Wind turbine blades icing detection with multi-parameter models based on AdaBoost algorithm

doi:10.3969/j.issn.1674-1951.2021.08.003

Abstract

Abstract:

Blade ice accretion is a factor that affects the safe operation of wind turbines in high-altitude and high-humidity areas of China in winter. Early detection of ice accretion and making adjustment on operation mode timely can guarantee the safety of wind turbines. The effects of ice accretion on the operation performance and parameters were analyzed thoroughly, and power, rotor blade speed and ambient temperature were taken as variables to monitor blade icing. Models of power and rotor blade speed were constructed by AdaBoost algorithm, and the prediction residuals of the two models were made by exponentially weighted moving average （EWMA）, in order to detect the abnormalities of power and rotor speed. If both abnormal power and rotor speed are detected and ambient temperature drops below 0 ℃ simultaneously, the blade icing alarm will be triggered. The effectiveness of the method has been proved by the icing data of a wind farm in Kunming.

Key words: renewable energy, wind turbine, blade icing, multi-parameter model, AdaBoost algorithm, EWMA

CLC Number:

TM315:TK83

FAN Daqian, LIU Bosong, GUO Peng. Wind turbine blades icing detection with multi-parameter models based on AdaBoost algorithm[J]. Huadian Technology, 2021, 43(8): 20-26.

Figures/Tables 7

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变量	单位	下限	上限
风速	m/s	3	25
功率	MW	0	2
转速	r/min	8	16
偏航误差	(°)	-20	20
桨距角	(°)	0	30
发电机转矩	kN·m	0	20

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