Optimal parameters selection of particle swarm optimization based global maximum power point tracking of partially shaded PV
| Parent link: | Journal of Physics: Conference Series Vol. 1399 : Applied Physics, Information Technologies and Engineering – APITECH-2019.— 2019.— [022032, 7 р.] |
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| Інші автори: | , |
| Резюме: | Title screen This paper presents optimal parameters selection of particle swarm optimization (PSO) algorithm for determining the global maximum power point tracking of photovoltaic array under partially shaded conditions. Under partial shading, the power-voltage characteristics have a more complex shape with several local peaks and one global peak. The two proposed controllers include dynamic Particle Swarm Optimization, and constant particle swarm optimization. The developed algorithms are implemented in MATLAB/Simulink platform, and their performances are evaluated. The results indicate that the dynamic particle swarm optimization algorithm can very fast track the GMPP within 128 ms for different shading conditions. In addition, the average tracking efficiency of the proposed algorithm is higher than 99.89%, which provides good prospects to apply this algorithm in the control search unit for the global maximum power point in stations. |
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2019
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| Онлайн доступ: | http://earchive.tpu.ru/handle/11683/57639 https://doi.org/10.1088/1742-6596/1399/2/022032 |
| Формат: | Електронний ресурс Частина з книги |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=661638 |
| Резюме: | Title screen This paper presents optimal parameters selection of particle swarm optimization (PSO) algorithm for determining the global maximum power point tracking of photovoltaic array under partially shaded conditions. Under partial shading, the power-voltage characteristics have a more complex shape with several local peaks and one global peak. The two proposed controllers include dynamic Particle Swarm Optimization, and constant particle swarm optimization. The developed algorithms are implemented in MATLAB/Simulink platform, and their performances are evaluated. The results indicate that the dynamic particle swarm optimization algorithm can very fast track the GMPP within 128 ms for different shading conditions. In addition, the average tracking efficiency of the proposed algorithm is higher than 99.89%, which provides good prospects to apply this algorithm in the control search unit for the global maximum power point in stations. |
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| DOI: | 10.1088/1742-6596/1399/2/022032 |