Abstract
Due to the dependence of Photovoltaic (PV) systems on environmental conditions, their behavior is completely non-linear. Therefore, achieving the maximum power point in the nonlinear curve of PV systems faces many difficulties. The effective way to achieve the maximum power point in PVs is to design a maximum power point tracking (MPPT) controller. Although different ideas are recommended for MPPT controllers, the use of the artificial neural network (ANN) controller is very attractive among them due to its high dynamic response and fewer oscillations. Nevertheless, the major challenge for designing the ANFIS-MPPT is obtaining precise training data. This work proposes a hybrid gravitational search and pattern search (GS-PS) algorithm trained ANN-based MPPT is implemented for different environmental conditions. Radiation and temperature are two important input variables while the optimum voltage is considered for the output in the proposed method. The optimum values are attained via the GS-PS algorithm to regulate ANN controller to maintain the tracking performance. In addition, the P&O technique is started to operate in the following cycle and initiates a precise searching procedure from that point. Less number of samples for training is needed by applying the combined ANFIS and P&O method because the P&O can cover the disadvantage of the ANFIS when it cannot detect the accurate point. The simulations show the recommended method has much enhanced presentation than the previous methods in the time response as well as high accuracy. The accuracy of the offered MPPT compared to other approaches has been proven with a 2%-8% improvement in different temperature and radiation conditions.
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References
Abd-Elazim SM, Ali ES (2016) Load frequency controller design via BAT algorithm for nonlinear interconnected power system. Int J Electr Power Energy Syst 1(77):166–177
AL-Dhaifallah M, Ali ZM, Alanazi M, Dadfar S, Fazaeli MH (2021) An efficient short-term energy management system for a microgrid with renewable power generation and electric vehicles. Neural Comput Appl 33(23):16095–111
Ali MN, Mahmoud K, Lehtonen M, Darwish MM (2021) An efficient fuzzy-logic based variable-step incremental conductance MPPT method for grid-connected PV systems. Ieee Access 8(9):26420–26430
Al-Saedi W, Lachowicz SW, Habibi D, Bass O (2012) Power quality enhancement in autonomous microgrid operation using particle swarm optimization. Int J Electr Power Energy Syst 42(1):139–149
Aly M, Rezk H (2021) An improved fuzzy logic control-based MPPT method to enhance the performance of PEM fuel cell system. Neural Comput Appl 22:1–2
Bao Y, Hu Z, Xiong T (2013) A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing 6(117):98–106
Blaabjerg F, Teodorescu R, Liserre M, Timbus AV (2006) Overview of control and grid synchronization for distributed power generation systems. IEEE Trans Industr Electron 53(5):1398–1409
Ceylan O, Ozdemir A, Dag H (2010) Gravitational search algorithm for post-outage bus voltage magnitude calculations. In: 45th International Universities Power Engineering Conference UPEC2010 (pp. 1–6). IEEE..
Dzung PQ, Lee HH, Vu NT (2010) The new MPPT algorithm using ANN-based PV. In: International Forum on Strategic Technology (pp. 402–407). IEEE.
Eltamaly AM (2021) A novel musical chairs algorithm applied for MPPT of PV systems. Renew Sustain Energy Rev 1(146):111135
Gong X, Dong F, Mohamed MA, Abdalla OM, Ali ZM (2020) A secured energy management architecture for smart hybrid microgrids considering PEM-fuel cell and electric vehicles. IEEE Access 5(8):47807–47823
Kofinas P, Dounis AI, Papadakis G, Assimakopoulos MN (2015) An intelligent MPPT controller based on direct neural control for partially shaded PV system. Energy and Buildings 1(90):51–64
Kulaksız AA, Akkaya R (2012) A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive. Sol Energy 86(9):2366–2375
Larbes C, Cheikh SA, Obeidi T, Zerguerras A (2009) Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system. Renew Energy 34(10):2093–2100
Laxman B, Annamraju A, Srikanth NV (2021) A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids. Int J Hydrogen Energy 46(18):10653–10665
Lin WM, Hong CM, Chen CH (2011) Neural-network-based MPPT control of a stand-alone hybrid power generation system. IEEE Trans Power Electron 26(12):3571–3581
Lopes JP, Moreira CL, Madureira AG, Resende FO, Wu XA, Jayawarna NA, Zhang YA, Jenkins NA, Kanellos FA, Hatziargyriou NA (2005) Control strategies for microgrids emergency operation. In2005 International Conference on Future Power Systems (pp. 6-pp). IEEE.
Mohamed MA, Almalaq A, Awwad EM, El-Meligy MA, Sharaf M, Ali ZM (2020) A modified balancing approach for renewable based microgrids using deep adversarial learning. IEEE Trans Indus Appl
Mousa HH, Youssef AR, Mohamed EE (2021) State of the art perturb and observe MPPT algorithms based wind energy conversion systems: A technology review. Int J Electr Power Energy Syst 1(126):106598
Oshaba AS, Ali ES, Abd Elazim SM (2017) PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load. Neural Comput Appl 28(2):353–364
Punitha K, Devaraj D, Sakthivel S (2013) Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions. Energy 1(62):330–340
Quynh NV, Ali ZM, Alhaider MM, Rezvani A, Suzuki K (2021) Optimal energy management strategy for a renewable-based microgrid considering sizing of battery energy storage with control policies. Int J Energy Res 45(4):5766–5780
Radhika A, Soundradevi G, Kumar RM. An effective compensation of power quality issues using MPPT-based cuckoo search optimization approach. SOFT COMPUTING. 2020 May 7.
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9(3):727–745
Safari A, Mekhilef S (2010) Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter. IEEE Trans Industr Electron 58(4):1154–1161
Sahu RK, Panda S, Padhan S (2015) A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system. Appl Soft Comput 1(29):310–327
Seyedmahmoudian M, Rahmani R, Mekhilef S, Oo AM, Stojcevski A, Soon TK, Ghandhari AS (2015) Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method. IEEE Trans Sustain Energy 6(3):850–862
Shengqing L, Fujun L, Jian Z, Wen C, Donghui Z (2020) An improved MPPT control strategy based on incremental conductance method. Soft Comput 24(8):6039–6046
Tey KS, Mekhilef S (2014) Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level. Sol Energy 1(101):333–342
Tey KS, Mekhilef S (2014) Modified incremental conductance algorithm for photovoltaic system under partial shading conditions and load variation. IEEE Trans Industr Electron 61(10):5384–5392
Veeramanikandan P, Selvaperumal S (2021) Investigation of different MPPT techniques based on fuzzy logic controller for multilevel DC link inverter to solve the partial shading. Soft Comput 25(4):3143–3154
Wani TA (2021) A review of fuzzy logic and artificial neural network technologies used for MPPT. Turkish J Comput Math Educat (TURCOMAT) 12(2):2912–2918
Wu D, Nariman GS, Mohammed SQ, Shao Z, Rezvani A, Mohajeryami S (2019) Modeling and simulation of novel dynamic control strategy for PV–wind hybrid power system using FGS−PID and RBFNSM methods. Soft Comput 10:1–23
Zhang Y, Jiang Z, Yu X (2009) Small-signal modeling and analysis of parallel-connected voltage source inverters. In2009 IEEE 6th International Power Electronics and Motion Control Conference (pp. 377–383). IEEE.
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Appendices
Appendix A
PQ Model
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Alkhalaf, S., Ali, Z.M. & Oikawa, H. A novel hybrid gravitational and pattern search algorithm based MPPT controller with ANN and perturb and observe for photovoltaic system. Soft Comput 26, 7293–7315 (2022). https://doi.org/10.1007/s00500-022-07139-z
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DOI: https://doi.org/10.1007/s00500-022-07139-z