Current Output Estimation of 12 Volts Solar Power Line Source Using Fuzzy Logic-Based Modelling Technique
Downloads
This study used the fuzzy logic-based system to estimate the output current of a solar panel system at specified amount of power and percentage of cloud cover in the atmosphere. This established the fuzzy rule-based system which was applied from a research study to analyze the behavior of the current output wherein the available solar panels are three 100 watts and two 50 watts making it difficult to measure the current with power of 175 watts and 350 watts. The amount of the produced current was established through the fuzzy rule-based system stated as the following: if the power is low and the atmosphere is almost cloudy then the current produced is very small; if the power is high and the atmosphere is cloudy then the current produced is small; if the power is medium and the atmosphere is partially cloudy then the current produced is average; if the power is high and the atmosphere is clear then the current produced is large. Through defuzzification the fuzzy system was implemented by computing the output value using the centroid method.
Dafallah, K.O. (2018). Experimental study of 12V and 24V photovoltaic DC refrigerator at different operating conditions. Physica B: Condensed Matter, 545(2018), 237-244.
Dafallah, K.O. M. Benghanem, S.N. Alamri, A.A. Joraid, A.A. Al-Mashraqi (2017). Experimental evaluation of photovoltaic DC refrigerator under different thermostat settings. Renewable Energy, 113(2017), 1150-1159.
Tan, Ömer, Daniel Jerouschek, Ralph Kennel, and Ahmet Taskiran (2022). Energy management strategy in 12-Volt electrical system based on deep reinforcement learning. Vehicles, 4(2), 621-638.
Táczi, István, István Vokony, Lilla Barancsuk, Gábor Mihály Péter, Balázs Tőzsér, Bálint Hartmann (2023). Role of voltage control devices in low voltage state estimation process", International Transactions on Electrical Energy Systems, 2023, (Article ID 6614905), 22 pages.
Pap, Endre (2022). Pseudo-analysis as a tool of information processing, Proceedings, 81(1), 116.
Perez, R., E. Inga, A. Aguila, C. V´asquez, L. Lima, A. Viloria, and M.-A. Henry (2018). Fault diagnosis on electrical distribution systems based on fuzzy logic, in International Conference on Swarm Intelligence. Springer, 174–185.
Jabri, Majed & Chouiref, Houda & Jerbi, Houssem & Braiek, Naceur. (2008). Fuzzy logic parameter estimation of an electrical system. 1 – 6.
Castillo, O., P. Melin, J. Kacprzyk, and W. Pedrycz (2007). Type-2 fuzzy logic: theory and applications,” in 2007 IEEE international conference on granular computing (GRC 2007). IEEE, 145–145.
Branco, P. C., N. Lori, and J. Dente (1995). New approaches on structure identification of fuzzy models: Case study in an electro-mechanical system, in World Wisepersons Workshop. Springer, 104–143.
Andrade-Benavides, Dayan, Diego Vallejo-Huangga & Paulina Morillo (2022), Fuzzy logic model for failure analysis in electric power distribution system, Procedia Computer Science, 204(2022), 497-504.
Das, B. (2005), Fuzzy logic-based fault-type identification in unbalanced radial power distribution system, IEEE Transactions on Power Delivery, 21(1), 278–285.
Branco, P. C. and J. Dente (1998). An experiment in automatic modeling an electrical drive system using fuzzy logic, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(2), 254–262.
Andrade-Benavides, D., Diego Vallejo-Huangga & Paulina Morillo (2022), Fuzzy logic model for failure analysis in electric power distribution system, Procedia Computer Science, 204(2022), 497-504.
Shirazi, E. and S. Jadid (2019), A multiagent design for self-healing in electric power distribution systems, Electric Power Systems Research, 171(2019), 230–239.
Dernoncourt, F. (2013), Introduction to fuzzy logic,” Massachusetts Institute of Technology, 21(2013).
Gururajapathy, S. S., H. Mokhlis, and H. A. Illias, (2017), Fault location and detection techniques in power distribution systems with distributed generation: A review,” Renewable and Sustainable Energy Reviews, 74(2017) 949–958.
Momesso, E., W. M. S. Bernardes, and E. N. Asada (2019), Fuzzy adaptive setting for time-current-voltage based overcurrent relays in distribution systems,” International Journal of Electrical Power & Energy Systems, 108(2019), 135–144.
Hussain, S., M. A. Ahmed, and Y.-C. Kim (2019). Efficient power management algorithm based on fuzzy logic inference for electric vehicles parking lot, IEEE Access, 7(2019), 65467–65485.
Hussain, Z. (2018). Fuzzy logic expert system for incipient fault diagnosis of power transformers, International Journal on Electrical Engineering and Informatics, 10(2), 300–317.
Petrovic, Predrag (2004). New digital multimeter for accurate measurement of synchronously sampled AC signals. Instrumentation and Measurement, IEEE Transactions on. 53. 716 – 725.
Mathur, Badrilal (2009). Effect of shading on series and parallel connected solar PV modules, Modern Applied Science, 3(10), 32-41.
Romero, Raymundo (2023). Battery Charging Performance Using 12 Volts Solar Power Line Source. Engineering and Technology Journal, 8(9), 2789-2794.
Reddy, R., D. & R. Veera Sudarasana Reddy (2016). Study on series and parallel connected photovoltaic system under shadow conditions, IOSR Journal of Electrical and Electronics Engineering, 11(1), 36-40.
Mustapha, K., Saheb Djohra, Hamza Belkhamsa (2015). Effect of parallel and series connection configuration of solar collector on the solar system performances, paper presented in 6th International Renewable Energy Congress.
Sathyanarayana P., Rajkiran Ballal, Lakshmi Sagar P. S. & Girish Kuma (2015). Effect of Shading on the Performance of Solar PV Panel, Energy and Power, 5(1-A), 1-4.
Knights, A., Vesna & Gacovski, Zoran (2023). Fuzzy rule-based system as a method of modeling for estimation quality of the Vardar River. American Scientific Research Journal for Engineering, Technology, and Sciences.
Asimakopoulou, G.E., V.T.Kontargyri,, G.J.Tsekouras, Ch.N.Elias, F.E.Asimakopoulou, I.A.Stathopulos (2011), A fuzzy logic optimization methodology for the estimation of the critical flashover voltage on insulators, Electric Power Systems Research, 81,(2), 580-588.
Mogharreban, N. & L.F. Dilalla (2006), Comparison of defuzzification techniques for analysis of non-interval data, Conference Paper in Annual Conference of the North American Fuzzy Information Processing Society – NAFIPS – July 2006.
Seyedmahmoudian, M., Mekhilef S, Rahmani R, Yusof R, Renani E. (2013). Analytical Modeling of Partially Shaded Photovoltaic Systems. Energies, 6(128).