Tao Hou1, Shan Wang This email address is being protected from spambots. You need JavaScript enabled to view it.1, Mingxi Chen1, and Yanzhang Zhao1

1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China


Received: April 11, 2022
Accepted: June 6, 2022
Publication Date: August 19, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202305_26(5).0012  


The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo Search Algorithm based on the Gravitational Acceleration Mechanism (CSAGAM) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 65% compared with the traditional Cuckoo Search Algorithm (CSA), and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization (PSO) and the Gravitational Search Algorithm (GSA). CSAGAM has an average tracking efficiency of 99.99% and an average tracking time of 0.15s when tracking the GMPP, which improves PV power generation’s efficiency and power quality.

Keywords: partial shading condition; PV system; cuckoo search algorithm; gravitational search algorithm; global maximum power point


  1. [1] H. Bakir and A. A. Kulaksiz, (2020) “Modelling and voltage control of the solar-wind hybrid micro-grid with optimized STATCOM using GA and BFA" Engineering Science and Technology, an International Journal 23(3): 576–584. DOI: 10.1016/j.jestch.2019.07.009.
  2. [2] E. B. Agyekum, (2021) “Techno-economic comparative analysis of solar photovoltaic power systems with and without storage systems in three different climatic regions, Ghana" Sustainable Energy Technologies and Assessments 43: DOI: 10.1016/j.seta.2020.100906.
  3. [3] Y. Zhang, J. Ren, Y. Pu, and P. Wang, (2020) “Solar energy potential assessment: A framework to integrate geographic, technological, and economic indices for a potential analysis" Renewable Energy 149: 577–586. DOI:10.1016/j.renene.2019.12.071.
  4. [4] M. Kamalesh, S. Nattuthurai, B. Chokkalingam, and L. Mihet-Popa, (2021) “Mitigation of circulating current with effective energy management in low-power PVFC-battery-microgrid" International Transactions on Electrical Energy Systems 31(6): DOI: 10.1002/2050-7038.12899.
  5. [5] M. Ali, M. Tariq, K. A. Lodi, R. K. Chakrabortty, M. J. Ryan, B. Alamri, and C. Bharatiraja, (2021) “Robust ANN-Based Control of Modified PUC-5 Inverter for Solar PV Applications" IEEE Transactions on Industry Applications 57(4): 3863–3876. DOI: 10.1109/TIA.2021.3076032.
  6. [6] C. Bharatiraja, G. Ramanathan, and G. S. Ilango. “A Stand Alone Building Integrated PV Tied Bidirectional Capability Direct DC Electric Vehicle Charging System through Z-Source Inverter Impedance Network Capacitors”. In: 107. 1. Cited by: 0. 2022, 10097–10106. DOI: 10.1149/10701.10097ecst.
  7. [7] P. Madasamy, R. Verma, A. Rameshbabu, A. Murugesan, R. Umamageswari, J. L. Munda, C. Bharatiraja, and L. Mihet-Popa, (2021) “Neutral point clamped transformer-less multilevel converter for grid-connected photovoltaic system" Electronics (Switzerland) 10(8): DOI: 10.3390/electronics10080977.
  8. [8] K. A. Lodi, M. Tariq, M. M. Roomi, and C. Bharatiraja. “GA optimized SHE and proportional resonant controller tuned integration of solar PV to the AC grid using packed U cell inverter”. In: 2020-January. Cited by: 0. 2020. DOI: 10.1109/PEDES49360.2020.9379439.
  9. [9] H. Labar and M. S. Kelaiaia, (2018) “Real time partial shading detection and global maximum power point tracking applied to outdoor PV panel boost converter" Energy Conversion and Management 171: 1246–1254. DOI: 10.1016/j.enconman.2018.06.038.
  10. [10] M. Lasheen and M. Abdel-Salam, (2018) “Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: A review and a novel hybrid approach" Energy Conversion and Management 171: 1002–1019. DOI: 10.1016/j.enconman.2018.06.003.
  11. [11] S. Salman, X. Ai, and Z.Wu, (2018) “Design of a P-&-O algorithm based MPPT charge controller for a stand-alone 200W PV system" Protection and Control of Modern Power Systems 3(1): DOI: 10.1186/s41601-018-0099-8.
  12. [12] S. Tang, Y. Xing, L. Chen, X. Song, and F. Yao, (2021) “Review and a novel strategy for mitigating hot spot of PV panels" Solar Energy 214: 51–61. DOI: 10.1016/j.solener.2020.11.047.
  13. [13] H. Rezk, M. Aly, M. Al-Dhaifallah, and M. Shoyama, (2019) “Design and Hardware Implementation of New Adaptive Fuzzy Logic-Based MPPT Control Method for Photovoltaic Applications" IEEE Access 7: 106427–106438. DOI: 10.1109/ACCESS.2019.2932694.
  14. [14] P. Jiang, Y. Luan, W. Zhang, J. Tian, and J. Dai, (2021) “A study of multi-peak MPPT based on improved PSO under partial shading." Solar Energy 42(08): 140–145.
  15. [15] N. Kalaiarasi, S. S. Dash, S. Paramasivam, and C. Bharatiraja, (2021) “Investigation on ANFIS aided MPPT technique for PV fed ZSI topologies in standalone applications" Journal of Applied Science and Engineering (Taiwan) 24(2): 261–269. DOI: 10.6180/jase.202104_24(2).0015.
  16. [16] B. Yang, T. Yu, X. Zhang, H. Li, H. Shu, Y. Sang, and L. Jiang, (2019) “Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition" Energy Conversion and Management 179: 286–303. DOI: 10.1016/j.enconman.2018.10.074.
  17. [17] B. Yang, L. Zhong, X. Zhang, H. Shu, T. Yu, H. Li, L. Jiang, and L. Sun, (2019) “Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition" Journal of Cleaner Production 215: 1203–1222. DOI: 10.1016/j.jclepro.2019.01.150.
  18. [18] X. Zhang, S. Li, T. He, B. Yang, T. Yu, H. Li, L. Jiang, and L. Sun, (2019) “Memetic reinforcement learning based maximum power point tracking design for PV systems under partial shading condition" Energy 174: 1079–1090. DOI: 10.1016/j.energy.2019.03.053.
  19. [19] S. Titri, C. Larbes, K. Y. Toumi, and K. Benatchba, (2017) “A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions" Applied Soft Computing Journal 58: 465–479. DOI: 10.1016/j.asoc.2017.05.017.
  20. [20] N. Kumar, I. Hussain, B. Singh, and B. K. Panigrahi, (2017) “MPPT in Dynamic Condition of Partially Shaded PV System by Using WODE Technique" IEEE Transactions on Sustainable Energy 8(3): 1204–1214. DOI: 10.1109/TSTE.2017.2669525.
  21. [21] R. Sridhar, S. Jeevananthan, S. Dash, and P. Vishnuram, (2017) “A new maximum power tracking in PV system during partially shaded conditions based on shuffled frog leap algorithm" Journal of Experimental and Theoretical Artificial Intelligence 29(3): 481–493. DOI: 10.1080/0952813X.2016.1186750.
  22. [22] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, (2009) “GSA: A Gravitational Search Algorithm" Information Sciences 179(13): 2232–2248. DOI: 10.1016/j.ins.2009.03.004.
  23. [23] A. Lou, M. Yao, W. Jia, and D. Yuan, (2020) “Adaptive gravitational search algorithm improved by hybrid methods" Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 42(1): 148–156. DOI: 10.3969/j.issn.1001-506X.2020.01.20.
  24. [24] X.-S. Yang and S. Deb, (2010) “Engineering optimisation by cuckoo search" International Journal of Mathematical Modelling and Numerical Optimisation 1(4): 330–343. DOI: 10.1504/IJMMNO.2010.035430.
  25. [25] S. Yazdani, H. Nezamabadi-Pour, and S. Kamyab, (2014) “A gravitational search algorithm for multimodal optimization" Swarm and Evolutionary Computation 14: 1–14. DOI: 10.1016/j.swevo.2013.08.001.


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