An Immune Firefly Algorithm for Tracking the Maximum Power Point of PV Array under Partial Shading Conditions

Mingrui Zhang, Zheyang Chen and Li Wei
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Mingrui Zhang: Department of Electrical Engineering, Tongji University, Shanghai 201804, China
Zheyang Chen: Department of Electrical Engineering, Tongji University, Shanghai 201804, China
Li Wei: Department of Electrical Engineering, Tongji University, Shanghai 201804, China

Energies, 2019, vol. 12, issue 16, 1-15

Abstract: Photovoltaic (PV) string exhibits complex multiple-peak characteristics under various partial shading conditions (PSC). If the maximum power point tracking cannot be achieved quickly and accurately, it will lead to a large amount of energy loss. Therefore, it has become a hot topic to study a reliable maximum power tracking control algorithm to ensure the PV system can still output maximum power under PSC. This paper proposes an immune firefly algorithm (IFA), which utilizes vaccine data-base to shorten the convergence time, eliminates the influence of bad individuals in time by immune replenishment operation, and reduces the steady-state oscillation by the improving iteration formula. The simulations in static and dynamic environments verify that the immune firefly algorithm can track the maximum power point under various partial shading conditions. Compared with conventional firefly algorithm (FA), IFA has faster convergence speed, and can effectively restrain the oscillation of voltage and power.

Keywords: photovoltaic (PV); Firefly algorithm (FA); maximum power point tracking (MPPT); partial shading conditions (PSC) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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