Abstract
The P-U curve of the PV (photovoltaic) system has multi-peak characteristics under non-uniform irradiance conditions (NUIC). The conventional MPPT algorithm can only track the local maximum power points, therefore, PV system fails to work at the global optimum, causing serious energy loss. How to track its global maximum power point is of great significance for the PV system to maintain an efficient output state. Artificial Fish Swarm Algorithm (AFSA) is a global maximum power point tracking (GMPPT) algorithm with strong global search capability, but the convergence speed and accuracy of the algorithm are limited. To solve the mentioned problems, a Hybrid Artificial Fish Swarm Algorithm (HAFSA) for GMPPT is proposed in this paper by using formulation of the Particle Swarm Optimization (PSO) to reformulate the AFSA and improving the principal parameters of the algorithm. Simulation results show that when under NUIC, compared with the PSO and AFSA algorithm, the proposed algorithm has well performance on the convergence speed and convergence accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, C., Cao, P., Li, J., Zhao, B.: Review on reactive voltage control methods for large-scale distributed PV integrated grid. J. Northeast Electr. Power Univ. 37(2), 82–88 (2017)
Wang, H., Chen, Y., Li, G., Zhuang, G.: Solution of voltage beyond limits in distribution network with large scale distributed photovoltaic generators. J. Northeast Electr. Power Univ. 37(6), 8–14 (2017)
Femia, N., Petrone, G., Spagnuolo, G.: Optimization of perturb and observe maximum power point tracking method. IEEE Trans. Power Electron. 20(4), 963–973 (2005)
Sera, D., Mathe, L., Kerekes, T.: On the perturb-and-observe and incremental conductance MPPT methods for PV systems. IEEE J. Photovolt. 3(3), 1070–1078 (2013)
Roman, E., Alonso, R., Ibanez, P.: Intelligent PV module for grid-connected PV systems. IEEE Trans. Ind. Electron. 53(4), 1066–1073 (2006)
Manickam, C., Raman, G.R., Raman, G.P.: A hybrid algorithm for tracking of GMPP based on P&O and PSO with reduced power oscillation in string inverters. IEEE Trans. Ind. Electron. 63(10), 6097–6106 (2016)
Oliveira, F.M.D., Silva, S.A.O.D., Durand, F.R.: Grid-tied photovoltaic system based on PSO MPPT technique with active power line conditioning. IET Power Electron. 9(6), 1180–1191 (2016)
Yang, M., Huang, X., Su, X.: Study on ultra-short term prediction method of photovoltaic power based on ANFIS. J. Northeast Electr. Power Univ. 38(4), 14–18 (2018)
Yin, L., Lv, L., Lei, G.: Three-step MPPT algorithm for photovoltaic arrays with local shadows. J. Northeast Electr. Power Univ. 37(6), 15–20 (2017)
Miyatake, M., Veerachary, M., Toriumi, F.: Maximum power point tracking of multiple photovoltaic arrays: a PSO approach. IEEE Trans. Aerosp. Electron. Syst. 47(1), 367–380 (2011)
Lyden, S., Haque, M.E.: A simulated annealing global maximum power point tracking approach for PV modules under partial shading conditions. IEEE Trans. Power Electron. 31(6), 4171–4181 (2016)
Acknowledgements
This work was supported by “Research on Lightweight Active Immune Technology for Electric Power Supervisory Control System”, a science and technology project of State Grid Co., Ltd in 2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Guan, T. et al. (2020). Global Maximum Power Point Tracking Algorithm for Solar Power System. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 157. Springer, Singapore. https://doi.org/10.1007/978-981-13-9710-3_26
Download citation
DOI: https://doi.org/10.1007/978-981-13-9710-3_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9709-7
Online ISBN: 978-981-13-9710-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)