2021
DOI: 10.1371/journal.pone.0252104
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Signal-piloted processing and machine learning based efficient power quality disturbances recognition

Abstract: Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless … Show more

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Cited by 31 publications
(8 citation statements)
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“…The self-configurability of ensemble classifiers, as a function of the utilized training dataset, is one of their main advantages. The use of event-driven tools can help in enhancing the computational effectiveness of the suggested method [ 45 48 ]. In the future, this aspect can be investigated.…”
Section: Discussionmentioning
confidence: 99%
“…The self-configurability of ensemble classifiers, as a function of the utilized training dataset, is one of their main advantages. The use of event-driven tools can help in enhancing the computational effectiveness of the suggested method [ 45 48 ]. In the future, this aspect can be investigated.…”
Section: Discussionmentioning
confidence: 99%
“…ML technologies are not only intelligent and cognitive, but also their accuracy is skyrocketing due to their embedded mechanical abilities such as extraction, selection, and reduction of joint spatial-spectral features as well as contextual ones [ 24 26 ]. Moreover, the hidden dense layers with various allocated functions of the extensive networks work as intelligent learners by creating dictionaries or learning spaces to store deterministic information and then separate the landcover classes through its classification units [ 27 29 ]. The latest ML techniques that assist in classifying the hyperspectral data, that is, SVM, SRC, ELM, MRF, AL, DL, and TL, are shown categorically in Figure 3 and are discussed hereafter in detail.…”
Section: Machine Learning-based Techniques For Hsi Classificationmentioning
confidence: 99%
“…Many existing PQD detection and classification methods rely on frequency-domain or time-frequency domain analyses of the signals to extract informative features for further analysis to identify the type of disturbances, e.g., wavelet transform [ 2 ], Fourier transform, short-time Fourier transform (STFT), S-transform [ 3 ], etc. These methods are usually assisted with machine learning (ML)-based classification methods, including decision tree (DT) [ 3 , 4 ], support vector machine (SVM) [ 5 , 6 ], k -nearest-neighbor-based methods ( k NN) [ 7 , 8 , 9 ], and neural networks [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”
Section: Related Workmentioning
confidence: 99%