Intelligent Mechanical Sensorless MPPT Control for Wind Energy Systems

被引:0
作者
Qiao, Wei [1 ]
机构
[1] Univ Nebraska, Dept Elect Engn, Lincoln, NE 68588 USA
来源
2012 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING | 2012年
关键词
Artificial neural network (ANN); intelligent control; maximum power point tracking (MPPT); sensorless control; wind energy system (WES);
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Wind energy systems (WESs) are usually equipped with mechanical sensors to measure wind speed, rotor shaft speed, and generator rotor position/speed for system monitoring, control, and protection. The use of these sensors increases cost, size, weight, and hardware wiring complexity and reduces reliability of WESs. The problems incurred in using mechanical sensors can be solved through mechanical sensorless control. This paper presents the principles of mechanical sensorless maximum power point tracking (MPPT) control for WESs based on an overview of existing work on the subject. Several intelligent mechanical sensorless control algorithms for WESs are presented, including: 1) a hill-climb search (HCS)-based wind speed sensor-less MPPT control algorithm, 2) various artificial neural network (ANN)-based wind speed sensorless MPPT control algorithms, and 3) an ANN-sliding mode observer (SMO)-based wind speed, generator rotor position and shaft speed sensorless MPPT control algorithm. The effectiveness of these intelligent mechanical sensorless MPPT control algorithms are demonstrated by computer simulations as well as experiments on practical WESs.
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页数:8
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