Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine

Eri̇şti̇, Hüseyin; Yıldırım, Özal; Eristi, Belkis; Demir, Yakup

doi:10.1016/j.ijepes.2014.04.010

Cited by 85 publications

(35 citation statements)

References 31 publications

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“…The Modular PNN, SVM and LS-SVM techniques were used as classifiers. In [92] S-Transform with Extreme Learning Machine (ST-ELM) based pattern recognition approach was proposed for the automatic classification of PQ disturbances. In [93] S-transform with rule-based decision tree and ANN classifiers was proposed for the recognition of single and multiple PQ disturbances.…”

Section: Stockwell-transform Based Feature Extractionmentioning

confidence: 99%

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

228

111

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Section: Stockwell-transform Based Feature Extractionmentioning

confidence: 99%

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Khokhar

Zin

Mokhtar

et al. 2015

Renewable and Sustainable Energy Reviews

228

111

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“…A hybrid approach based on Fourier linear combiner and a FES for the classification of transient PQ disturbances in a EPS was presented in Dash et al 208 A WT-based extended fuzzy reasoning scheme for PQD&C was proposed in Xiangxun. 83,85 A hybrid PQD&C method based on the DWT, KF, and FES were presented in Rilling et al 103 The PQD&C based on a fuzzy ARTMAP wavelet network were presented in Decanini et al 210 An approach using ST and FCM clustering was proposed by Mahela and Shaik 211 for PQD&C. The FPARR-based PRT has the advantage of higher classification accuracy because of its learning and generalization abilities.…”

Section: Fuzzy Expert System-based Approachmentioning

confidence: 99%

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

137

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Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

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“…In PNN, the parameters setting method is from [25]. In ELM, the number of the hidden neurons and the activation function setting method is from [26]. Furthermore, in SVM, the kernel and regularization parameters setting method is from [27].…”

Section: The Comparison Of Classification Accuracy Of Different Classmentioning

confidence: 99%

“…In the pattern recognition of PQD, probabilistic neural network (PNN) [24,25], extreme learning machine (ELM) [26], support vector machine (SVM) [27][28][29], and decision tree (DT) [19,[30][31][32][33] have been applied for PQD recognition and achieved good classification results. Compared with other classifiers, the DT has a simple structure, high classification efficiency, and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm

et al. 2016

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Abstract:In order to improve the recognition accuracy and efficiency of power quality disturbances (PQD) in microgrids, a novel PQD feature selection and recognition method based on optimal multi-resolution fast S-transform (OMFST) and classification and regression tree (CART) algorithm is proposed. Firstly, OMFST is carried out according to the frequency domain characteristic of disturbance signal, and 67 features are extracted by time-frequency analysis to construct the original feature set. Subsequently, the optimal feature subset is determined by Gini importance and sorted according to an embedded feature selection method based on the Gini index. Finally, one standard error rule subtree evaluation methods were applied for cost complexity pruning. After pruning, the optimal decision tree (ODT) is obtained for PQD classification. The experiments show that the new method can effectively improve the classification efficiency and accuracy with feature selection step. Simultaneously, the ODT can be constructed automatically according to the ability of feature classification. In different noise environments, the classification accuracy of the new method is higher than the method based on probabilistic neural network, extreme learning machine, and support vector machine.

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Automatic recognition system of underlying causes of power quality disturbances based on S-Transform and Extreme Learning Machine

Cited by 85 publications

References 31 publications

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Power Quality Disturbances Feature Selection and Recognition Using Optimal Multi-Resolution Fast S-Transform and CART Algorithm

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