Global maximum power point tracking algorithm for solar power system
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.
PV system NUIC PSO AFSA GMPPT
Jianpo Li Pengwei Dong Fuxin Liu Songjun Pan Cong Zheng
School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R.China;Global Ener School of Computer Science, Northeast Electric Power University, Jilin 132012, P.R.China Global Energy Interconnection Development and Cooperation Organization, Beijing 100031, P.R.China
国际会议
法国巴黎
英文
408-415
2019-03-13（万方平台首次上网日期，不代表论文的发表时间）