卡尔曼滤波- kf在准牛顿- QN -光伏最大功率点检测算法中的应用研究

J. D. de Carvalho, L. Kretly
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引用次数: 0

摘要

本研究的目的是研究卡尔曼滤波在光伏分析中准牛顿(QN)算法参数估计中的应用,以改善最大功率点跟踪(MPPT)。最近在PV-MPPT中的研究[1]证明了双卡尔曼滤波器在PV-MPPT中的适用性。在这项工作中,我们实现了一个卡尔玛滤波器(KF)的仿真工作表,以识别QN算法的一些改进。QN算法是一种强大的凸曲线优化方法,当应用于温度和天气控制条件时,太阳能组件的功率曲线就是这种情况。但是,由于温度变化和阴影等随机过程,曲线会产生扭曲。目标是应用KF来估计由这些过程引起的噪声,以获得算法的快速收敛,并减轻最大功率点(MPP)周围的振荡。该策略背后的基本思想是通过MATLAB实现和模拟MPP周围的收敛时间和振荡来分析该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of Kalman Filter-KF-Application on a Quasi-Newtonian - QN - Algorithm for Photovoltaic Maximum Power Point Detection - MPPT
The objective of this work is to investigate the application of Kalman Filters to the estimation of parameters of a Quasi-Newtonian (QN) algorithm in the photovoltaic analysis to improve the Maximum Power Point Tracking (MPPT). Recent work in PV-MPPT [1], demonstrate the applicability of double Kalman Filter to PV-MPPT. In this work, we implement a simulation worksheet of a Kalmar Filter (KF) to identify some improvement on the QN algorithm. The QN algorithm is a powerful method of optimization of convex curves, which is the case of the power curve of a solar module when applied to controlled conditions of temperature and weather. But, there is a distortion of the curve that is made by random processes such temperature changes and shadowing. The objective is to apply the KF to estimate the noise provoked by those processes to obtain a rapid convergence of the algorithm and to mitigate the oscillation around the Maximum Power Point (MPP). The basic idea behind this strategy is to analyze the viability of this method by an implementation and simulation of the convergence time and the oscillation around the MPP using MATLAB.
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