High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Supanat Chamchuen, Apirat Siritaratiwat, Pradit Fuangfoo, Puripong Suthisopapan and Pirat Khunkitti
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Supanat Chamchuen: Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Apirat Siritaratiwat: Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Pradit Fuangfoo: Provincial Electricity Authority, Bangkok 10900, Thailand
Puripong Suthisopapan: Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Pirat Khunkitti: Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand

Energies, 2021, vol. 14, issue 5, 1-18

Abstract: Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

Keywords: power quality disturbance classification; optimal feature selection; probabilistic neural network; particle swarm optimization; artificial bee colony (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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