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Poprawa jakości układu elektronicznego w systemie fotowoltaicznym użytym do produkcji wodoru
Języki publikacji
Abstrakty
In this work, we will compare between two types of converters Cuk and SEPIC because they are the most widely used and are two of the developed family of the converter. This paper presents under MATLAB/Simulink the use of CUK converter with maximum power point tracking (MPPT) technology, to increase its efficiency by an algorithm Perturb and Observe (P&O) and incremental conductance method, then we will apply artificial neural network (ANN) to avoid the disadvantages of MPPT Classical. The MPPT developed presents a better behavior than the P&O system.
W artykule porównano dwa rodzaje przekształtników CUK i SEPIC ponieważ są one najczęściej używane. Przedstawiono symulację użycia przekształtnika CUK w technice śledzenia punktu maksymalnej mocy MPPT oraz algorytmy Perturb and Observe (P&O) przyroostowej przewodności.
Wydawca
Czasopismo
Rocznik
Tom
Strony
81--85
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
autor
- Unité de Développement des Equipments Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou Ismail, W. Tipaza 42415, Algeria
- Biomaterials and Transport Phenomena Laboratory (LBMPT), Université Yahia Fares Medea, Aïn d'Heb Street, 26000, Algeria
autor
- Unité de Développement des Equipments Solaires, UDES/Centre de Développement des Energies Renouvelables, CDER, Bou Ismail, W. Tipaza 42415, Algeria
autor
- Biomaterials and Transport Phenomena Laboratory (LBMPT), Université Yahia Fares Medea, Aïn d'Heb Street, 26000, Algeria
Bibliografia
- [1] Boutelhig, A., Mellit, A., Hanini, S., Ground water sources assessment for sustainable supply through photovoltaic water pumping system, in M’zab valley, Ghardaia, Energy Procedia, vol.141, pp. 76-80. 2017.
- [2] Sabat, M., Baczyński, D., Szafranek, K., The trials of providing the power and energy balancing of the studied area concerning the cooperation of the res, employing a different number of these sources, Przeglad Elektrotechniczny, vol.93, no 9, pp. 11-15, 2017.
- [3] Benhamza, T., Laidi, M., Hanini, S., Modeling of an Improved Liquid Desiccant Solar Cooling System by Artificial Neural Network, Lecture Notes in Networks and Systems, 35, pp. 337- 348, 2018.
- [4] Bartosik, M., Kamrat, W., Kaźmierkowski, M., (...), Strupczewski, A., Szeląg, A., Storage of electrical energy and hydrogen economy, Przeglad Elektrotechniczny, vol.92, no 12, pp. 332-340, 2016.
- [5] Hatti, M., Operation and maintenance methods in solar power plants, Use, Operation and Maintenance of Renewable Energy Systems, pp. 61-93, 2014.
- [6] Singh, B., Singh, S., GA-based optimization for integration of DGs, STATCOM and PHEVs in distribution systems, Energy Reports, 5, pp. 84-103, 2019.
- [7] Saadi, A., Becherif, M., Ramadan, H.S., Hydrogen production horizon using solar energy in Biskra, Algeria, International Journal of Hydrogen Energy, vol.41, no47, , pp. 21899-21912, 2016.
- [8] Hatti, M.; Meharrar, A.; and Tioursi, M. Novel approach of maximum power point tracking for photovoltaic module neural network based. In Int. Symposium on Environment Friendly Energies in Electrical Applications. pp. 1-6. 2010.
- [9] Malinowski, M., Chmielowiec, J., Paściak, G., Świeboda, T., Usability evaluation of PEM fuel cell and supercapacitors application in the emergency power backup system, Przeglad Elektrotechniczny, vol.89, no 8, pp. 201-204, 2013.
- [10] Amar Bensaber, A., Benghanem, M., Guerouad, A., Amar Bensaber, M., Power flow control and management of a hybrid power system, Przeglad Elektrotechniczny, vol. 95, no1, pp. 186-190, 2019.
- [11] Wu, J., Xing, X., Liu, X., Guerrero, J.M., Chen, Z., Energy management strategy for grid-tied microgrids considering the energy storage efficiency, IEEE Transactions on Industrial Electronics, vol.65,no12, pp. 9539-9549, 2018.
- [12] Dai, M., Tang, D., Giret, A., Salido, M.A., Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints, Robotics and Computer-Integrated Manufacturing, vol. 59, pp. 143-157, 2019.
- [13] Hatti, M.; Tioursi, M. and Nouibat, W. Neural Network Approach for Semi-Empirical Modelling of PEM Fuel-Cell. In Industrial Electronics, 2006 IEEE International Symposium on vol.3, pp.1858-1863, 2006.
- [14] Ganjehsarabi, H., Performance assessment of solar-powered high pressure proton exchange membrane electrolyzer: A case study for Erzincan, International Journal of Hydrogen Energy, 44(20), pp. 9701-9707, 2019.
- [15] Chmielewski, A., Możaryn, J., Piórkowski, P., Bogdziński, K., Battery Voltage Estimation Using NARX Recurrent Neural Network Model, Advances in Intelligent Systems and Computing, 920, pp. 218-23, 2020.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f0e8ec3e-b211-41a7-a445-fd6a100ae042