A novel combined MPPT-pitch angle control for wide range variable speed wind turbine based on neural network

被引：97
作者：
Dahbi, Abdeldjalil [1 ,2 ]
Nait-Said, Nasreddine [2 ]
Nait-Said, Mohamed-Said [2 ]
机构：
[1] CDER, URERMS, Adrar 01000, Algeria
[2] Batna Univ, Dept Elect Engn, LSP IE, Batna 05000, Algeria
来源：
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY | 2016年 / 41卷 / 22期
关键词：
Permanent-magnet synchronous; generator (PMSG); Maximum power point tracking; (MPPT) control; Pitch control; Artificial neural network (ANN); Multi-layer perception (MLP); Wind energy conversion system; (WECS); DESIGN; SYSTEM;
D O I：
10.1016/j.ijhydene.2016.03.105
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
The objective of this paper is to develop a novel combined MPPT-pitch angle robust control system of a variable-speed wind turbine. The direct driven wind turbine using the permanent magnet synchronous generator (PMSG) is connected to the grid by means of fully controlled frequency converters, which consist of a pulse width-modulation PWM rectifier connected to an inverter via an intermediate DC bus. In order to maximize the exploited wind power and benefit from a wide range of the wind speed, a novel combined maximum power point tracking (MPPT)-Pitch angle control is developed using only one low cost circuit based on Neural Network (ANN), which allows the PMSG to operate at an optimal speed to extract maximum power when this last is lower than nominal power, and limit the extra power. To achieve feeding the grid with high-power and good quality of electrical energy, the inverter is controlled by (PWM) in a way to deliver only the active power into the grid, and thus to obtain a unit power factor. DC-link voltage is also controlled by the inverter. The dynamic and steady-state performances of the wind energy conversion system (WECS) are carried by using Matlab Simulink. (C) 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：9427 / 9442
页数：16
相关论文
共 36 条
[21]  
Hanzo L., 2011, POWER CONVERSION CON
[22]   Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors [J].
Hong, Chih-Ming ;
Chen, Chiung-Hsing ;
Tu, Chia-Sheng .
ENERGY CONVERSION AND MANAGEMENT, 2013, 69 :58-67
[23]   Optimal estimation and control of WECS via a Genetic Neuro Fuzzy Approach [J].
Kasiri, H. ;
Abadeh, M. Saniee ;
Momeni, H. R. .
ENERGY, 2012, 40 (01) :438-444
[24]   A novel adaptative maximum power point tracking algorithm for small wind turbines [J].
Kortabarria, Inigo ;
Andreu, Jon ;
Martinez de Alegria, Inigo ;
Jimenez, Jaime ;
Ignacio Garate, Jose ;
Robles, Eider .
RENEWABLE ENERGY, 2014, 63 :785-796
[25]   Hybrid intelligent control of PMSG wind generation system using pitch angle control with RBFN [J].
Lin, Whei-Min ;
Hong, Chih-Ming ;
Ou, Ting-Chia ;
Chiu, Tai-Ming .
ENERGY CONVERSION AND MANAGEMENT, 2011, 52 (02) :1244-1251
[26]   Architecture complexity and energy efficiency of small wind turbines [J].
Mirecki, Adam ;
Roboam, Xavier ;
Richardeau, Feredric .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2007, 54 (01) :660-670
[27]   A Single-Sensor-Based MPPT Controller for Wind-Driven Induction Generators Supplying DC Microgrid [J].
Nayanar, V. ;
Kumaresan, N. ;
Gounden, N. Ammasai .
IEEE TRANSACTIONS ON POWER ELECTRONICS, 2016, 31 (02) :1161-1172
[28]   Analysis of sensorless MPPT method for hybrid PV-Wind system using DFIG Wind Turbines [J].
Nguyen, Danvu ;
Fujita, Goro .
SUSTAINABLE ENERGY GRIDS & NETWORKS, 2016, 5 :50-57
[29]   Design and real time implementation of type-2 fuzzy vector control for DFIG based wind generators [J].
Raju, S. Krishnama ;
Pillai, G. N. .
RENEWABLE ENERGY, 2016, 88 :40-50
[30]   Dynamic model of wind energy conversion systems with PMSG-based variable-speed wind turbines for power system studies [J].
Sanchez, A. G. ;
Molina, M. G. ;
Rizzato Lede, A. M. .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2012, 37 (13) :10064-10069
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