage and current signals over a long period takes a huge amount of memory and requires the use of a compression technique running in real time …”

Section: Introductionmentioning

confidence: 99%

“…The storage of the voltage and current signals over a long period takes a huge amount of memory and requires the use of a compression technique running in real time. 1 Although aggregation is useful to data reduction and comparison, deep data analysis should be enabled, considering the possibilities of a full observation. 2 The storage of the raw signal of voltage and current is supported by the fact that offline (more complex) techniques may reveal information that cannot be perceived by online (simpler) techniques.…”

Section: Introductionmentioning

confidence: 99%

Sparse representation algorithm applied to power systems signal compression

Silva

Filho

Duque

2018

Int Trans Electr Energ Syst

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Summary Power systems signal compression is an important task considering the nature of electrical disturbances, which must be analyzed offline in order to extract some useful information. This paper presents a comparative study about greedy algorithms to obtain sparse representation of signals applied to power systems signal compression. The algorithms will be compared according to two parameters: the quality of the compressed signal, in terms of its correlation coefficient related to the original signal, and the number of elements in the representation, which is related to the compression ratio. In addition, a computational complexity, in terms of required flops, will be performed, aiming at identifying which algorithm is most suitable to run in real time. Finally, a comparison with the wavelet transform, using a database of real power system signals, will be shown.

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Gapless Power-Quality Disturbance Recorder

Cited by 27 publications

References 25 publications

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

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

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

Sparse representation algorithm applied to power systems signal compression

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