Simulation modeling of multiport DC-DC converter in MPPT solar battery controllers under neural network control.
DOI:
https://doi.org/10.15276/opu.1.69.2024.11Keywords:
multi-port DC converter, artificial neural network (ANN), rechargeable batteries (AB), pulse width modulation (PWM), solar panels (SP), MOSFET power keysAbstract
Photovoltaic generation system - energy system, designed to convert useful solar energy through photovoltaic systems. It can consist of several components, including a solar array, DC/DC and DC/AC semiconductor converter, battery, filter or transformer, control system (CS). Depending on the application, photovoltaic systems can be operated as part of a self-contained power plant or operate on a network. Thus, it is possible to distinguish several basic configurations of photovoltaic generation systems. The standalone generation system is the most common configuration of photovoltaic generation systems, which contains rechargeable batteries (AB). This system is completely independent of the centralized power supply networks and is suitable for comfortable energy supply to consumers. The use of AB makes it possible to increase the reliability of the photovoltaic system and to extend the possibilities of using e.g. battery power is used during insufficient light or when the load exceeds the generation of solar cells. The scope of such configurations are lighting systems of residential and non-residential objects, power supply of houses and buildings, security systems and emergency power supply, power supply of remote residential and non-residential objects, Power supply to spacecraft, etc. Autonomous generation systems typically contain two transducers. The DC/DC converter acts as a battery charge controller. The control system for such a transducer may include the function of tracking the maximum power point for the maximum use of solar energy. The excess energy will be stored in AB. The DC/AC converter converts the DC current energy into the AC energy of the required frequency and voltage. The advantage of such a system is the possibility of using solar energy, both during the day and at night, due to the power of AB and the possibility of using the system at remote sites where there is no grid power supply. The disadvantage of such a system is the loss of double conversion of solar energy and the high cost of batteries. Artificial Neural Network (ANN) provides an alternative way to solve complex problems. A neural network, with the right structure, can compute the values of any continuous function with some predetermined accuracy. The neural network requires no knowledge of the internal parameters of the solar module, learns quickly, has the ability to optimize and approximate. Therefore, the use of INS to track the maximum power point is relevant and of practical and scientific importance.
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T. Haas, R. Krause, R. Weber, M. Demler, & G. Schmid. (2018). Technical photosynthesis involving CO2 electrolysis and fermentation. Nat. Catal., 1, 1, 32–39.
W. Li & X. He. (2011). Review of nonisolated high-step-up DC/DC converters in photovoltaic grid-connected applications. IEEE Trans. Ind. Electron., 58, 4, 1239–1250.
J. C. Rosas-Caro, J. M. Ramirez, F. Z. Peng, & A. Valderrabano. (2010). A DC-DC multilevel boost converter. IET Power Electron., 3, 1, 129–137.
J. A. Gow & C. D. Manning. (1999). Development of a photovoltaic array model for use in power-electronics simulation studies. IEE Proc. - Electr. Power Appl., 146, 2, 193–200.
J. Yuan et al. (2019). Single-Junction Organic Solar Cell with over 15% Efficiency Using Fused-Ring Acceptor with Electron-Deficient Core. Joule, 3, 4, 1140–1151.
Carleaa, F., Teodoreanua, D. I., & Iancua, I. (2015). Analysis of financial parameters for a combined photovoltaic/ LED intelligent lighting low voltage distributed generation. 1st International Conference 'Economic Scientific Research - Theoretical, Empirical and Practical Approaches', ESPERA 2013. Procedia Economics and Finance 8 (2014), рр.113–121.
Palombaa, V., Vastaa, S., Giacoppoa, G., Calabreseb, L., Giuseppe Gulli`a, Davide La Rosaa, & Angelo Frenia. (2015). Design of an innovative graphite exchanger for adsorption heat pumps and chillers. ScienceDirect 69th Conference of the Italian Thermal Engineering Association, ATI 2014. Energy Procedia 81 (2015). рр. 1030–1040.
Zendehbad, M., Chokani, N., & Abhari, R.S. (2016). Impact of forested fetch on energy yield and maintenance of wind turbines. Renewable Energy, 96, Part A, 548–558.
Jerson R.P. Vaz, & David H. Wood. (2016). Performance analysis of wind turbines at low tip-speed ratio using the Betz-Goldstein model. Energy Conversion and Management, 126, 15, 662–672.
Sicari, S., Rizzardi, A., Miorandi, D., Cappiello, C., & Coen-Porisini, A. (2016). Security policy enforcement for networked smart objects. Computer Networks, 108, 133–147.
Pavle Skocir, Petar Krivic, Matea Tomeljak, Mario Kusek, & Gordan Jezic. (2016). Activity detection in smart home environment. 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems Procedia Computer Science 96, 672–681.
E. H. Jung et al. (2019). Efficient, stable and scalable perovskite solar cells using poly(3-hexylthiophene). Nature, 567, 7749, 511–515.
M. Jeong et al. (2020). Stable perovskite solar cells with efficiency exceeding 24.8% and 0.3-V voltage loss. Science, 369, 6511.
R. B. A. Koad, A. F. Zobaa, & A. El-Shahat. (2017). A Novel MPPT Algorithm Based on Particle Swarm Optimization for Photovoltaic Systems. IEEE Trans. Sustain. Energy, 8, 2, 468–476.
A. Chaouachi, R. M. Kamel, & K. Nagasaka. (2010). A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system. Sol. Energy, 84, 12, 2219–2229.
E. Karatepe, M. Boztepe, & M. Colak. (2006). Neural network based solar cell model. Energy Convers. Manag., 47, 9, 1159–1178.
A. Rivaton, S. Chambon, M. Manceau, J.-L. Gardette, N. Lemaître, & S. Guillerez. (2010). Light-induced degradation of the active layer of polymer-based solar cells. Polym. Degrad. Stab., 95, 3, 278–284.
E. B. on 10 M. 2017 V. (2015). clear explanation on simple way U. so many P. plant owners not familiar with this problem A. well I. suggest to add material for P. repairing Reply. PID & LID: Devastating Phenomena for PV plants. Sinovoltaics - Your Solar Supply Network.
M. A. de Blas, J. L. Torres, E. Prieto, & A. Garc a. (2002). Selecting a suitable model for characterizing photovoltaic devices. Renew. Energy, 25, 3, 371–380.
S. Abdallah & S. Nijmeh. (2004). Two axes sun tracking system with PLC control. Energy Convers. Manag., 45, 11–12, 1931–1939.
M. J. Currie, J. K. Mapel, T. D. Heidel, S. Goffri, & M. A. Baldo. (2008). High-efficiency organic solar concentrators for photovoltaics. Science, 321, 5886, 226–228.
