Photovoltaic MPPT Strategy Based on Hybrid Particle Swarm Optimization Algorithm and Grey Wolf Algorithm

In partially shaded environments, the output power characteristic curve of a PV generation system shows multiple partial peaks. The conventional maximum power point tracking (MPPT) control methods are prone to tracking only the partial extreme points. In order to improve the tracking accuracy and ef...

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Bibliographic Details
Published in2023 5th International Conference on Smart Power & Internet Energy Systems (SPIES) pp. 216 - 221
Main Authors Wu, Chenyang, Zhan, Huamao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2023
Subjects
Grey Wolf Optimization algorithm
Heuristic algorithms
Mathematical models
Maximum power point trackers
maximun power point tracking
Particle Swarm Optimization algorithm
photovoltaic
Photovoltaic systems
Reliability
Simulation
Software packages
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Summary:In partially shaded environments, the output power characteristic curve of a PV generation system shows multiple partial peaks. The conventional maximum power point tracking (MPPT) control methods are prone to tracking only the partial extreme points. In order to improve the tracking accuracy and efficiency of the MPPT control method, the output power characteristics of PV arrays under different light intensity conditions are analysed in this paper. According to the distribution characteristics of each extreme point, a MPPT method using a hybrid Particle Swarm Optimization (PSO) and Grey Wolf Optimization algorithm (GWO) is proposed. Meanwhile, the effectiveness of the proposed method is verified by using the simulation model built in MATLAB/ Simulink. The results show that the algorithm proposed in this paper has higher tracking accuracy and faster convergence speed, so the method can effectively achieve the tracking of the global maximum output power point of the PV array.
DOI:10.1109/SPIES60658.2023.10474932