ANN-Based Energy Storage System for an EV Charging Station Using Quadratic Boost Converter

T. Muthamizhan, M. Janarthanan, P. Nalin, M. Nirmal
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Abstract

A solar PV, wind energy and battery energy storage system (BESS), connected to a dc bus by a quadratic boost converter (QBC), controlled by a closed loop PI and ANN Control is instigated in this work. The QBC for renewable energy sources (RES), energy storage elements and a DC Micro-Grid with resistive and dc motor loads with different control topologies are analysed. When compared to a PI controller, ANN confirms the power balance and grid stability even in worst environmental conditions and load variation, with respect to time. Open loop DC micro-grid system (DC-MGs) framework with disturbance, closed loop PI control and ANN based Data Management frameworks are formed and pretended using MATLAB/Simulink simulation software. Assessment of the time-domain parameters exhibit the accomplishment of DC-MGs framework control. The proposed framework has characteristics like minimal error towards the disturbance and have a quick response for the proposed system.
基于二次升压变换器的电动汽车充电站神经网络储能系统
本文设计了一种由二次升压变换器(QBC)连接到直流母线上,由闭环PI和人工神经网络控制的太阳能光伏、风能和电池储能系统(BESS)。分析了可再生能源(RES)、储能元件和具有不同控制拓扑的电阻和直流电机负载的直流微电网的QBC。与PI控制器相比,即使在最恶劣的环境条件和负载变化下,人工神经网络也能确定功率平衡和电网稳定性。采用MATLAB/Simulink仿真软件,形成了带扰动的开环直流微电网系统框架、闭环PI控制框架和基于神经网络的数据管理框架。时域参数的评估表明dc - mg框架控制的完成。所提出的框架具有对扰动误差最小、系统响应快等特点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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