Maximum photovoltaic power point tracking based on hybrid GWO-P&O algorithm under local shadow

Abstract

In view of the problem of multiple peak values of P-U characteristic curve under a local shading environment,the traditional gray wolf optimization (GWO) is slow in convergence speed and low in accuracy of steady state at the late stage when tracking the maximum power point. Combining the advantages of GWO and perturbation & observation (P&O) method,an improved hybrid maximum power point tracking(MPPT) algorithm based on GWO-P&O was proposed. Firstly, optimized by the GWO, the algorithm was gradually close to the global MPPT. Then, P&O was introduced into the GWO at the late convergence stage, so that the local maximum power point of of photovoltaic power can be found at a faster speed while maintaining a high steady-state accuracy of the GWO,which overcomes the shortcomings of the traditional GWO algorithm. Finally, the proposed method was compared with the GWO under different environments. The results show that the proposed GWO-P&O method can improve the convergence speed in the late stage of the GWO when tracking the maximum power while ensuring high steady-state accuracy.

Keywords

gray wolf optimization(GWO) / perturbation & observation(P&O) method / partial shading environment / hybrid maximum power point tracking (MPPT) algorithm / global maximum power point

References

[1] YUAN Q, ZHAO B, WANG L, et al. Photovoltaic mutipeak MPPT control based on TSA-P&O hybrid algorithm. Journal of Power Systems and Automation, 2021, 33(12): 1-9.

[2] OHAMMADREZA A, KAZEM P. A novel reconfiguration procedure to extract maximum power from partially-shaded photovoltaic arrays. Solar Energy, 2018, 173: 110-119.

[3] XI J Z, LI F, ZHANG X, et al. Rt-lab simulation analysis of annual generating capacity of photovoltaic cell based on centralized loss. Automation of Electric Power Systems, 2018, 42(8): 156-162.

[4] BOSCO M J, MABEL M C. A novel cross diagonal view configuration of a PV system under partial shading conditions. Solar Energy, 2017, 158: 760-773.

[5] BANA S, SAINI R P. Experimental investigation on power output of different photovoltaic array configurations under uniform and partial shading scenarios. Energy, 2017, 1127: 438-453.

[6] DALIENTO S, DINAPOLI F, GUERRIERO P, et al. A modified bypass circuit for improved hot spot reliability of solar panels subject to partial shading. Solar Energy, 2016, 134: 211-218.

[7] XIE X H, LAI J J. Review of global maximum power point tracking control methods for photovoltaic arrays in local shadow. Power Grid Technology, 2014, 38(12): 3279-3285.

[8] MOHANTY S, SUBUDHI B, RAY P K. A grey wolf-assisted perturb & observe MPPT algorithm for a PV system. IEEE Transactions on Energy Conversion, 2017, 32(1): 340-347.

[9] KEY K S, MEKHILEF S. Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level. Solar Energy, 2014, 133: 333-342.

[10] DILEEP G, SINGH S N. An improved particle swarm optimization based maximum power point tracking aigorithm for PV system operating under partial shading conditions. Solar Energy, 2017, 158: 1006-1015.

[11] SHENG S Q, CHEN Y L, ZHANG J J. Study on PV maximum power tracking strategy based on the differential evolution artificial swarm algorithm. Protection and Control of Electric Power System, 2018, 46(11): 23-29.

[12] GAVHANE P S, KRISHNAMURTHY S, DIXIT R, et al. EL-PSO based MPPT for solar PV under partial shaded condition. Energy Procedia, 2017, 117: 1047-1053.

[13] ELDAHABA Y E A, SAAD N H, ZEKRY A. Enhancing the tracking techniques for the global maximum power point under partial shading conditions. Renewable and Sustainable Energy Reviews, 2017, 73: 1173-1183.

[14] ELTAMALY A M, FARH H M H. Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC. Solar Energy, 2019, 177: 306-316.

[15] SOUFI Y, BECHOUAT M, KAHLA S. Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system. International Journal of Hydrogen Energy, 2017, 42: 8680-8688.

[16] LI G Q, JIN Y, AKRAM M W, et al. Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditionsa-A review. Renewable and Sustainable Energy Reviews, 2017, 42: 8680-8688.

[17] GAO Z Q, LI S, ZHOU X S, et al. Application of linear autoperturbation in PV power generation system MPPT. Protection and Control of electric Power System, 2018, 46(15): 52-59.

[18] RAJKUMAR M V, MAHAKUMAR M, MANOJKUMA R M, et al. A new DC-DC converter topology with grey wolf MPPT algorithm for photovoltaic syste. International Journal of Emerging Technology & Research, 2017, 5(4): 54-59.

[19] WU D, XU C Y, ZHENG L J, et al. Research on the maximum power point tracking algorithm of photovoltaic system based on β parameters. Solar Journal, 2020, 41(6): 234-241.

[20] LI S Y, ZHANG W B, ZHAO X Z, et al. Improved whale algorithm optimized to support vector regression for photovoltaic maximum power point tracking. Electro-technical Journal, 2021, 36(9): 1771-1781.

[21] LI Z J, ZHANG Y N, WANG L J, et al. Multimodal MPPT study based on improved quantum particle group algorithm. Solar Journal, 2021, 42(5): 221-229.

[22] ZHANG Q J, WANG K L, FANG X Q. Multipeak MPPT of photovoltaic array based on GWO algorithm. Journal of Jilin University (Natural Science Edition), 2018, 56(6): 1526-1532.

[23] ZHAO S Q, XIAO H, LIU Z B, et al. Maximum power point tracking of PV under partial shading based on CSA-IP&O. Protection and Control of Power System, 2020, 48(5): 26-32.