In the whole process of power plant construction and operation, it is very important to calculate the power generation through artificial intelligence. Tracking the maximum power point in a photovoltaic power generation system, can make photovoltaic power generation systems maintain peak power operation when solar energy and ambient temperature change. In the photovoltaic system, the control strategy mainly depends on the perturbation observation method. The traditional perturbation and observation method is easy to implement, but it also has some problems, such as steady-state oscillation or insufficient tracking speed. To solve these problems, this paper proposes an innovative method based on traditional disturbance observation methods. A new variable step-size disturbance observation method and particle swarm optimization algorithm are introduced. Compared with traditional methods, this new method has better performance in tracking maximum power points. It not only reduces the steadystate oscillation, but also completes the task with higher speed and accuracy. Through simulation experiments, this paper verifies the advancement and effectiveness of these two new methods in detail.


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    Titel :

    Maximum Power Tracking Control Strategy of Photovoltaic Power Generation System


    Beteiligte:
    Ma, Jiayu (Autor:in) / Zhang, Shengguo (Autor:in) / Hu, Jie (Autor:in) / Zhu, Jiaran (Autor:in) / Yu, Changxun (Autor:in) / Ma, Chao (Autor:in)


    Erscheinungsdatum :

    11.10.2023


    Format / Umfang :

    2658895 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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