Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System
When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms—such a...
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9212370/ |
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| author | Imran Pervez Arsalan Pervez Mohd Tariq Adil Sarwar Ripon K. Chakrabortty Michael J. Ryan |
| author_facet | Imran Pervez Arsalan Pervez Mohd Tariq Adil Sarwar Ripon K. Chakrabortty Michael J. Ryan |
| author_sort | Imran Pervez |
| collection | DOAJ |
| description | When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms—such as perturb and observe (P&O) and hill-climbing (HC)—are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithms—such as an artificial neural network (ANN) and fuzzy logic control (FLC)—have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB<sup>®</sup> is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard. |
| first_indexed | 2024-12-24T04:12:54Z |
| format | Article |
| id | doaj.art-471fe19b994b46f5a7f2a50394665e8a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T04:12:54Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj.art-471fe19b994b46f5a7f2a50394665e8a2022-12-21T17:16:01ZengIEEEIEEE Access2169-35362021-01-019486794870310.1109/ACCESS.2020.30286099212370Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation SystemImran Pervez0https://orcid.org/0000-0002-1410-5992Arsalan Pervez1https://orcid.org/0000-0002-8773-6218Mohd Tariq2https://orcid.org/0000-0002-5162-7626Adil Sarwar3https://orcid.org/0000-0002-8614-6697Ripon K. Chakrabortty4https://orcid.org/0000-0002-7373-0149Michael J. Ryan5https://orcid.org/0000-0002-6335-3773Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh, IndiaExterran Water Solutions, Calgary, CanadaDepartment of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh, IndiaDepartment of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh, IndiaCapability Systems Centre, School of Engineering and IT, UNSW Canberra at ADFA, Canberra, ACT, AustraliaCapability Systems Centre, School of Engineering and IT, UNSW Canberra at ADFA, Canberra, ACT, AustraliaWhen subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms—such as perturb and observe (P&O) and hill-climbing (HC)—are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithms—such as an artificial neural network (ANN) and fuzzy logic control (FLC)—have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB<sup>®</sup> is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard.https://ieeexplore.ieee.org/document/9212370/Adaptive jaya (Ajaya)maximum power point tracking (MPPT)metaheuristic algorithmsconventional algorithmsphotovoltaic (PV) |
| spellingShingle | Imran Pervez Arsalan Pervez Mohd Tariq Adil Sarwar Ripon K. Chakrabortty Michael J. Ryan Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System IEEE Access Adaptive jaya (Ajaya) maximum power point tracking (MPPT) metaheuristic algorithms conventional algorithms photovoltaic (PV) |
| title | Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System |
| title_full | Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System |
| title_fullStr | Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System |
| title_full_unstemmed | Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System |
| title_short | Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System |
| title_sort | rapid and robust adaptive jaya ajaya based maximum power point tracking of a pv based generation system |
| topic | Adaptive jaya (Ajaya) maximum power point tracking (MPPT) metaheuristic algorithms conventional algorithms photovoltaic (PV) |
| url | https://ieeexplore.ieee.org/document/9212370/ |
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