Design and Implementation of PV Maximum Power Point Tracking Control
DOI:
https://doi.org/10.54097/z4nkh225Keywords:
PV; MPPT tracking; Perturb and Observe method; Incremental Conductance method.Abstract
Photovoltaic Power generation system is an important renewable energy generation mode, in order to maximize the power generation efficiency of photovoltaic system, it is necessary to use Maximum Power Point Tracking (MPPT) technology. MPPT technology is designed to monitor the output voltage and current of the photovoltaic cell in real time, and adjust the operating point of the photovoltaic cell to make it work at the maximum power point. In this paper, the traditional disturbance observation method and variable step conductance increment method used in MPPT technology are modeled and compared, and it is concluded that the variable step conductance increment algorithm has high control accuracy, fast response speed and higher power generation efficiency in photovoltaic power generation. Secondly, based on the Arduino control chip, the hardware and software of the photovoltaic maximum power point tracking control circuit are designed, the hardware experiment platform of the photovoltaic power generation system is built, and the control algorithm of variable step length conductance increment method is realized in combination with programming. Finally, the performance of the variable step conductance increment algorithm in real environment is verified and evaluated. The test results are consistent with the simulation results, which further proves the superiority of variable step conductance increment method in practical application.
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[1] Lu Huibin. Design and implementation of indoor environment energy harvesting circuit based on maximum power point tracking[D]. Southeast University, 2020.5.
[2] Li Hong, Yang Duo, Su Wenzhe, et al. An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading[J], IEEE Transactions on Industrial Electronics,2018, 66(1):265-275.
[3] Wen Yunfeng, Yang Weifeng, Wang Ronghua, et al. Review and prospects for building a 100% renewable energy power system [J]. Chinese Journal of Electrical Engineering, 2020, 40(06): 1843-1856. DOI: 10.13334/j. 0258-8013.pcsee.192031.
[4] Wang Yibo, Su Gaomin, Qiu Rongxin. Research on global maximum power point tracking control of photovoltaic array based on jump exploratory conductance increment method [J/OL]. Chinese Journal of Electrical Engineering, 1-7[2024-03-19].
[5] Su Y ,Ma K ,Zheng S , et al.Corrigendum to "Rigdelet Neural Networks-based Maximum Power Point Tracking for a PEMFC connected to the network with Interleaved Boost Converter optimized by Improved Satin Bowerbird Optimization" [Energy Rep. 9 (2023) 4960-4970][J].Energy Reports,2024,111318-1318.
[6] Yu Zijie, Wei Chenxi, Tian Fangfang, et al. Application of an improved disturbance observation method in maximum power point tracking [J]. Electrical Measurement and Instrumentation, 2017, 54(15): 113-119.
[7] Liu Zuosong, Wang Hongyan, Zhou Mengen, et al. Global maximum power point tracking algorithm based on conductance increment method [J]. Transactions of the Chinese Society of Electrical Engineering, 2023, 18(02): 245-253.
[8] Liu Yang. Maximum power tracking algorithm combining short-circuit current method and variable step conductance increment method [J]. Science, Technology and Engineering, 2018, 18(13): 55-60.
[9] Yang Hui, Luo Shan, Sun Xiangdong, et al. Research on active disturbance rejection control method of photovoltaic energy storage bidirectional DC-DC converter [J]. Journal of Solar Energy, 2018, 39(05): 1342-1350.DOI: 10.19912/ j.0254-0096.2018.05.024.
[10] XU Shungang, GAO Yuan, ZHOU Guohua, et al. A global maximum power point tracking algorithm for photovoltaicsystems under partially shaded conditions using modified maximum power trapezium method[J]. lEEE Transactionson Industrial Electronics, 2021,68(1): 370-380.
[11] Zhang W, Li Q, He Q. Application of machine learning methods in photovoltaic output powerprediction: A reviewI. Journal of Renewable and Sustainable Energy, 2022, 14(2).
[12] Liu Pinping, Cheng Ruofa, Xu Libin, et al. Adaptive variable step size INC algorithm based on photovoltaic MPPT sampling current [J]. Journal of Electric Power Sources, 2023, 21(05): 58-66. DOI: 10.13234/j.issn .2095-2805.2023.5.58.
[13] Wei Liming, Li Kaikai.Photovoltaic MPPT control strategy based on improved disturbance observation method [J]. Power Supply Technology,2022,46(07):811-814.
[14] Yi Lei, Xie Yulong, Zeng Fanyan, et al. MPPT control based on improved perturbation observation method based on neural network [J]. ActaSolari, 2022, 43(04): 198-203. DOI:10.19912/j.0254-0096.tynxb.2020-0803.
[15] He Ning, Xiao Wenxun. Implementation of photovoltaic MPPT based on improved non-singular terminal sliding mode control [J]. Transactions of the Electrical Engineering Society, 2022, 17(02): 160-167.
[16] Zhang Zhimin, Peng Hongyi, Pan Ruoyan, et al. Research on photovoltaic grid-connected inverter based on MPPT[J].Power Supply Technology,2023,47(01):108-111.
[17] Yuan Chenhu, Wang Kun, Liu Xiaoming, etc. Improved adaptive variable step size photovoltaic MPPT algorithm for power prediction [J]. Computer Simulation, 2021, 38(03): 34-39.
[18] XU Shungang, GAO Yuan, ZHOU Guohua, et al. A global maximum power point tracking algorithm for photovoltaic systems under partially shaded conditions using modified maximum power trapezium method[J]. lEEE Transactionson Industrial Electronics, 2021,68(1): 370-380.
[19] CHAWDA G,MAHELA O, GUPTA N, et al. Incremental conductance based particle swarm optimization algorithm for global maximum power tracking of solar-PV under nonuniform operating conditions [J].Applied Scienses, 2020,10(13): 1-16.
[20] Majad Mansoor, Adeel Feroz Mirza, Qiang Ling, et al. Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions[J].Solar Energy,2020, 198(C):499-518
[21] lmmad Shams, Saad Mekhilef, Kok Soon Tey. Maximum Power Point Tracking Using Modified Butterfly Optimization Algorithm for Partial Shading, Uniform Shading, and Fast Varying Load Conditions[J]. IEEE Transactions on Power Electronics, 2021, 36(5):5569-5581
[22] Hong Ying-Yi, Pula Rolando A. Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network[J]. Energy, 2022, 246: 133391
[23] NKAMBULE M S,HASAN A N, ALI A, et al. Comprehensive evaluation of machine learning MPPT algorithms for a PV system under different weather conditions[J]. Joural of Electrical Engineering & Technology, 2021 , 16 (1) : 411-427.
[24] LI M X, L X O, YU D S, et al. Perturbation observation method based optimization seeking control of soft-switching and no backflow power for LCL resonant-type dual active bridge DC-DC converters [J]. lEEE Transactions on Industrial Electronics, 2023.70(8): 7810-7820.
[25] LIU F, LIK,CHEN K,et al. A phase synchronization technique based on perturbation and observation for bidirectional wireless power transfer system[J]. lEEE Journal of Emerging and Selected Topics in Power Electronics, 2020,8(2):1287-1297.
[26] SONG Shaojian, LI Bohan. Research on short-term prediction method of photovoltaic power generation based on LSTM network[J]. Renewable Energy,2021,39(05): 594-60.
[27] LIU Guohai, SUN Wenging, wU Zhenfei, et al.Short-term photovoltaic power generation prediction based on Attention-GRU[J]. Journal of Solar Energy,2022,43(02):226-232.
[28] Jianzhou w ,Yue Y,Bo Z , et al.Hybrid ultra-short-term PV power forecasting system for deter ministic forecasting and uncertaintyanalysis[1].Energy,2024,288129898-.
[29] Ahmad Z K ,Tanveer H ,Wook S B .Dual stream network with attention mechanism for photovoltaic power forecasting [J]. Applied Energy,2023,338.
[30] Bollipo R B, Mikkili S, Bonthagorla P K. Hybrid, optimal, intelligent and classical PV MPPT techniques: A review [J]. CSEE Journal of Power & Energy Systems 2021,7(1):9-33.
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