Detection method and characterization of ramp events of wind speed and wind power based on swinging door algorithm

doi:10.7523/j.ucas.2023.014

Abstract

Abstract: 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%.

Key words: ramping event, detection method, swinging door algorithm, wind farm

CLC Number:

P49
P412

LIANG Zhi, ZHANG Zhe, SHI Yu, LIU Lei. Detection method and characterization of ramp events of wind speed and wind power based on swinging door algorithm[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(6): 821-829.

References

[1] 王克, 刘芳名, 尹明健, 等. 1.5 ℃温升目标下中国碳排放路径研究[J]. 气候变化研究进展, 2021, 17(1): 7-17. DOI:10.12006/j.issn.1673-1719.2020.228.
[2] 张颖超, 宗阳, 邓华, 等. 基于趋势特征的风电功率爬坡事件检测方法[J]. 电测与仪表, 2020, 57(18): 122-127, 132. DOI: 10.19753/j.issn1001-1390.2020.18.020.
[3] Greaves B, Collins J, Parkes J, et al. Temporal forecast uncertainty for ramp events[J]. Wind Engineering, 2009, 33(4): 309-319. DOI:10.1260/030952409789685681.
[4] Cutler N, Kay M, Jacka K, et al. Detecting, categorizing and forecasting large ramps in wind farm power output using meteorological observations and WPPT[J]. Wind Energy, 2007, 10(5): 453-470. DOI:10.1002/we.235.
[5] 安磊, 王绵斌, 齐霞, 等. “风、光、火、蓄、储”多能源互补优化调度方法研究[J]. 可再生能源, 2018, 36(10): 1492-1498. DOI: 10.13941/j.cnki.21-1469/tk.2018.10.012.
[6] 唐一铭, 赵双芝, 郭昭艺, 等. 基于负荷曲线等效斜率提升光伏消纳能力的需求响应策略[J]. 可再生能源, 2020, 38(12): 1626-1632. DOI: 10.13941/j.cnki.21-1469/tk.2020.12.011.
[7] 于松涛, 王晓琨, 赵利强, 等. 基于容差动态调整的旋转门(SDT)改进算法[J]. 北京化工大学学报(自然科学版), 2013, 40(3): 109-113. DOI:10.13543/j.cnki.bhxbzr.2013.03.010.
[8] Han S, Liu X M, Chen J, et al. A real-time data compression algorithm for gear fault signals[J]. Measurement, 2016, 88: 165-175. DOI: 10.1016/j.measurement.2016.03.051.
[9] Hodge B. Value of improved wind power forecasting in the western interconnection[C]//EWEA Wind Power Forecasting Technology Workshop. December 3-4, 2013, Rotterdam, Netherlands.
[10] Kamath C. Understanding wind ramp events through analysis of historical data[C]//IEEE PES T&D. April 19-22, 2010, New Orleans, LA, USA. IEEE, 2010: 1-6. DOI:10.1109/TDC.2010.5484508.
[11] Cui Y, He Y J, Xiong X, et al. Algorithm for identifying wind power ramp events via novel improved dynamic swinging door[J]. Renewable Energy, 2021, 171: 542-556. DOI: 10.1016/j.renene.2021.02.123.
[12] 全利红, 胡非, 程雪玲. 用小波系数谱方法分析湍流湿度脉动的相干结构[J]. 大气科学, 2007, 31(1): 57-63. DOI: 10.3878/j.issn.1006-9895.2007.01.06.
[13] Hannesdóttir Á, Kelly M. Detection and characterization of extreme wind speed ramps[J]. Wind Energy Science, 2019, 4(3): 385-396. DOI: 10.5194/wes-4-385-2019.
[14] 唐振浩, 孟庆煜, 曹生现, 等. 基于小波深度置信网络的风电爬坡预测方法[J]. 太阳能学报, 2019, 40(11): 3213-3220. DOI: 10.19912/j.0254-0096.2019.11.026.
[15] Zucatelli P J, Nascimento E G S, Santos A Á B, et al. An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: a case study in Brazil and Uruguay[J]. Energy, 2021, 230: 120842. DOI:10.1016/j.energy.2021.120842.
[16] 黄麒元, 王致杰, 杜彬, 等. 基于前置分解组合预测方法的风电功率爬坡预测研究[J]. 可再生能源, 2016, 34(12): 1847-1852. DOI: 10.13941/j.cnki.21-1469/tk.2016.12.017.
[17] 景惠甜, 韩丽, 高志宇. 基于卷积神经网络特征提取的风电功率爬坡预测[J]. 电力系统自动化, 2021, 45(4): 98-105. DOI: 10.7500/AEPS20200227005.
[18] Ouyang T H, Zha X M, Qin L, et al. Prediction of wind power ramp events based on residual correction[J]. Renewable Energy, 2019, 136: 781-792. DOI:10.1016/j.renene.2019.01.049.
[19] Cui M J, Zhang J, Feng C, et al. Characterizing and analyzing ramping events in wind power, solar power, load, and netload[J]. Renewable Energy, 2017, 111: 227-244. DOI: 10.1016/j.renene.2017.04.005.