Abstract

This article presents an improved maximum power tracking and power sharing technique by using a self-recurrent wavelet neural network (SRWNN) controller. A power factor correction (PFC) rectifier as machine-side converter and proportional resonant (PR) controlled three-phase inverter as grid-side converter for wind energy conversion system (WECS) is presented. The modified PFC rectifier ensures the machine to work at maximum power coefficient, by using a sensor less SRWNN controller. Hence, it extracts maximum power at various wind speeds and provides unity power factor operation at generator side so as to increase the efficiency. The PR controlled inverter ensures proper real and reactive power sharing with the grid, which also acts as a distribution static compensator (DSTATCOM), by casting off harmonics at the grid side for nonlinear loads. The wind turbine, generator, converter and controller are modeled in MATLAB/Simulink platform and performance is analyzed for maximum power tracking and power sharing for a 40kVA, 400 V WECS. An extensive analysis of the proposed system is carried out in a developed experimental setup and the results validate the theoretical claims.

摘要

本文提出了一种使用自递归小波神经网络（SRWNN）控制器的改进的最大功率跟踪和功率共享技术。提出了一种功率因数校正（PFC）整流器作为机器侧转换器，以及比例谐振（PR）控制的三相逆变器作为风能转换系统（WECS）的电网侧转换器。改进的PFC整流器通过使用无传感器的SRWNN控制器来确保机器以最大功率系数工作。因此，它可以在各种风速下提取最大功率，并在发电机侧提供统一的功率因数操作，从而提高效率。PR控制的逆变器通过消除电网侧的谐波以消除非线性负载，从而确保与电网进行适当的有功和无功功率共享，还可以充当配电网静态补偿器（DSTATCOM）。在MATLAB / Simulink平台中对风力涡轮机，发电机，转换器和控制器进行了建模，并对40kVA，400 V WECS的最大功率跟踪和功率共享进行了性能分析。在开发的实验装置中对提出的系统进行了广泛的分析，结果验证了理论要求。