An ensemble approach of classification model for detection and classification of power quality disturbances in PV integrated microgrid network

Vinayagam, Arangarajan; Veerasamy, Veerapandiyan; Radhakrishnan, Padmavathi; Sepperumal, Maheswari; Ramaiyan, Kalaivani

doi:10.1016/j.asoc.2021.107294

Cited by 26 publications

(11 citation statements)

References 34 publications

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“…WEKA program is a currently-used program in data mining and different PQ studies [28,29]. The sigmoid activation function was used in the MPA algorithm in WEKA and the number of neurons was determined automatically.…”

Section: Simulation Resultsmentioning

confidence: 99%

Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm

Akmaz

2022

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

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: In this study, a method based on Stockwell transform (ST), ReliefF feature selection method and Multilayer Perceptron Algorithm (MPA) algorithm was developed for classification of Power Quality (PQ) disturbance signals. In the method, firstly, ST was applied to different PQ signals to obtain classification features. A total of 30 different classification features were obtained by taking different entropy values of the matrix obtained after ST and different entropy values of the PQ signals. The use of all of the classification features obtained causes the method to be complicated and the training/testing times to be prolonged. Therefore, so as to determine the effective ones among the classification features and to ensure high classification success with less classification features, ReliefF feature selection method was used in this study. PQ disturbances were classified by using 8 different classification features determined by ReliefF feature selection method and MPA. The simulation results show that the method provides a high classification success in a shorter training/testing time. At the same time, simulation results have shown that the method was successful on testing data with noise levels of 35 dB and above after only one training.

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Section: Simulation Resultsmentioning

confidence: 99%

Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm

Akmaz

2022

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

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“…It has the merit of requiring less computational time from the processor. A voting approach using DWT and an ensemble classification technique is designed by the authors in [14]. The method is implemented in steps, including data collection, feature extraction, first-level classification (base classifiers), and second-level classification (ensemble with voting approach).…”

Section: Related Research Workmentioning

confidence: 99%

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

Swarnkar¹,

Mahela²,

Khan³

et al. 2022

Energy Engineering

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A multiple power quality (MPQ) disturbance has two or more power quality (PQ) disturbances superimposed on a voltage signal. A compact and robust technique is required to identify and classify the MPQ disturbances. This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality (MPQ) disturbance using stockwell transform (ST) and hilbert transform (HT). This will reduce the computational time to identify the MPQ disturbances, which makes the algorithm fast. A MPQ identification index (IPI) is computed using statistical features extracted from the voltage signal using the ST and HT. IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances. A MPQ time location index (IPL) is computed using the features extracted from the voltage signal using ST and HT. IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time. Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio (SNR). The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree (RBDT) is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances. MATLAB software is used to perform the study.

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“…From the results of comparison, as illustrated in the Table 15 , it is evident that the classification accuracy of different classifiers varies from the ranges of 95.30% to 100%. According to the Table 13 , the research works in [ 14 , 33 ] were considered to study different PQEs in simple power networks without integration of RE sources, whereas the research works in [ 32 , 35 , 64 ] were discriminated different PQEs in RE integrated MG networks but failed to analyse under uncertain RE source conditions. However, in this study, different PQEs and transients due to switching and LG fault events were considered to be categorised with the proposed RS ensemble method in the PV integrated MG network under real-time varying solar irradiance of the PV system.…”

Section: Comparative Analysis With Exiting Literature Work and Non-linear Classifiersmentioning

confidence: 99%

“…The S-Transform extraction method with Adaboost ensemble approach [ 33 ] and Hilbert Huang Transform feature extraction with adaptive NFS [ 34 ] have been used for PQ analysis with achievement of higher accuracy and better performance than single classifiers. Furthermore, DWT analysis with voting approach in [ 35 ] and stacking ensemble approach in [ 36 ] have shown better effectiveness in predicting various PQEs in the PV integrated power network. Similarly, to improve the classification accuracy and robustness of individual weak classifiers, the authors in [ 37 ] used Random Forest classifier for discrimination of multiple PQ signals in RE connected power network.…”

Section: Introductionmentioning

confidence: 99%

A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network

Vinayagam¹,

Othman

Veerasamy

et al. 2022

PLoS ONE

Self Cite

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This study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) connected Microgrid (MG) model. The MG model is developed and simulated with the presence of different PQEs (voltage and harmonic related signals and distinctive transients) in both on-grid and off-grid modes of MG network, respectively. In the pre-stage of classification, the features are extracted from numerous PQE signals by Discrete Wavelet Transform (DWT) analysis, and the extracted features are used to learn the classifiers at the final stage. In this study, first three Kernel types of SVM classifiers (Linear, Quadratic, and Cubic) are used to predict the different PQEs. Among the results that Cubic kernel SVM classifier offers higher accuracy and better performance than other kernel types (Linear and Quadradic). Further, to enhance the accuracy of SVM classifiers, a SVM based RS ensemble model is proposed and its effectiveness is verified with the results of kernel based SVM classifiers under the standard test condition (STC) and varying solar irradiance of PV in real time. From the final results, it can be concluded that the proposed method is more robust and offers superior performance with higher accuracy of classification than kernel based SVM classifiers.

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An ensemble approach of classification model for detection and classification of power quality disturbances in PV integrated microgrid network

Cited by 26 publications

References 34 publications

Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm

Classification of Single and Combined Power Quality Disturbances Using Stockwell Transform, ReliefF Feature Selection Method and Multilayer Perceptron Algorithm

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network

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