Kang Peng, Aochen Wang, Yingjun Sang, Zhijie Ding, Heng Zhang and Yuanyuan Fan
Faculty of Automation, Huaiyin Institute of Technology, Huaian, China
Power generation based on solar photovoltaic principle has developed rapidly in recent years due to its advantages of no pollution and inexhaustible resources. The output characteristics of solar photovoltaic are susceptible to external environmental factors, therefore, the maximum power point needs to be tracked in real time so that the PV system works continuously at the maximum power point. In order to improve the speed and stability of the PV system tracking and reduce the power loss in the steady state, the perturbation and observation (P&O) method which is used commonly in Maximum Power Point Tracking (MPPT) control method is selected for analysis in this paper. In view of the shortcomings of its existing control methods, this paper will analyze and compare the algorithms based on three perturbation and observation strategies: fixed-step, variable-step and artificial neural networks. The simulation results indicate that the perturbation and observation method of variable-step and neural network track strategy are relatively fast, and the neural network control strategy is the most stable after tracking to the maximum power point.
Solar Power, MPPT, Perturbation and Observation Method, Artificial Neural Networks
Kang Peng, Aochen Wang, Yingjun Sang, Zhijie Ding, Heng Zhang and Yuanyuan Fan. Optimization and Simulation of Photovoltaic MPPT Control Strategies. Academic Journal of Energy (2022), Vol. 3, Issue 3: 68-77. https://doi.org/10.38007/RE.2022.030308.
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