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Singh G, Pal Y, Dahiya AK. Classification of Power Quality Disturbances using Linear Discriminant Analysis. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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Jayasree T, Raj TR. Combined ST/MST and radial basis function neural networks for power quality disturbance signal classification. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this paper, the classification of power quality disturbances using combined ST/MST (S-Transform/Modified S-Transform) and Radial Basis Function Neural Network (RBFNN) is proposed. The extraction of significant features from the power quality disturbance signals is one of the challenging tasks in recognizing different disturbances. The Stockwell Transform/Modified Stockwell Transform (ST/MST) based features are distinct, understandable and more immune to noise. The important attributes present in the signals are retrieved from the ST/MST contours, MST 3D plots and MST based statistical curves. The relevant features are also extracted from the statistical curves. The extracted features are given as input to the RBFNN for further classification. This method is evaluated under both noisy and noiseless conditions. The performance of the proposed approach is compared with other conventional approaches in the literature. The simulation results demonstrate that the proposed MST based RFNN technique is more effective for the detection and classification of power quality disturbances.
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Affiliation(s)
- T. Jayasree
- Department of Electronics & Communication Engineering, Government College of Engineering, Tirunelveli-7, Tamilnadu, India
| | - T.Selvin Retna Raj
- Department of Electronics & Communication Engineering, DMI College of Engineering, Chennai, Tamilnadu, India
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A New S-Transform-Based Method for Identification of Power Quality Disturbances. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2017. [DOI: 10.1007/s13369-017-2895-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Combined VMD-SVM based feature selection method for classification of power quality events. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.10.038] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Andrade LC, Oleskovicz M, Fernandes RA. Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2015.10.061] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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A novel data selection technique using fuzzy C-means clustering to enhance SVM-based power quality classification. Soft comput 2014. [DOI: 10.1007/s00500-014-1472-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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