Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network

被引：74

作者：

Ganjefar, Soheil ^{[1
]}

Ghassemi, Ali Akbar ^{[1
]}

Ahmadi, Mohamad Mehdi ^{[1
]}

机构：

[1] Bu Ali Sina Univ, Dept Elect Engn, Hamadan, Iran

来源：

ENERGY | 2014年 / 67卷

关键词：

Battery-charging windmill system; Maximum power point tracking; Tip-speed ratio; Optimum torque; Quantum neural network; Direct and indirect adaptive control; ENERGY; ALGORITHMS;

D O I：

10.1016/j.energy.2014.02.023

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

In this paper, a quantum neural network (QNN) is used as controller in the adaptive control structures to improve efficiency of the maximum power point tracking (MPPT) methods in the wind turbine system. For this purpose, direct and indirect adaptive control structures equipped with QNN are used in tip-speed ratio (TSR) and optimum torque (OT) MPPT methods. The proposed control schemes are evaluated through a battery-charging windmill system equipped with PMSG (permanent magnet synchronous generator) at a random wind speed to demonstrate transcendence of their effectiveness as compared to PID controller and conventional neural network controller (CNNC). (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：444 / 453

页数：10

共 28 条

[1] A review of maximum power point tracking algorithms for wind energy systems [J].