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>Učinkoviti optimalni regulator uzlaznog DC/DC pretvarača za sustav fotonaponskih ćelija za praćenje točke maksimalne snage temeljen na umjetnim neuronskim mrežama
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Učinkoviti optimalni regulator uzlaznog DC/DC pretvarača za sustav fotonaponskih ćelija za praćenje točke maksimalne snage temeljen na umjetnim neuronskim mrežama
In this paper, a simulation study of the maximum power point tracking (MPPT) for a photovoltaic system using an artificial neural network is presented. Maximum power point tracking (MPPT) plays an important role in photovoltaic systems because it maximizes the power output from a PV solar system for all temperature and irradiation conditions, and therefore maximizes the power efficiency. Since the maximum power point (MPP) varies, based on the PV irradiation and temperature, appropriate algorithms must be utilized to track it in order maintain the optimal operation of the system. The software Matlab/Simulink is used to develop the model of PV solar system MPPT controller. The system simulation is elaborated by combining the models established of solar PV module and a DC/DC Boost converter. The system is studied using various irradiance shading conditions. Simulation results show that the photovoltaic simulation system tracks optimally the maximum power point even under severe disturbances conditions.
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机译:在本文中,使用人工神经网络对光伏系统的最大功率点跟踪(MPPT)进行了仿真研究。最大功率点跟踪(MPPT)在光伏系统中起着重要作用,因为它在所有温度和辐照条件下都能使PV太阳能系统的功率输出最大化,从而使功率效率最大化。由于最大功率点(MPP)会根据PV辐射和温度而变化,因此必须使用适当的算法对其进行跟踪,以保持系统的最佳运行。 Matlab / Simulink软件用于开发光伏太阳能系统MPPT控制器的模型。通过将太阳能光伏组件和DC / DC Boost转换器建立的模型相结合,详细阐述了系统仿真。使用各种辐照阴影条件研究了该系统。仿真结果表明,即使在严重干扰条件下,光伏仿真系统也可以最佳地跟踪最大功率点。
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