An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions
A partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Grey wolf optimi...
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
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| Online Access: | https://ieeexplore.ieee.org/document/9785916/ |
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| author | Ibrahim Saiful Millah Pei Cheng Chang Dawit Fekadu Teshome Ramadhani Kurniawan Subroto Kuo Lung Lian Jia-Fu Lin |
| author_facet | Ibrahim Saiful Millah Pei Cheng Chang Dawit Fekadu Teshome Ramadhani Kurniawan Subroto Kuo Lung Lian Jia-Fu Lin |
| author_sort | Ibrahim Saiful Millah |
| collection | DOAJ |
| description | A partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been used to solve optimization problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the original GWO (OGWO) can still be further improved for various PSCs. Therefore, there have been some modified grey wolf optimization (MGWO) algorithms proposed to improve the GWO. Nevertheless, only incremental improvement has been made. Therefore, a modified GWO, named enhanced grey wolf optimization (EGWO) is proposed in this paper. The proposed method adds the weighting average, the pouncing behavior and nonlinear convergence factor in the OGWO. In particular, since real wolves may engage in pouncing action when they are hunting, inclusion of pouncing completes the GWO algorithm and yields great improvements. As will be shown via simulation and experiment, the EGWO can drastically reduce the tracking time (up to 45.5% of the OGWO) and the dynamic tracking efficiency can be improved by more than 2%, compared to the OGWO. Moreover, the EGWO achieves the highest maximum power point compared to some of the existing GWO and other swarm based algorithms. |
| first_indexed | 2024-04-11T04:24:53Z |
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| id | doaj.art-5d446b5127f747d98783acb25d1d0c5b |
| institution | Directory Open Access Journal |
| issn | 2644-1284 |
| language | English |
| last_indexed | 2024-04-11T04:24:53Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj.art-5d446b5127f747d98783acb25d1d0c5b2022-12-30T00:00:29ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842022-01-01339240810.1109/OJIES.2022.31792849785916An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading ConditionsIbrahim Saiful Millah0https://orcid.org/0000-0003-2521-6310Pei Cheng Chang1Dawit Fekadu Teshome2https://orcid.org/0000-0002-8389-5529Ramadhani Kurniawan Subroto3https://orcid.org/0000-0003-3072-9823Kuo Lung Lian4https://orcid.org/0000-0002-1242-7330Jia-Fu Lin5Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, TaiwanATCO, Calgary, CanadaTechnical University of Denmark, Kgs. Lyngby, DenmarkTechnical University of Denmark, Kgs. Lyngby, DenmarkDepartment of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, TaiwanA partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been used to solve optimization problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the original GWO (OGWO) can still be further improved for various PSCs. Therefore, there have been some modified grey wolf optimization (MGWO) algorithms proposed to improve the GWO. Nevertheless, only incremental improvement has been made. Therefore, a modified GWO, named enhanced grey wolf optimization (EGWO) is proposed in this paper. The proposed method adds the weighting average, the pouncing behavior and nonlinear convergence factor in the OGWO. In particular, since real wolves may engage in pouncing action when they are hunting, inclusion of pouncing completes the GWO algorithm and yields great improvements. As will be shown via simulation and experiment, the EGWO can drastically reduce the tracking time (up to 45.5% of the OGWO) and the dynamic tracking efficiency can be improved by more than 2%, compared to the OGWO. Moreover, the EGWO achieves the highest maximum power point compared to some of the existing GWO and other swarm based algorithms.https://ieeexplore.ieee.org/document/9785916/Maximum power point tracking (MPPT)modified grey wolf optimizer (MGWO)partial shading condition (PSC)photovoltaic (PV) array |
| spellingShingle | Ibrahim Saiful Millah Pei Cheng Chang Dawit Fekadu Teshome Ramadhani Kurniawan Subroto Kuo Lung Lian Jia-Fu Lin An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions IEEE Open Journal of the Industrial Electronics Society Maximum power point tracking (MPPT) modified grey wolf optimizer (MGWO) partial shading condition (PSC) photovoltaic (PV) array |
| title | An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions |
| title_full | An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions |
| title_fullStr | An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions |
| title_full_unstemmed | An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions |
| title_short | An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions |
| title_sort | enhanced grey wolf optimization algorithm for photovoltaic maximum power point tracking control under partial shading conditions |
| topic | Maximum power point tracking (MPPT) modified grey wolf optimizer (MGWO) partial shading condition (PSC) photovoltaic (PV) array |
| url | https://ieeexplore.ieee.org/document/9785916/ |
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