Article: Multiple power quality event detection and classification using a modified S-transform and WOA tuned SVM classifier Journal: International Journal of Power and Energy Conversion (IJPEC) 2021 Vol.12 No.4 pp.338 - 363 Abstract: In this paper, a novel method for the classification of power quality events is illustrated. Fifteen types of power quality events consisting of single and multi-stage disturbances are considered for the study. A database of the synthetic PQ events is generated in MATLAB using mathematical models. The generated signals are passed through a novel modified Stockwell transform consisting of a second-order Gaussian window that provides the ST matrix. From the ST matrix, various statistical features such as energy and standard deviation of the magnitude and phase contour are extracted and given as input to support vector machine (SVM). Furthermore, to improve the performance of SVM, a novel meta-heuristic technique called whale optimisation algorithm (WOA) is used to tune the hyperparameters of the SVM classifier. The performance of the proposed method is analysed under noisy and noiseless conditions. It is observed that WOA tuned SVM provides improved classification accuracy than other widely used meta-heuristic optimisation algorithms such as particle swarm optimisation (PSO) tuned SVM and genetic algorithm (GA) tuned SVM. Further, two novel circuits for the generation of sag, swell, and interrupt are developed and the proposed technique is validated on real-time signals obtained from the circuits. Inderscience Publishers - linking academia, business and industry through research

Title: Multiple power quality event detection and classification using a modified S-transform and WOA tuned SVM classifier

Authors: Sambit Dash; Umamani Subudhi

Addresses: Infosys Limited, No. 44 & 97/A, Infosys Avenue, Electronics City Phase 1, Electronic City, Bengaluru, Karnataka 560100, India ' IIIT Bhubaneswar, Gothapatna, Bhubaneswar, Odisha, India

Abstract: In this paper, a novel method for the classification of power quality events is illustrated. Fifteen types of power quality events consisting of single and multi-stage disturbances are considered for the study. A database of the synthetic PQ events is generated in MATLAB using mathematical models. The generated signals are passed through a novel modified Stockwell transform consisting of a second-order Gaussian window that provides the ST matrix. From the ST matrix, various statistical features such as energy and standard deviation of the magnitude and phase contour are extracted and given as input to support vector machine (SVM). Furthermore, to improve the performance of SVM, a novel meta-heuristic technique called whale optimisation algorithm (WOA) is used to tune the hyperparameters of the SVM classifier. The performance of the proposed method is analysed under noisy and noiseless conditions. It is observed that WOA tuned SVM provides improved classification accuracy than other widely used meta-heuristic optimisation algorithms such as particle swarm optimisation (PSO) tuned SVM and genetic algorithm (GA) tuned SVM. Further, two novel circuits for the generation of sag, swell, and interrupt are developed and the proposed technique is validated on real-time signals obtained from the circuits.

Keywords: power quality; PQ; modified S-transform; MST; whale optimisation algorithm; WOA; support vector machine; SVM; classification.

DOI: 10.1504/IJPEC.2021.118050

International Journal of Power and Energy Conversion, 2021 Vol.12 No.4, pp.338 - 363

Received: 11 Aug 2020
Accepted: 09 Mar 2021
Published online: 08 Oct 2021 *

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Title: Multiple power quality event detection and classification using a modified S-transform and WOA tuned SVM classifier

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