Document Type : Original Article

Authors

1 Poornima College of Engineering, Jaipur, India.

2 Global Institute of Technology, Jaipur, India.

10.22044/rera.2023.12408.1180

Abstract

Most of the partial shading maximum power point tracking methods have been designed for the static shading pattern of the partial shading conditions, however, the irradiance pattern may change further when in partial shading mode. Therefore, to cover this research gap, a global maximum power point control under varying irradiance (GCVI) algorithm is proposed in this paper. The algorithm does not use any sensors to detect the change in the irradiance, instead, the change in the current values of the modules are continuously monitored to detect the change. The reference voltages across which the peaks on the power curve are scanned are obtained from the reference voltage generation process, the consideration of these reference points avoids the excessive power losses in the system. The verification of the working of the proposed algorithm is carried out by simulating the photovoltaic system model on SIMULINK in MATLAB software. Simulations are carried out in various scenarios to show the effectiveness of the control. The simulation results illustrate that with the change in the global maximum under partial shading, the system successfully retunes to the new maximum point; the maximum point retunes from 10 kW to 9.2 kW and from 13.8 kW to 11.5 kW for two different case scenarios. Further, the comparisons are also carried out with the previously reported methods.

Keywords

Main Subjects

[1] Dubey R, Joshi D, and Bansal RC. (2016). Optimization of Solar Photovoltaic Plant and Economic Analysis. Electr. Power Components Syst., Vol. 44, No. 5, pp. 2025-2035.
 
[2] Esram T and Chapman PL. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion, Vol. 22, No. 2, pp. 439-449.
 
[3] Hussein KH. (1995). Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions. IEEE Proceedings - Generation, Transmission and Distribution, Vol. 142, No. 1, pp. 59.
 
[4] Ahmad R, Murtaza AF, and Sher HA. (2016). Power tracking techniques for efficient operation of photovoltaic array in solar applications – a review. Renewable and Sustainable Energy Reviews, Vol. 44, No. 18, pp. 82-102.
 
[5] Eltamaly AM and Abdelaziz AY. (2020). Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems. Springer Nature, Gewerbestrasse 11, 6330 Cham, Switzerland.
 
[6] Bhatnagar P and Nema RK. (2017). Maximum power point tracking control techniques: state-of-the-art in photovoltaic applications. Renewable and Sustainable Energy Reviews, vol. 23, pp. 224-241.
 
[7] Lupangu C and Bansal RC. (2017). A review of technical issues on the development of solar photovoltaic systems. Renewable and Sustainable Energy Reviews, Vol. 73, pp. 950-965.
 
[8] Kennedy J and Eberhart R. (1994). Particle swarm optimization. Proceedings of International Conference on Neural Networks (ICNN), pp. 1942-1948.
 
[9] Tey, K.S., Mekhilef, S., Seyedmahmoudian, M., Horan, B.; Oo, A.T.; and Stojcevski (2018). A. Improved differential evolution-based MPPT algorithm using SEPIC for PV systems under partial shading conditions and load variation. IEEE Transactions on Industrial Informatics, Vol. 14, No. 10, pp. 4322-4333.
 
[10] Peng, B.R., Ho, K.C., and Liu, Y.H. (2018). A novel and fast MPPT method suitable for both fast changing and partially shaded conditions. IEEE Transactions on Industrial Electronics, Vol. 65, No. 4, pp. 3240-3251.
 
[11] Li, G.; Jin, Y., Akram, M.W., Chen, X., and Ji, J. (2018). Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions-A review. Renewable and Sustainable Energy Reviews, Vol. 81, pp. 840-873.
 
[12] Luo, S., Zhang, L., and Fan, Y. (2019). Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. Journal of Cleaner Production, Vol. 234, pp. 1365-1384.
 
[13] Arora, S. and Singh, S. (2019). Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, Vol. 23, No. 3, pp. 715-734.
 
[14] Aygül, K., Cikan, M., Demirdelen, T., and Tumay, M. (2019). Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 19, pp. 1-19.
 
[15] Aouchiche, N., Aitcheikh, M.S., Becherif, M., and Ebrahim, M.A. (2018). AI-based global MPPT for partial shaded grid connected PV plant via MFO approach. Solar Energy. Vol. 171, pp. 593-603.
 
[16] Liu, D., Fan, Z., Fu, Q., Li, M., Faiz, M.A., Ali, S., Khan, M.I. (2019). Random forest regression evaluation model of regional flood disaster resilience based on the whale optimization algorithm. Journal of Cleaner Production, Vol. 250, 119468.
 
[17] Premkumar, M. and Sumithira, T.R. (2018). Humpback whale assisted hybrid maximum power point tracking algorithm for partially shaded solar photovoltaic systems. Journal of Power Electronics, Vol. 18, No. 6, pp. 1805-1818.
 
[18] Farzaneh, J., Keypour, R., and Khanesar, M.A. (2018). A new maximum power point tracking based on modified firefly algorithm for PV system under partial shading conditions. Technology and Economics of Smart Grids and Sustainable Energy, Vol. 3, No. 1, pp. 9-23.
 
[19] Krishnan, S. and Sathiyasekar, K. (2019). A novel salp swarm optimization MPP tracking algorithm for the solar photovoltaic systems under partial shading conditions. Journal of Circuits, Vol. 29, No. 1, 2050017.
 
[20] Yang, B., Zhong, L.E., Yu, T., Li, H.F., Zhang, X.S., Shu, H.C., Sang, Y.Y., and Jiang, L. (2019). Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition. Journal of Cleaner Production, Vol. 215, pp. 1203-1222.
 
[21] Irsalinda, N., Thobirin, A., and Wijayanti, D.E. (2017). Chicken swarm as a multi-step algorithm for global optimization. International Journal of Engineering Science Invention, Vol. 6, No. 1, pp. 8-14.
 
[22] Wu, Z., Yu, D., and Kang, X. (2018). Application of improved chicken swarm optimization for MPPT in photovoltaic system. Optimal Control Applications and Methods, Vol. 39, No. 2, pp. 1029-1042.
 
[23] Pei, T., Hao, X., and Gu, Q. (2018). A novel global maximum power point tracking strategy based on modified flower pollination algorithm for photovoltaic systems under non-uniform irradiation and temperature conditions. Energies, Vol. 11, No. 10, pp. 2708-2725.
 
[24] Guo, L., Meng, Z., Sun, Y., Wang, L. (2018). A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition. Energy, vol. 144, pp. 501-514.
 
[25] Wang, F., Zhu, T., Zhuo, F., Yi, H., and Fan, Y. (2017). Enhanced simulated annealing-based global MPPT for different PV systems in mismatched conditions. Journal of Power Electronics, Vol. 17, No. 5, pp. 1327-1337.
 
[26] Li, L.L., Lin, G.Q. Tseng, M.L., Tan, K., and Lim, M.K. (2018). A maximum power point tracking method for PV system with improved gravitational search algorithm. Applied Soft Computing, Vol. 65, pp. 338-348.
 
[27] Abdalla, O., Rezk, H., and Ahmed, E.M. (2019). Wind driven optimization algorithm based global MPPT for PV system under non-uniform solar irradiance. Solar Energy, Vol. 180, pp. 429-444.
 
[28] Kumar, N., Hussain, I., Singh, B., and Panigrahi, B.K. (2017). MPPT in dynamic condition of partially shaded PV system by using WODE technique. IEEE Transactions on Sustainable Energy, Vol. 8, No. 3, pp. 1204-1241.
 
[29] Chen, L. and Wang, X. (2019). Enhanced MPPT method based on ANN-assisted sequential Monte-Carlo and quickest change detection. IET Smart Grid, Vol. 2, No. 4, pp. 635-644.
 
[30] Huang, Y.P., Chen, X., and Ye, C.E. (2018). A hybrid maximum power point tracking approach for photovoltaic systems under partial shading conditions using a modified genetic algorithm and the firefly algorithm. International Journal of Photoenergy, Vol. 2018, 7598653.
 
[31] Hiren Patel and Vivek Agarwal (2008). Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions. IEEE Transactions on Industrial Electronics, Vol. 55, No. 4, pp. 1689-1698.