Particle swarm optimization algorithm for solar pv system under partial shading

• Autorzy: Abdulla Fawaz S. , Hamoodi Ali N. , Kheder Abdulaziz M.

Identyfikatory

DOI

10.15199/48.2021.10.17

Warianty tytułu

PL

Algorytm optymalizacji roju cząstek dla systemu fotowoltaicznego w warunkach częściowego zacienienia

• Języki publikacji: EN

Abstrakty

EN

Conventional maximum power point tracking (MPPT) has several demits such as steady-state oscillation and the inability to distinguish between multipacks generated under partial shading conditions (PSC). This paper studies the compression between the conventional Perturb and Observe (P&O) algorithm and the Particle Swarm Optimization ( PSO) algorithm to track global peak (GP) . Matlab Simulink carried out under PSC, the result shows that the PSO algorithm is successful to capture GP with 98.6% efficiency and the P&O algorithm is failed to capture the GP.

PL

Konwencjonalne śledzenie punktu maksymalnej mocy (MPPT) ma kilka wad, takich jak oscylacja stanu ustalonego i niemożność rozróżnienia opakowań zbiorczych generowanych w warunkach częściowego zacienienia (PSC). Ten artykuł bada kompresję pomiędzy konwencjonalnym algorytmem Perturb and Observe (P&O) a algorytmem Particle Swarm Optimization (PSO) w celu śledzenia globalnego piku (GP). Matlab Simulink przeprowadzony w ramach PSC, wynik pokazuje, że algorytm PSO z powodzeniem wychwytuje GP z wydajnością 98,6%, a algorytm P&O nie jest w stanie wychwycić GP.

Słowa kluczowe

EN

particle swarm optimization; PSO; maximum power point; MPPT; photovoltaic; PV; partial shading condition

PL

algorytm rojowy; system fotowoltaiczny; MPPT

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2021
• Tom: R. 97, nr 10
• Strony: 87--90
• Opis fizyczny: Bibliogr. 10 poz., rys., tab.

Twórcy

autor

Abdulla Fawaz S.

• Northern Technical University, Engineering Technical College of Mosul, Mosul, Iraq

• https://orcid.org/0000-0002-6888-0580

autor

Hamoodi Ali N.

• Northern Technical University, Engineering Technical College of Mosul, Mosul, Iraq

• https://orcid.org/000-0003-0991-3538

autor

Kheder Abdulaziz M.

• Northern Technical University, Engineering Technical College of Mosul, Mosul, Iraq

• https://orcid.org/0000-0003-2606-4697

Bibliografia

• [1] ashif Ishaque, et al. ,A Direct Control Based Maximum Power Point Tracking Method for Photovoltaic System Under partial Shading Conditions using Particle Swarm Optimization algorithm, Applied Energy 99 (2012): 414-422.
• [2] Ali M. Eltamaly, et al. ,Photovoltaic Maximum Power Point Tracking Under Dynamic Partial Shading Changes by Novel Adaptive Particle Swarm Optimization Strategy, Transactions of the Institute of Measurement and Control 42.1 (2020): 104-115.
• [3] Outlook, Annual Energy. ,U.S. Energy information administration, Department of Energy (2020).
• [4] Eltamaly, Ali M., and Almoataz Y. Abdelaziz, eds, Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems. Springer, 2019.
• [5] Teo, Kenneth Tze Kin, et al., Maximum Power Point Tracking of Partially Shaded Photovoltaic Arrays using Particle Swarm Optimization, 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology. IEEE, 2014.
• [6] Mahdi, A. J., et al. ,Improvement of a MPPT Algorithm for PV Systems and its Experimental Validation, International Conference on Renewable Energies and Power Quality. Vol. 25. 2010.
• [7] González-Longatt, Francisco M. ,Model of Photovoltaic Module in Matlab, i Cibelec 2005 (2005): 1-5.
• [8] Joshi, Arati, Afrah Khan, and S. P. Afra., Comparison of Half Cut Solar Cells with Standard Solar Cells, 2019 Advances in Science and Engineering Technology International Conferences (ASET). IEEE, 2019.
• [9] Abouelela, Mohamed, Power Electronics for Practical Implementation of PV MPPT, Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems, Springer, Cham, 2020. 65-105.
• [10] Kennedy, James, and Russell Eberhart. Particle Swarm Optimization, Proceedings of ICNN'95-international conference on neural networks. Vol. 4. IEEE, 1995.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).