MPPT control based on improved mayfly optimization algorithm under complex shading conditions
-
Xingmin He
,
Baina He
,
Yunwei Zhao
Abstract
The output power curve of the photovoltaic array presents a multi-peak characteristic under partial shading conditions, which causes the traditional maximum power point tracking technology to fail to guarantee the maximum power output of the photovoltaic cell. In response to this problem, this paper proposes to apply the improved Mayfly Optimization Algorithm (MA) to the maximum power tracking control. By introducing the gravity factor and limiting the search space of male mayfly, the optimization accuracy of the algorithm is enhanced, the vibration of the algorithm near the MPP is reduced, and the occurrence of premature phenomenon is avoided. Three test functions are selected to verify the algorithm, and under the conditions of rapid irradiance changes and complex shadow occlusion, an MPPT model based on the Boost circuit is established to verify the effectiveness of the algorithm. The simulation results show that the improved MA algorithm can effectively converge to MPP under complex shading conditions, and the output efficiency of photovoltaic arrays can be maintained above 99.96%. The average tracking time for different shading patterns is about 0.15 s.
Funding source: Shandong Province Graduate Education Quality Improvement Program
Award Identifier / Grant number: SDYKC19103
Funding source: Zibo Science and Technology Planning Project
Award Identifier / Grant number: 2018kj010141
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: This Project Supported by Shandong Province Graduate Education Quality Improvement Program (SDYKC19103) and Zibo Science and Technology Planning Project (2018kj010141).
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Ayang, A, Wamkeue, R, Ouhrouche, M, Djongyang, N, Salomé, NE, Pombe, JK, et al.. Maximum likelihood parameters estimation of single-diode model of photovoltaic generator. Renew Energy 2019;130:111–21. https://doi.org/10.1016/j.renene.2018.06.039.Search in Google Scholar
2. Dhimish, M. Assessing MPPT techniques on hot-spotted and partially shaded photovoltaic modules: comprehensive review based on experimental data. IEEE Trans Electron Dev 2019;66:1132–44. https://doi.org/10.1109/ted.2019.2894009.Search in Google Scholar
3. Liu, Y-H, Chen, J-H, Huang, J-W. A review of maximum power point tracking techniques for use in partially shaded conditions. Renew Sustain Energy Rev 2015;41:436–53. https://doi.org/10.1016/j.rser.2014.08.038.Search in Google Scholar
4. Aquib, M, Jain, S, Agarwal, V. A time-based global maximum power point tracking technique for PV system. IEEE Trans Power Electron 2020;35:393–402. https://doi.org/10.1109/tpel.2019.2915774.Search in Google Scholar
5. Rezk, H, AL-Oran, M, Gomaa, MR, Tolba, MA, Fathy, A, Abdelkareem, MA, et al.. A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system. Renew Sustain Energy Rev 2019;115:109372. https://doi.org/10.1016/j.rser.2019.109372.Search in Google Scholar
6. Prasanth Ram, J, Rajasekar, N. A novel flower pollination based global maximum power point method for solar maximum power point tracking. IEEE Trans Power Electron 2017;32:8486–99. https://doi.org/10.1109/tpel.2016.2645449.Search in Google Scholar
7. Seyedmahmoudian, M, Rahmani, R, Mekhilef, S, Maung Than Oo, A, Stojcevski, A, Soon, TK, et al.. Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method. IEEE Trans Sustain Energy 2015;6:850–62. https://doi.org/10.1109/tste.2015.2413359.Search in Google Scholar
8. Tey, KS, Mekhilef, S. Modified incremental conductance algorithm for photovoltaic system under partial shading conditions and load variation. IEEE Trans Ind Electron 2014;61:5384–92. https://doi.org/10.1109/tie.2014.2304921.Search in Google Scholar
9. Tey, KS, Mekhilef, S. Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level. Sol Energy 2014;101:333–42. https://doi.org/10.1016/j.solener.2014.01.003.Search in Google Scholar
10. Shuaiqi, Z, Hui, X, Zhongbing, L, Zijia, Z. Photovoltaic maximum power point tracking under partial shading based on CSA-IP&O. Power Syst Protect Control 2020;48:26–32.Search in Google Scholar
11. Surender Reddy, S, Bijwe, PR, Abhyankar, AR. Faster evolutionary algorithm based optimal power flow using incremental variables. Int J Electr Power Energy Syst 2014;54:198–210. https://doi.org/10.1016/j.ijepes.2013.07.019.Search in Google Scholar
12. Surender Reddy, S, Bijwe, PR. Multi-objective optimal power flow using efficient evolutionary algorithm. Int J Emerg Elec Power Syst 2017;18. https://doi.org/10.1515/ijeeps-2016-0233.Search in Google Scholar
13. Li, W, Zhang, G, Pan, T, Zhang, Z, Geng, Y, Wang, J. A Lipschitz optimization-based MPPT algorithm for photovoltaic system under partial shading condition. IEEE Access 2019;2019:126323–33. https://doi.org/10.1109/access.2019.2939095.Search in Google Scholar
14. Zhongqiang, W, Danqi, Y, Xiaohua, K. Application of improved chicken swarm optimization for MPPT in photovoltaic system. Acta Energiae Solaris Sin 2019;40:1589–98.Search in Google Scholar
15. Mansoor, M, Mirza, AF, Ling, Q, Yaqoob, MY. Novel Grass Hopper optimization based MPPT of PV systems for complex partial shading conditions. Sol Energy 2020;198:499–518. https://doi.org/10.1016/j.solener.2020.01.070.Search in Google Scholar
16. Rizzo, SA, Scelba, G. ANN based MPPT method for rapidly variable shading conditions. Appl Energy 2015;5:124–32. https://doi.org/10.1016/j.apenergy.2015.01.077.Search in Google Scholar
17. Ibrahim, A, Obukhov, S, Aboelsaud, R. Determination of global maximum power point tracking of PV under partial shading using cuckoo search algorithm. Appl Sol Energy 2019;55:444–52. https://doi.org/10.3103/s0003701x19060045.Search in Google Scholar
18. Bhukya, L, Anil, A, Venkata, SN. A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids. Int J Hydrogen Energy 2021;46:10653–65.10.1016/j.ijhydene.2020.12.158Search in Google Scholar
19. Guo, L, Meng, Z, Sun, Y, Wang, L. A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition. Energy 2018;144. https://doi.org/10.1016/j.energy.2017.12.059.Search in Google Scholar
20. Shams, I, Mekhilef, S, Tey, KS. Maximum power point tracking using modified butterfly optimization algorithm for partial shading, uniform shading, and fast varying load conditions. IEEE Trans Power Electron 2021;36:5569–81. https://doi.org/10.1109/tpel.2020.3029607.Search in Google Scholar
21. Mirza, AF, Ling, Q, Javed, MY, Mansoor, M. Novel MPPT techniques for photovoltaic systems under uniform irradiance and partial shading. Sol Energy 2019;184:628–48.10.1016/j.solener.2019.04.034Search in Google Scholar
22. Li, H, Yang, D, Su, W, Lv, J, Yu, X. An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading. IEEE Trans Ind Electron 2019;66:265–75. https://doi.org/10.1109/tie.2018.2829668.Search in Google Scholar
23. Siqing, S, Yuliang, C, Jingjing, Z. Research on maximum power point tracking strategy based on differential evolution artificial bee colony algorithm of photovoltaic system. Power Syst Protect Contr 2018;46:23–9.Search in Google Scholar
24. Zhihao, W, Zicheng, L, Houneng, W, Qing, L. MPPT study of solar PV power system based on RBF neural network algorithm. Power Syst Protect Contr 2020;48:85–91.Search in Google Scholar
25. Titri, S, Larbes, C, Toumi, K, Benatchba, K. A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions. Appl Soft Comput 2017;58:465–79. https://doi.org/10.1016/j.asoc.2017.05.017.Search in Google Scholar
26. Zervoudakis, K, Tsafarakis, S. A mayfly optimization algorithm. Comput Ind Eng 2020;145:106559. https://doi.org/10.1016/j.cie.2020.106559.Search in Google Scholar
27. Podder, AK, Roy, NK, Pota, HR. MPPT methods for solar PV systems: a critical review based on tracking nature. IET Renew Power Gener 2019;3:1615–32. https://doi.org/10.1049/iet-rpg.2018.5946.Search in Google Scholar
28. Priyadarshi, N, Padmanaban, S, Holmnielsen, J, Blaabjerg, F, Sagar Bhaskar, M. An experimental estimation of hybrid ANFIS–PSO-based MPPT for PV grid integration under fluctuating sun irradiance. IEEE Syst J 2020;14:1218–29. https://doi.org/10.1109/jsyst.2019.2949083.Search in Google Scholar
29. Tubniyom, C, Chatthaworn, R, Suksri, A, Wongwuttanasatian, T. Minimization of losses in solar photovoltaic modules by reconfiguration under various patterns of partial shading. Energies 2019;12:24.10.3390/en12010024Search in Google Scholar
30. Wen, Z, Chen, J, Cheng, X, Niu, H, Luo, X. A new and simple split series strings approach for adding bypass diodes in shingled cells modules to reduce shading loss. Sol Energy 2019;184:497–507. https://doi.org/10.1016/j.solener.2019.03.099.Search in Google Scholar
31. Mohamed, MA, Diab, AAZ, Rezk, H. Partial shading mitigation of PV systems via different meta-heuristic techniques. Renew Energy 2019;130:1159–75. https://doi.org/10.1016/j.renene.2018.08.077.Search in Google Scholar
32. Abedinia, O, Amjady, N, Ghasemi, A. A new metaheuristic algorithm based on shark smell optimization. Complexity 2016;21:97–116. https://doi.org/10.1002/cplx.21634.Search in Google Scholar
33. Mohanty, S, Subudhi, B, Ray, PK. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Trans Sustain Energy 2016;7:181–8. https://doi.org/10.1109/tste.2015.2482120.Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes
- MPPT control based on improved mayfly optimization algorithm under complex shading conditions
- A cogeneration scheme with biogas and improvement of frequency stability using inertia based control in AC microgrid
- Profit evaluation inclusive of reserve pricing for renewable-integrated GENCOs
- An ideal solution for the deployment of photovoltaic generators using agent-based Nash Differential Evolution (NashDE) algorithm
- Design and operation of smart hybrid microgrid
- Three-state switching cell boost converter using H-inf controller
- Bayesian optimization based machine learning approaches for prediction of plug-in electric vehicle state-of-charge
- Probabilistic and deterministic analysis of single diode model of a solar cell: a case study
- A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage
- A non-cooperative game based energy management considering distributed energy resources in price-based and incentive-based demand response program
Articles in the same Issue
- Frontmatter
- Research Articles
- Efficient power scheduling in smart homes using a novel artificial ecosystem optimization technique considering two pricing schemes
- MPPT control based on improved mayfly optimization algorithm under complex shading conditions
- A cogeneration scheme with biogas and improvement of frequency stability using inertia based control in AC microgrid
- Profit evaluation inclusive of reserve pricing for renewable-integrated GENCOs
- An ideal solution for the deployment of photovoltaic generators using agent-based Nash Differential Evolution (NashDE) algorithm
- Design and operation of smart hybrid microgrid
- Three-state switching cell boost converter using H-inf controller
- Bayesian optimization based machine learning approaches for prediction of plug-in electric vehicle state-of-charge
- Probabilistic and deterministic analysis of single diode model of a solar cell: a case study
- A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage
- A non-cooperative game based energy management considering distributed energy resources in price-based and incentive-based demand response program
