首页> 外文会议>2017 18th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering Book of Abstracts >Combination of artificial neural network and flower pollination algorithm to model fuzzy logic MPPT controller for photovoltaic systems
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Combination of artificial neural network and flower pollination algorithm to model fuzzy logic MPPT controller for photovoltaic systems

机译:人工神经网络与花粉授粉算法相结合的光伏系统模糊逻辑MPPT控制器建模

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摘要

In this paper, a new hybrid model is proposed to control the DC/DC converter of Grid Connected Photovoltaic System. The approach used to build the developed controller is divided into three steps, the first step is to generate a data based on the fuzzy logic controller (FLC), the second step is to choose an artificial neural network (ANN) structure to model the FLC and the last step is the training process of the ANN model which is performed by the flower pollination algorithm (FPA). This algorithm is based on the optimization of the root mean square error between the FLC and the ANN models. A validation test of the proposed controller was carried with various irradiations under any weather conditions.
机译:本文提出了一种新的混合模型来控制并网光伏系统的DC / DC转换器。用于构建开发的控制器的方法分为三个步骤,第一步是基于模糊逻辑控制器(FLC)生成数据,第二步是选择人工神经网络(ANN)结构来建模FLC最后一步是由花授粉算法(FPA)执行的ANN模型的训练过程。该算法基于FLC和ANN模型之间的均方根误差的优化。在任何天气条件下,使用各种辐照进行所提出控制器的验证测试。

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