Wavelet transform‐based feature extraction for detection and classification of disturbances in an islanded micro‐grid

Wang, Yunqi; Ravishankar, Jayashri; Phung, B.T.

doi:10.1049/iet-gtd.2018.5131

Cited by 11 publications

(5 citation statements)

References 36 publications

(45 reference statements)

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“…For the computer vision analysis CV2 and Mahotas libraries were used. For a comparative analysis, the results of the application of well‐established models such as support‐vector machine (SVM) [54], k‐nearest neighbor (k‐NN) [55], and ensemble learning methods (Ens) [56] are presented.…”

Section: Classifier Model Architecturementioning

confidence: 99%

Classification of insulators using neural network based on computer vision

Stefenon

Corso

Nied

et al. 2021

IET Generation Trans & Dist

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Insulators of the electrical power grid are usually installed outdoors, so they suffer from environmental stresses, such as the presence of contamination. Contamination can increase surface conductivity, which can lead to system failures, reducing the reliability of the network. The identification of insulators that have their properties compromised is important so that there are no discharges through its insulating body. To perform the classification of contaminated insulators, this paper presents computer vision techniques for the extraction of contamination characteristics, and a neural network (NN) model for the classification of this condition. Specifically, the Sobel edge detector, Canny edge detection, binarization with threshold, adaptive binarization with threshold, threshold with Otsu and Riddler–Calvard techniques will be evaluated. The results show that it is possible to have an accuracy of up to 97.50% for the classification of contaminated insulators from the extraction of characteristics with computer vision using the NN for the classification. The proposed model is more accurate than well‐established models such as support‐vector machine (SVM), k‐nearest neighbor (k‐NN), and ensemble learning methods. This showed that optimizing the model's parameters can make it superior to solve the problem in question.

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Section: Classifier Model Architecturementioning

confidence: 99%

Classification of insulators using neural network based on computer vision

Stefenon

Corso

Nied

et al. 2021

IET Generation Trans & Dist

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“…Wavelet transform (WT) is the widely applied signal processing technique for islanding feature extraction but has some shortcomings with non-linear loads and harmonics. To overcome this lag of the WT, in [160], Renyi entropy was employed with WT to identify and categorize seven PQ disturbances. The features extracted using Renyi and WT were then trained in SVM classifiers and tested in real-time scenarios.…”

Section: Support-vector-machine-based Idsmentioning

confidence: 99%

“…The drawbacks of such techniques are the effects of mother wavelet selection, threshold settings, and various frequencies. Intelligent IDSs made extensive use of various forms of WT for islanding detection, as described in section IV [136], [138], [153], [155], and [160].…”

Section: ) Wt-based Idsmentioning

confidence: 99%

A Comprehensive Review of Intelligent Islanding Schemes and Feature Selection Techniques for Distributed Generation System

Hussain¹,

Kim

Mehdi

2021

IEEE Access

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Detection of unintentional islanding, defined as inadvertently separation of distributed generators (DGs) from the utility grid, is a major challenging issue for modern distribution networks. Islanding detection becomes problematic especially when the local generation matches or closely matches the local load. Therefore, there are strict requirements for accurate, fast, and reliable islanding detection of renewables and DG-based systems. Various islanding schemes have been proposed in the literature, which can be categorized as remote, local, and intelligent-classifier-based schemes. Recently, intelligent schemes have gained attention due to their superior properties and advantages relative to traditional approaches. This paper overviews the shift in research from traditional schemes to intelligent islanding schemes. It also highlights the major obstacles, challenges, advantages and disadvantages, and future research directions of intelligent schemes. In this study, the intelligent-classifier-based islanding detection schemes presented over the last decade are analyzed objectively and comprehensively from all aspects of islanding detection. This research further highlights feature selection schemes and the most common parameters used for islanding detection. Finally, based on a detailed and critical analysis, the findings and potential recommendations are presented.

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“…The authors in [25] presented a squaring and low-pass filtering method based on an autocorrelation function. Another WT and Renyi entropy technique for an islanded microgrid is presented in [26]. In [27] and [28], the authors described combined signal processing and machine learning schemes for radial distribution grid.…”

Section: Introductionmentioning

confidence: 99%

Fault Protection in Microgrid Using Wavelet Multiresolution Analysis and Data Mining

2021

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The protection problems in microgrid effect the reliability of the power system caused due to high distributed generator penetrations. Therefore, fault protection in microgrid is extremely important and needs to be resolved to enhance the robustness of the power system. This manuscript proposes a combined signal processing and data mining-based approach for microgrid fault protection. In this study, first the multiresolution decomposition of wavelet transform is employed to preprocess the voltage and current signals to compute the total harmonic distortion of the voltage and current. Then, the statistical indices of standard deviation, mean, and median of the total harmonic distortion and the negative sequence components of active and reactive power are used to collect the input data. After that, all the available data is provided to the random forest-based classifier to evaluate the efficiency of the proposed scheme in terms of the detection, identification, and classification of faults. This study used different aspects for data collection by simulating various fault and no-fault cases for both looped and radial configurations under grid-connected and islanded modes of operation. The simulations were performed on a standard medium voltage microgrid using MATLAB/SIMULINK, whereas the analysis for testing and training of the random forest were conducted in Python. It is recognized that the proposed method performed better than support vector machines and decision tree that are reported in the literature. The results further demonstrate that the proposed method can also detect simultaneous faults, and it is also effective against measurement noise.INDEX TERMS Data-mining, discrete wavelet transform, feature extraction, fault protection, multiresolution analysis, random forest.

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Wavelet transform‐based feature extraction for detection and classification of disturbances in an islanded micro‐grid

Cited by 11 publications

References 36 publications

Classification of insulators using neural network based on computer vision

Classification of insulators using neural network based on computer vision

A Comprehensive Review of Intelligent Islanding Schemes and Feature Selection Techniques for Distributed Generation System

Fault Protection in Microgrid Using Wavelet Multiresolution Analysis and Data Mining

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