Open AccessResearch on Yaw Error Detection of Wind Turbine Based on Particle Swarm Optimization
Author(s) -
Xinyue Zhang,
Chengdong Yang,
Xinrui Li,
Dawei Yan,
Hai Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012213
Subject(s) - turbine , wind power , particle swarm optimization , wind speed , wind direction , computer science , marine engineering , automotive engineering , engineering , meteorology , aerospace engineering , algorithm , physics , electrical engineering
Yaw system is an important part of the wind turbine, one of its main functions is to automatically face the wind when the wind turbine is in normal operation, to ensure that the engine room is facing the wind direction, so that the wind turbine can make efficient use of wind energy. However, the yaw system can not guarantee the accuracy at all times, which leads to the wind deviation. Too large deviation of wind energy not only reduces the efficiency of wind turbine using wind energy, but also leads to abnormal wear of equipment parts, which is not conducive to the normal operation of the unit. In this paper, a wind turbine deviation detection algorithm based on SCADA data is proposed. The detection algorithm mainly includes wind speed segmentation, data fitting, particle swarm optimization and other key technologies. After wind field verification, the algorithm proposed in this paper can effectively detect the wind deviation of wind turbines, and help to improve the efficiency of wind turbines.