A global optimization method for multiple peak photovoltaic MPPT

Under partial shading condition or imbalanced temperature distribution, the power-voltage characteristic curve of photovoltaic (PV) array exhibits complicated multiple peaks. Traditional maximum power point tracking (MPPT) method is usually easy to trap into local extreme power point. To solve this...

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Bibliographic Details
Published in:IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society pp. 554 - 559
Main Authors: Yuling Li, Dianjun Ju, Yimeng Wang, Jian-er Wu, Zhidong Dong
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2017
Subjects:
complicated multiple peak
Mathematical model
Maximum power point trackers
maximum power point tracking (MPPT)
Optimization
photovoltaic array
quantum-behaved particle swarm optimization algorithm (QPSO)
Sociology
Statistics
Voltage control
Voltage measurement
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Summary:Under partial shading condition or imbalanced temperature distribution, the power-voltage characteristic curve of photovoltaic (PV) array exhibits complicated multiple peaks. Traditional maximum power point tracking (MPPT) method is usually easy to trap into local extreme power point. To solve this issue, this paper proposes a novel MPPT method using quantum-behaved particle swarm optimization (QPSO) algorithm. This algorithm adopts detect-search strategy based on voltage closed-loop control, making voltage particles search photovoltaic characteristic curves in quantum-behaved paths. By setting a modulation strategy of reunion and separation factor and stopping criterion, the proposed QPSO algorithm improves the precision of stable state, and eliminates power oscillation under stability condition. Experimental results show that QPSO algorithm has good performance with less parameters, superior global optimization ability, favorable stability and better adaptability compared with traditional particle swarm optimization (PSO) algorithm in PV MPPT.
DOI:10.1109/IECON.2017.8216097