[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. |