Methodology based on higher‐order statistics and genetic algorithms for the classification of power quality disturbances

Romero-Ramirez, Luis Alejandro; Elvira-Ortiz, David A.; Jaen-Cuéllar, Arturo Yosimar; Moriñigo-Sotelo, Daniel; Osornio-Ríos, Roque Alfredo; Romero-Troncoso, René de Jesús

doi:10.1049/iet-gtd.2020.0366

Cited by 8 publications

(4 citation statements)

References 31 publications

(35 reference statements)

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“…Papers [5,16] both apply most of the techniques discussed in this review, and [17] exposes a selection of additional techniques, such as KF, HHT, RMS, and HOS. Other authors prefer the combined use of AI with one another technique; this is what happens in the case of [10] with HHT, [18] with RMS, [19] with THD, [6] with HOS, or [8] with ST. Papers [7,20,21] omit the use of other complementary techniques. Indeed, Figure 2 depicts a timeline relationship among analysis procedures and application fields.…”

Section: Resultsmentioning

confidence: 99%

“…Another interesting observation that can be extracted from the analyzed papers is the source of the signals used to carry out each study. Even though in some cases both real and synthetic/simulated signals are used to corroborate the programming or calculations made [5][6][7][8][9], in most documents just one of the signal types is chosen. Other clear examples of the use of both types of signals are [52][53][54][55][56], applied to PQ analysis with HOS, [57][58][59] for WT analysis, and [60,61] for AI.…”

Section: Resultsmentioning

confidence: 99%

“…While for SOS the wavelet transform stands out as expected, for HOS more cases of kurtosis, or 4th order cumulants, are found (27 papers), followed by 3rd order cumulants (19 papers). Although the use of 5th and 6th order statistics has not yet become very popular, there is a subtle trend towards research with them in recent years: in [6], even order cumulants (2nd, 4th, and 6th) are obtained; in [54], the 3rd, 4th, and 5th order cumulants are used, and, in [47], cumulants of the 3rd, 4th, 5th, and 6th orders are utilized for the detection of complex PQ disturbances.…”

Section: Figurementioning

confidence: 99%

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Current Status and Future Trends of Power Quality Analysis

et al. 2022

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In this article, a systematic literature review of 153 articles on power quality analysis in PV systems published in the last 20 years is presented. This provides readers with an overview on PQ trends in several fields related to instrumental techniques that are being used in the smart grid to visualize the quality of the energy, establishing a solid literature base from which to start future research. A preliminary appreciation allows us to intuit that higher-order statistics are not implemented in measurement equipment and that traditional instrumentation is still used for the performance of measurement campaigns, not yielding the expected results since the information processed does not come from an electrical network from 20 years ago. Instead, current networks contain numerous coupled load effects; thus, new disturbances are not simple; they are usually complex events, the sum of several types of disturbances. Likewise, depending on the type of installation, the objective of the PQ analysis changes, either by detecting certain events or simply focusing on seeing the state of the network.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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Current Status and Future Trends of Power Quality Analysis

et al. 2022

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“…It aims to minimize classification error and the number of features simultaneously for improved accuracy and computation time. [112] proposes a fuzzy classifier with GA-based methodology for detecting and classifying transient PQDs. It optimally suppresses the fundamental frequency component, allowing better identification of anomalies.…”

Section: Terminationmentioning

confidence: 99%

Review on Power Quality Disturbances Detection, Classification, Optimization and Mitigation Techniques

Khaldi,

Dekhandji,

Recioui

2024

Preprint

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Power quality (PQ) plays an important role in ensuring the smooth operation of power systems. PQ disturbances may cause significant consequences, impacting the reliability and efficiency of power networks. This paper presents a comprehensive review of PQ within smart grids and the integration of renewable energy sources. Methods of detecting, classifying, optimizing, and mitigating PQ events are reviewed with providing a brief theoretical explanation for each technique. Related work done in the past two decades, was conducted, presenting a clear view of the development of research in a chronological order serving as a guide for future investigations in this field of study.

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