风速的爬坡事件是风速在短时间内大幅度增加或减少，风电场功率同时发生大幅度改变，影响到电网安全运行，甚至引发电网局部频率降低、电压崩溃等事故。选取风电场内风机和气象铁塔的同期数据，通过旋转门算法(SDA)识别爬坡事件，分析爬坡事件的持续时间、爬坡幅度和变化速率，讨论山地地形对爬坡事件的影响。基于SDA设计了风速和功率的爬坡事件识别算法，算法参数取值为时间阈值4 h、风速阈值6 m·s-1、功率阈值1 000 kW，算法的识别效果较好，可有效识别爬坡事件。针对不同风机的爬坡事件识别，建议采用额定风速与切入风速差值的2/3数值作为风速阈值参数，采用额定功率的2/3数值作为功率阈值参数。山地地形对爬坡事件的影响较为显著，爬坡事件与风机处海拔高度、平均风速较为相关，不同地形下风机的爬坡事件的时间比例分布在6.5%~9.8%，平均值为7.8%。
梁志
,
张哲
,
师宇
,
刘磊
. 基于旋转门算法的风速和风功率爬坡事件的识别方法及特征分析[J]. 中国科学院大学学报, 2024
, 41(6)
: 821
-829
.
DOI: 10.7523/j.ucas.2023.014
The ramp event of wind speed is a large increase or decrease in wind speed within a short period, causing a significant change in wind farm power, affecting the safe operation of the grid and even triggering accidents such as frequency reduction and voltage collapse. This paper selects the simultaneous data of wind turbines and meteorological towers in wind farms, identifies the ramp events by the swinging door algorithm (SDA), analyzes the duration, magnitude and change rate of the ramp events, and discusses the influence of mountainous terrain on them. In this paper, the recognition algorithm of the ramp event of wind speed and power is designed based on the SDA, and the algorithm parameters are set as follows: the time threshold 4 h, wind speed threshold 6 m·s-1, and power threshold 1 000 kW. For the recognition of ramp events in other wind turbines, this paper suggests using 2/3 value of the difference between rated wind speed and cut-in wind speed as the wind speed threshold parameter, and 2/3 value of rated power as the power threshold parameter. The terrain influence on the ramp event is significant, and the ramp event is more related to the altitude and average wind speed at the turbine, and the time proportion of the ramp event under different terrain ranges from 6.5% to 9.8%, with the average value of 7.8%.
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