High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm
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) a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/5/1238 |
| _version_ | 1797395353481248768 |
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| author | Supanat Chamchuen Apirat Siritaratiwat Pradit Fuangfoo Puripong Suthisopapan Pirat Khunkitti |
| author_facet | Supanat Chamchuen Apirat Siritaratiwat Pradit Fuangfoo Puripong Suthisopapan Pirat Khunkitti |
| author_sort | Supanat Chamchuen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:34:20Z |
| format | Article |
| id | doaj.art-2cc249fbeb5443368443f853df414cc5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T00:34:20Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2cc249fbeb5443368443f853df414cc52023-12-11T18:16:28ZengMDPI AGEnergies1996-10732021-02-01145123810.3390/en14051238High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection AlgorithmSupanat Chamchuen0Apirat Siritaratiwat1Pradit Fuangfoo2Puripong Suthisopapan3Pirat Khunkitti4Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandProvincial Electricity Authority, Bangkok 10900, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandPower 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.https://www.mdpi.com/1996-1073/14/5/1238power quality disturbance classificationoptimal feature selectionprobabilistic neural networkparticle swarm optimizationartificial bee colony |
| spellingShingle | Supanat Chamchuen Apirat Siritaratiwat Pradit Fuangfoo Puripong Suthisopapan Pirat Khunkitti High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm Energies power quality disturbance classification optimal feature selection probabilistic neural network particle swarm optimization artificial bee colony |
| title | High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm |
| title_full | High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm |
| title_fullStr | High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm |
| title_full_unstemmed | High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm |
| title_short | High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm |
| title_sort | high accuracy power quality disturbance classification using the adaptive abc pso as optimal feature selection algorithm |
| topic | power quality disturbance classification optimal feature selection probabilistic neural network particle swarm optimization artificial bee colony |
| url | https://www.mdpi.com/1996-1073/14/5/1238 |
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