An Improved Hunger Games Search Algorithm-based Multi-peak MPPT Control for PV System under Partial Shading

Page: [261 - 273] Pages: 13

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Abstract

Background: In photovoltaic power generation systems, partial shading may cause the PV array to mismatch, thus leading to multi-peak output characteristics, which makes the conventional Maximum Power Point Tracking (MPPT) algorithm easily fall into local extremes and cause power loss.

Objective: The study aimed to accurately and quickly track the maximum power point of PV arrays in partial shading through swarm intelligence algorithms.

Methods: Based on the above, a MPPT control algorithm based on Chaos Adaptive Hunger Games Search with Dynamic Lévy Mutation Strategy (CAHGSL) is proposed in this paper. By introducing an improved logistics chaos map initialization population, a nonlinear adaptive convergence factor and a dynamic Lévy mutation strategy enhance their ability to jump out of local extremes during multi-peak MPPT and improve their tracking speed and efficiency.

Results: Under the three working conditions, the tracking efficiency of the MPPT algorithm proposed in this paper has been achieved by more than 99.5% in an average time of 0.152s, which is higher tracking efficiency compared to the PO, PSO, and HGS algorithms.

Conclusion: The results show that the MPPT algorithm proposed in this paper can balance the tracking speed and efficiency with less power oscillation during the tracking process, and can ensure stable output after convergence. The method proposed in this paper is helpful to improve the output power of PV arrays under partial shading.

[1]
Zhu W, Shang L, Li P, Guo H. Modified hill climbing MPPT algorithm with reduced steady‐state oscillation and improved tracking efficiency. J Eng 2018; 2018(17): 1878-83.
[http://dx.doi.org/10.1049/joe.2018.8337]
[2]
Bahri H, Harrag A. Variable step size P&O MPPT controller to improve static and dynamic PV system performances. J Adv Engi Comput 2018; 2(2): 86-93.
[http://dx.doi.org/10.25073/jaec.201822.94]
[3]
Peng ZH, Peng YC, Zhou HM, et al. Optimal design of MPPT algorithm perturbation step under rapid change of irradiation. Chinese Sol Energy J 2020; 41(08): 137-43.
[4]
Zheng HB, Du Q, Guo WH, et al. An improved perturbation observation MPPT algorithm applied to optical storage systems. Control Theory App 2022; 39(03): 491-8.
[5]
Singh P, Shukla N, Gaur P. Modified variable step incremental-conductance MPPT technique for photovoltaic system. Int J Info Technol 2021; 13(6): 2483-90.
[http://dx.doi.org/10.1007/s41870-020-00450-8]
[6]
Xu JG, Shen JX, Wang HX, et al. A maximum power point tracking strategy based on a novel variable step incremental conductance method. Renew Energ Res 2018; 36(09): 1305-13.
[7]
He JC, Wang XY, Joseph K. An improved MPPT control device, method and application system with variable step size perturbation method. Patent CN108347165A, 2018.
[8]
Kumar R, Tadikonda N, Kumar J, et al. An ANN-based MPPT technique for partial shading photo voltaic distribution generation. Kumar J, Tripathy M, Jena P, Eds Control Applications in Modern Power Systems. Singapore: Springer 2022; 870: p. 391-403.
[9]
Algazar MM. AL-monier H, EL-halim HA, Salem MEEK. Maximum power point tracking using fuzzy logic control. Int J Electr Power Energy Syst 2012; 39(1): 21-8.
[http://dx.doi.org/10.1016/j.ijepes.2011.12.006]
[10]
Yap KY, Sarimuthu CR, Lim JM-Y. Artificial intelligence based MPPT techniques for solar power system: A review. J Mod Power Syst Clean Energy 2020; 8(6): 1043-59.
[http://dx.doi.org/10.35833/MPCE.2020.000159]
[11]
Yi L, Jiang Z, Wang Y, et al. Short-term Power Load Forecasting Based on Orthogonal PCA-LPP Dimension Reduction and IGWO-BiLSTM. Recent Pat Mech Eng 2023; 16(1): 72-86.
[12]
Wang X, Wang W, Chen C, Cao Y, Xu J. Optimized rod size design of denim fabric grinding robot based on improved cuckoo search algorithm. Recent Pat Mech Eng 2022; 15(3): 351-60.
[http://dx.doi.org/10.2174/2212797614666210708130626]
[13]
Feng W, Zhang G, Yi O, et al. Fault diagnosis of oil-immersed transformer based on TSNE and IBASA- SVM. Recent Pat Mech Eng 2022; 15(5): 504-14.
[14]
Bu S, Yan L, Gao X, Zhao P, Lim CK. Vision-guided manipulator operating system based on CSRT algorithm. Int J Hydromechatronics 2022; 5(3): 260-74.
[http://dx.doi.org/10.1504/IJHM.2022.125091]
[15]
Murlidhar BR, Sinha RK, Mohamad ET, Sonkar R, Khorami M. The effects of particle swarm optimisation and genetic algorithm on ANN results in predicting pile bearing capacity. Int J Hydromechatronics 2020; 3(1): 69-87.
[http://dx.doi.org/10.1504/IJHM.2020.105484]
[16]
Mansoor M, Mirza AF, Ling Q. Harris hawk optimization-based MPPT control for PV systems under partial shading conditions. J Clean Prod 2020; 274: 122857.
[http://dx.doi.org/10.1016/j.jclepro.2020.122857]
[17]
Hamza Zafar M, Mujeeb Khan N, Feroz Mirza A, et al. A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustain Energy Technol Assess 2021; 47: 101367.
[http://dx.doi.org/10.1016/j.seta.2021.101367]
[18]
Mirza AF, Mansoor M, Ling Q, Yin B, Javed MY. A Salp-Swarm Optimization based MPPT technique for harvesting maximum energy from PV systems under partial shading conditions. Energy Convers Manage 2020; 209: 112625.
[http://dx.doi.org/10.1016/j.enconman.2020.112625]
[19]
Li CW, Du RF, Zhang ZH, An Q PV. MPPT method based on improved grey wolf optimization algorithm. Patent CN113342124A, 2021.
[20]
Li H, Yang D, Su W, Lu J, Yu X. An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading. IEEE Trans Ind Electron 2019; 66(1): 265-75.
[http://dx.doi.org/10.1109/TIE.2018.2829668]
[21]
Mansoor M, Mirza AF, Ling Q, Javed MY. Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions. Sol Energy 2020; 198: 499-518.
[http://dx.doi.org/10.1016/j.solener.2020.01.070]
[22]
Fares D, Fathi M, Shams I, Mekhilef S. A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions. Energy Convers Manage 2021; 230: 113773.
[http://dx.doi.org/10.1016/j.enconman.2020.113773]
[23]
Seyedmahmoudian M, Kok Soon T, Jamei E, et al. Maximum power point tracking for photovoltaic systems under partial shading conditions using bat algorithm. Sustainability 2018; 10(5): 1347.
[http://dx.doi.org/10.3390/su10051347]
[24]
Abdalla O, Rezk H, Ahmed EM. Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance. Sol Energy 2019; 180: 429-44.
[http://dx.doi.org/10.1016/j.solener.2019.01.056]
[25]
Kadri R, Andrei H, Gaubert JP, Ivanovici T, Champenois G, Andrei P. Modeling of the photovoltaic cell circuit parameters for optimum connection model and real-time emulator with partial shadow conditions. Energy 2012; 42(1): 57-67.
[http://dx.doi.org/10.1016/j.energy.2011.10.018]
[26]
Yang Y, Chen H, Heidari AA, Gandomi AH. Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 2021; 177: 114864.
[http://dx.doi.org/10.1016/j.eswa.2021.114864]
[27]
Ma BJ, Liu S, Heidari AA. Multi-strategy ensemble binary hunger games search for feature selection. Knowl Base Syst 2022; 248: 108787.
[http://dx.doi.org/10.1016/j.knosys.2022.108787]
[28]
Abd Elaziz M, Abo Zaid EO, Al-qaness MAA, Ibrahim RA. Automatic superpixel-based clustering for color image segmentation using q-generalized Pareto distribution under linear normalization and hunger games search. Mathematics 2021; 9(19): 2383.
[http://dx.doi.org/10.3390/math9192383]
[29]
Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M. Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 2020; 94: 103731.
[http://dx.doi.org/10.1016/j.engappai.2020.103731]