Modified tunicate swarm algorithm-based methodology for enhancing the operation of partially shaded photovoltaic system
The enhancement of photovoltaic array conversion efficiency during the partial shade operation is essential as the power-voltage curve includes multi-local peaks and one global peak. In such case, the conventional hill climbing-based maximum power point tracker approaches failed in monitoring the gl...
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| Veröffentlicht in: | Alexandria engineering journal Jg. 79; S. 449 - 470 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
15.09.2023
Elsevier |
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| ISSN: | 1110-0168 |
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| Abstract | The enhancement of photovoltaic array conversion efficiency during the partial shade operation is essential as the power-voltage curve includes multi-local peaks and one global peak. In such case, the conventional hill climbing-based maximum power point tracker approaches failed in monitoring the global power as they fall in local optima. Therefore, this paper proposes a new metaheuristic approach named modified tunicate swarm algorithm to track the global power of the photovoltaic array in the event of partial shade operation. In the proposed approach, two distinct random numbers are incorporated in the tunicate's updating process to enhance the search ability and avoid falling in local optima. The proposed approach is validated using two applications, CEC’17 test suite and practical one of designing the tracker installed with the photovoltaic array operated at partial shade. The proposed approach controls the dc-dc boost converter at the array terminals via obtaining the suitable duty cycle at which the global power is extracted. Five shade patterns with distinct locations of global power are analyzed in addition to normal operation, the proposed tracker is compared to teaching–learning-based optimizer, particle swarm optimizer, Coot optimization algorithm, capuchin search algorithm, and perturb and observe. Moreover, statistical tests of ANOVA table, Wilcoxon rank test, and Friedman test are conducted to assess the proposed approach. The fetched results clarified that, the best power error obtained via the proposed tracker is 0.000042 W at the third shade pattern while the worst one is 0.0652 W at the fifth pattern. Moreover, the p-value obtained via ANOVA table is 1.85399e-19 which confirms significant difference in the column means. The results proved the superiority and competence of the proposed tracker in solving the CEC’17 test suite and extracting the best global power from the photovoltaic array in all studied operating conditions. |
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| AbstractList | The enhancement of photovoltaic array conversion efficiency during the partial shade operation is essential as the power-voltage curve includes multi-local peaks and one global peak. In such case, the conventional hill climbing-based maximum power point tracker approaches failed in monitoring the global power as they fall in local optima. Therefore, this paper proposes a new metaheuristic approach named modified tunicate swarm algorithm to track the global power of the photovoltaic array in the event of partial shade operation. In the proposed approach, two distinct random numbers are incorporated in the tunicate's updating process to enhance the search ability and avoid falling in local optima. The proposed approach is validated using two applications, CEC’17 test suite and practical one of designing the tracker installed with the photovoltaic array operated at partial shade. The proposed approach controls the dc-dc boost converter at the array terminals via obtaining the suitable duty cycle at which the global power is extracted. Five shade patterns with distinct locations of global power are analyzed in addition to normal operation, the proposed tracker is compared to teaching–learning-based optimizer, particle swarm optimizer, Coot optimization algorithm, capuchin search algorithm, and perturb and observe. Moreover, statistical tests of ANOVA table, Wilcoxon rank test, and Friedman test are conducted to assess the proposed approach. The fetched results clarified that, the best power error obtained via the proposed tracker is 0.000042 W at the third shade pattern while the worst one is 0.0652 W at the fifth pattern. Moreover, the p-value obtained via ANOVA table is 1.85399e-19 which confirms significant difference in the column means. The results proved the superiority and competence of the proposed tracker in solving the CEC’17 test suite and extracting the best global power from the photovoltaic array in all studied operating conditions. |
| Author | Al-Dhaifallah, Mujahed Fathy, Ahmed Amer, Dina A. |
| Author_xml | – sequence: 1 givenname: Ahmed surname: Fathy fullname: Fathy, Ahmed email: afali@zu.edu.eg organization: Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia – sequence: 2 givenname: Dina A. surname: Amer fullname: Amer, Dina A. email: dina_amer1310@hti.edu.eg organization: Computer Science Dept., Higher Technological Institute, Tenth of Ramadan City 44228, Egypt – sequence: 3 givenname: Mujahed orcidid: 0000-0002-8441-2146 surname: Al-Dhaifallah fullname: Al-Dhaifallah, Mujahed email: mujahed@kfupm.edu.sa organization: Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia |
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| Keywords | Tunicate swarm algorithm Maximum power point tracker Photovoltaic Partial shade |
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| Title | Modified tunicate swarm algorithm-based methodology for enhancing the operation of partially shaded photovoltaic system |
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