PSO-AFSA Global Maximum Power Point Tracking Algorithm with Adaptive Evolutionary Strategy for PV 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. PSO-AFSA (Particle Swarm Optimization Artificial Fish Swarm Algorithm) 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 modified AESPSO-AFSA GMPPT algorithm is proposed in this paper by introducing the evolution strategy into PSO-AFSA algorithm. Simulation results show that under NUIC, compared with the conventional P&O and PSO-AFSA algorithm, the proposed algorithm has well performance on getting out of the local optimal solution and improving the global optimal solution of the individual neighborhood, the convergence speed and convergence accuracy are also increased.
KeywordsPV system GMPPT PSO-AFSA Adaptive evolutionary strategy
This work was supported by National Natural Science Foundation of China (No. 61501106), Science and Technology Foundation of Jilin Province (No. 20180101039JC and JJKH20170102KJ).
- 1.Hu, P.: Geothermal-solar combined organic Rankine cycle power generation technology research. J. Northeast. Dianli Univ. 35(5), 41–45 (2015)Google Scholar
- 4.Li, X.: A comparative study of the main incremental conductance-based MPPT techniques for PV applications. Power Electron. 50(12), 91–98 (2016)Google Scholar
- 5.Pragallapati, N.: Distributed PV power extraction based on a modified interleaved SEPIC for nonuniform irradiance conditions. IEEE J. PVs 5(5), 1442–1453 (2015)Google Scholar
- 6.Ishaque, K.: A deterministic particle swarm optimization maximum power point tracker for PV system under partial shading condition. IEEE Trans. Ind. Electron. 60(8), 3195–3206 (2013)Google Scholar
- 8.Lian, K.: A maximum power point tracking method based on Perturb-and-Observe combined with particle swarm optimization. IEEE J. PVs 4(2), 626–633 (2014)Google Scholar
- 9.Dang, K.: Simulation study of improved single neuron in photovoltaic power generation system. J. Northeast. Dianli Univ. 33(Z1), 99–102 (2013)Google Scholar
- 10.Yin, L.: Three-step MPPT algorithm for photovoltaic arrays with local shadows. J. Northeast. Electr. Power Univ. 37(6), 15–20 (2017)Google Scholar
- 12.Duan, Q.: An intelligent algorithm for maximum power point tracking in PV system under partial shading conditions. Trans. Inst. Meas. Control. 39(2), 1–13 (2016)Google Scholar