Robust H-Infinity Filter and PSO-SVM Based Monitoring of Power Quality Disturbances System
This Power quality problem is a vital issue in current-day power system with the increased penetration of distributed generation (DG) systems which greatly affects the stability and reliability of the system. This study presents a comprehensive analysis of detection and classification strategies for...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10440305/ |
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| author | Prakash K. Ray Harish Kumar Sahoo Asit Mohanty Javed Khan Bhutto Abdulwasa Bakr Barnawi Abdulmjeed Ali Alshaya |
| author_facet | Prakash K. Ray Harish Kumar Sahoo Asit Mohanty Javed Khan Bhutto Abdulwasa Bakr Barnawi Abdulmjeed Ali Alshaya |
| author_sort | Prakash K. Ray |
| collection | DOAJ |
| description | This Power quality problem is a vital issue in current-day power system with the increased penetration of distributed generation (DG) systems which greatly affects the stability and reliability of the system. This study presents a comprehensive analysis of detection and classification strategies for monitoring power quality (PQ) disturbances. Specifically, the focus is on PQ disturbances caused as a result of load switching and variations in environmental factors, such as changes in solar irradiation and wind speed. Twelve different types of PQ disturbances under various operating scenarios are considered for the detection and classification study. Twelve different statistical features are extracted by passing the disturbances through robust H-infinity filter in order to minimize the data size and computational complexities. The statistical properties of PQ disturbances are initially categorized using the Fast Generalized Fuzzy-C means (FGFCM) clustering approach. Further, the disturbances are correctly classified by pattern classification approach like support vector machines (SVM) in order to distinctly classify them. The validity of the classification analysis is reinforced by the inclusion of experimental data gathered from a prototype wind energy system and photovoltaic (PV) system setup. Again, Genetic algorithm (GA) is used for extracting optimal features and Particle swarm optimization (PSO) is applied for optimising the heuristic parameters of the SVM in order to improvise the classification accuracy under normal, noisy as well as increased penetration of wind and photovoltaic (PV) systems. |
| format | Article |
| id | doaj-art-7cb4e1a36dc341cfaa6ae458ba23c7c5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7cb4e1a36dc341cfaa6ae458ba23c7c52025-01-09T00:00:49ZengIEEEIEEE Access2169-35362024-01-0112390413905710.1109/ACCESS.2024.336772710440305Robust H-Infinity Filter and PSO-SVM Based Monitoring of Power Quality Disturbances SystemPrakash K. Ray0https://orcid.org/0000-0003-4632-6201Harish Kumar Sahoo1Asit Mohanty2https://orcid.org/0000-0001-6124-1957Javed Khan Bhutto3https://orcid.org/0000-0003-3261-9978Abdulwasa Bakr Barnawi4https://orcid.org/0000-0002-8117-7742Abdulmjeed Ali Alshaya5School of Electrical Engineering, OUTR Bhubaneswar, Bhubaneswar, IndiaDepartment of Electronics and Tele-Communication Engineering, VSSUT, Burla, IndiaDepartment of Electrical Engineering, MIET Bhubaneswar, Bhubaneswar, IndiaDepartment of Electrical Engineering, King Khalid University, Abha, Saudi ArabiaDepartment of Electrical Engineering, King Khalid University, Abha, Saudi ArabiaDepartment of Electrical Engineering, King Khalid University, Abha, Saudi ArabiaThis Power quality problem is a vital issue in current-day power system with the increased penetration of distributed generation (DG) systems which greatly affects the stability and reliability of the system. This study presents a comprehensive analysis of detection and classification strategies for monitoring power quality (PQ) disturbances. Specifically, the focus is on PQ disturbances caused as a result of load switching and variations in environmental factors, such as changes in solar irradiation and wind speed. Twelve different types of PQ disturbances under various operating scenarios are considered for the detection and classification study. Twelve different statistical features are extracted by passing the disturbances through robust H-infinity filter in order to minimize the data size and computational complexities. The statistical properties of PQ disturbances are initially categorized using the Fast Generalized Fuzzy-C means (FGFCM) clustering approach. Further, the disturbances are correctly classified by pattern classification approach like support vector machines (SVM) in order to distinctly classify them. The validity of the classification analysis is reinforced by the inclusion of experimental data gathered from a prototype wind energy system and photovoltaic (PV) system setup. Again, Genetic algorithm (GA) is used for extracting optimal features and Particle swarm optimization (PSO) is applied for optimising the heuristic parameters of the SVM in order to improvise the classification accuracy under normal, noisy as well as increased penetration of wind and photovoltaic (PV) systems.https://ieeexplore.ieee.org/document/10440305/Classificationdetectionfast generalized fuzzy-C means clusteringH-infinity filterpower qualitysupport vector machines |
| spellingShingle | Prakash K. Ray Harish Kumar Sahoo Asit Mohanty Javed Khan Bhutto Abdulwasa Bakr Barnawi Abdulmjeed Ali Alshaya Robust H-Infinity Filter and PSO-SVM Based Monitoring of Power Quality Disturbances System IEEE Access Classification detection fast generalized fuzzy-C means clustering H-infinity filter power quality support vector machines |
| title | Robust H-Infinity Filter and PSO-SVM Based Monitoring of Power Quality Disturbances System |
| title_full | Robust H-Infinity Filter and PSO-SVM Based Monitoring of Power Quality Disturbances System |
| title_fullStr | Robust H-Infinity Filter and PSO-SVM Based Monitoring of Power Quality Disturbances System |
| title_full_unstemmed | Robust H-Infinity Filter and PSO-SVM Based Monitoring of Power Quality Disturbances System |
| title_short | Robust H-Infinity Filter and PSO-SVM Based Monitoring of Power Quality Disturbances System |
| title_sort | robust h infinity filter and pso svm based monitoring of power quality disturbances system |
| topic | Classification detection fast generalized fuzzy-C means clustering H-infinity filter power quality support vector machines |
| url | https://ieeexplore.ieee.org/document/10440305/ |
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