A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading Condition
One of the most critical tasks during the application of photovoltaic (PV) systems is to harvest the optimal output power at various environmental scenarios, which is called maximum power point tracking (MPPT). Though plenty of advanced techniques are developed to achieve this purpose, most of them...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.801571/full |
| _version_ | 1819179581104455680 |
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| author | Dongrui Li Jinjin Li Ning Wang |
| author_facet | Dongrui Li Jinjin Li Ning Wang |
| author_sort | Dongrui Li |
| collection | DOAJ |
| description | One of the most critical tasks during the application of photovoltaic (PV) systems is to harvest the optimal output power at various environmental scenarios, which is called maximum power point tracking (MPPT). Though plenty of advanced techniques are developed to achieve this purpose, most of them have corresponding prominent disadvantages, such as inefficient tracking ability, high computation burden, and complex convergence mechanism. Therefore, this work aims to propose a novel and powerful bio-inspired meta-heuristic optimization algorithm called peafowl optimization algorithm (POA), which is inspired by the group food searching behaviors of peafowl swarm. It can effectively achieve a suitable balance between local exploitation and global exploration thanks to its efficient exploratory and exploitative searching operators. Thus, a satisfactory MPPT performance for PV systems under partial shading condition (PSC) can be obtained based on POA. Moreover, two case studies, e.g., start-up test and step change in solar irradiation with constant temperature, are adopted to fairly and comprehensively validate the superiority and effectiveness of POA in contrast with particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), respectively. |
| first_indexed | 2024-12-22T22:00:43Z |
| format | Article |
| id | doaj.art-1441619298a64473aa9c1068926ec211 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-22T22:00:43Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-1441619298a64473aa9c1068926ec2112022-12-21T18:11:06ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-12-01910.3389/fenrg.2021.801571801571A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading ConditionDongrui Li0Jinjin Li1Ning Wang2Yunnan Baichuan Power Technology Co., Ltd., Kunming, ChinaCISDI Electric Technology Co., Ltd., Chongqing, ChinaGuian New District Electricity Distribution Co., Ltd., Guiyang, ChinaOne of the most critical tasks during the application of photovoltaic (PV) systems is to harvest the optimal output power at various environmental scenarios, which is called maximum power point tracking (MPPT). Though plenty of advanced techniques are developed to achieve this purpose, most of them have corresponding prominent disadvantages, such as inefficient tracking ability, high computation burden, and complex convergence mechanism. Therefore, this work aims to propose a novel and powerful bio-inspired meta-heuristic optimization algorithm called peafowl optimization algorithm (POA), which is inspired by the group food searching behaviors of peafowl swarm. It can effectively achieve a suitable balance between local exploitation and global exploration thanks to its efficient exploratory and exploitative searching operators. Thus, a satisfactory MPPT performance for PV systems under partial shading condition (PSC) can be obtained based on POA. Moreover, two case studies, e.g., start-up test and step change in solar irradiation with constant temperature, are adopted to fairly and comprehensively validate the superiority and effectiveness of POA in contrast with particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), respectively.https://www.frontiersin.org/articles/10.3389/fenrg.2021.801571/fullPV systemMPPTpartial shading conditionPOA algorithmmatlab/simulink |
| spellingShingle | Dongrui Li Jinjin Li Ning Wang A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading Condition Frontiers in Energy Research PV system MPPT partial shading condition POA algorithm matlab/simulink |
| title | A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading Condition |
| title_full | A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading Condition |
| title_fullStr | A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading Condition |
| title_full_unstemmed | A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading Condition |
| title_short | A Novel Technique Based on Peafowl Optimization Algorithm for Maximum Power Point Tracking of PV Systems Under Partial Shading Condition |
| title_sort | novel technique based on peafowl optimization algorithm for maximum power point tracking of pv systems under partial shading condition |
| topic | PV system MPPT partial shading condition POA algorithm matlab/simulink |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.801571/full |
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