Detection and classification of power quality disturbances with wavelet transform, decision tree algorithm and support vector machines

Monteiro, Douglas do Amaral; Zvietcovich, Wilingthon Guerra; Braga, Márcio F.

doi:10.1109/sbse.2018.8395809

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…The multi-layer extreme learning machine autoencoder (ML-ELM-AE) performs stacking operations on the basis of ELM-AE [38,51], and builds a network structure with multiple hidden layers, as shown in Fig. 3, which can improve the average power accuracy and generalization ability of the quality disturbance classification [39,[52][53][54][55][56][57].…”

Section: Self-encoder Structurementioning

confidence: 99%

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Disturbance Evaluation in Power System Based on Machine Learning

Sivakumar¹,

Ibrahim²

2022

Computers, Materials &Amp; Continua

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The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances. Then, a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality disturbances. The two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine, and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are developed. The proposed method can be used to classify the single and compound power quality disturbances with improved classification effect, reliability, robustness, and anti-noise performance, according to the experimental results. The hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF, SVM and ML-KNN schemes have 0.28, 0.23 and 0.22 respectively at a noise intensity of 20 dB. The average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF, SVM and ML-KNN schemes indicates 0.7, 0.77 and 0.78 respectively.

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Section: Self-encoder Structurementioning

confidence: 99%

“…In this paper, the optimal network structure of ML-ELM-AE is evaluated through experiments, and the multi-label mapping function g(s i , y j ) is obtained by using the ML-ELM-AE, and the threshold function t(s i ) is determined through multiple test experiments [53].…”

Section: Classifier Model Parameter Designmentioning

confidence: 99%

Disturbance Evaluation in Power System Based on Machine Learning

Sivakumar¹,

Ibrahim²

2022

Computers, Materials &Amp; Continua

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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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Classification of Emergent Dynamics in Microgrids Utilizing an Entropic Complexity-Based Feature Extractor

Ferdowsi

Hosseini

2020

Data-Enabled Discov. Appl.

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Detection and classification of power quality disturbances with wavelet transform, decision tree algorithm and support vector machines

Cited by 7 publications

References 11 publications

Disturbance Evaluation in Power System Based on Machine Learning

Disturbance Evaluation in Power System Based on Machine Learning

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

Classification of Emergent Dynamics in Microgrids Utilizing an Entropic Complexity-Based Feature Extractor

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