为了增强风电机组偏航系统自适应水平,提升风能利用率,提出一种基于K近邻聚类（KNN）算法风电机组偏航控制参数优化方法。为准确描述风向变化,建立改进Weibull概率分布建立风向评估模型,即以风向波动的幅值（A）和波动持续时间（T）作为风况的数据标签来描述风向。对比风电机组不同偏航参数下的运行数据确定聚类中心（已知风况下的最佳偏航参数）,通过基于KNN算法的风电机组偏航控制参数优化模型,得到不同风况下风电机组最佳的偏航参数。通过对风电机组运行数据进行算例分析表明,该方法高风速时可提升风电机组发电效率,并在低风速时减少偏航启动次数。
Abstract
The frequency and range of wind direction change under different geographical conditions are different, and the adaptive level of wind turbine yaw control is poor, which significantly affects the power generation. In order to enhance the adaptive level of wind turbine yaw system and improve the utilization of wind energy, a wind turbine yaw control parameter optimization method based on K-nearest neighbor clustering （KNN） algorithm is proposed. In order to accurately describe wind direction changes, an improved Weibull probability distribution was established to establish a wind direction evaluation model. The amplitude of wind direction fluctuations （A） and the duration of fluctuations （T） are used as data labels for wind conditions to describe the wind direction. The operating data of wind turbines under different yaw parameters are compared to determine the clustering center （the best yaw parameter under known wind conditions）. The wind turbine yaw control parameters based on the KNN algorithm are optimized. Therefore, the best yaw parameters of wind turbines under different wind conditions are obtained. Case studies show that the proposed method can improve the power generation efficiency of wind turbines at high wind speeds and reduce the number of yaw starts at low wind speeds.
关键词
风电机组 /
KNN算法 /
Weibull分布 /
偏航参数优化
Key words
wind turbines /
k-nearest neighbors /
Weibull distribution /
yaw parameter optimization
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 薛禹胜, 雷兴, 薛峰, 等. 关于风电不确定性对电力系统影响的评述[J]. 中国电机工程学报, 2014, 34(29): 5029-5040.
XUE Y S, LEI X, XUE F, et al.Comment on the impact of wind power uncertainty on power systems[J]. Proceedings of the CSEE, 2014, 34(29): 5029-5040.
[2] LIU Y, WANG Y, WANG X, et al.Active power dispatch for supporting grid frequency regulation in wind farms considering fatigue load[J]. Energies, 2019, 12(8): 1508-1531.
[3] SONG D, FAN X, YANG J, et al.Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method[J]. Applied energy, 2018, 224(Aug. 15): 267-279.
[4] AITOR S G, ZULUETA E, UNAI F G,et al.Performance enhancement of the artificial neural network-based reinforcement learning for wind turbine yaw control[J]. Wind energy, 2020, 23(3): 676-690.
[5] SAENZ A A, ZULUEAT E, FERNANDEZ-GAMIZ U, et al.Artificial neural network based reinforcement learning for wind turbine yaw control[J]. Energies, 2019, 12(3): 436-453.
[6] EDUARDO C M, LUI S F, CERECERO N, et al.Mathematical modeling and fuzzy proportional-integral-derivative scheme to control the yaw motion of a wind turbine[J]. Wind energy, 2020, 24(4): 379-401.
[7] PEDERSEN T F, DEMURTAS G, SOMMER A, et al.Measurement of rotor centre flow direction and turbulence in wind farm environment[J]. Journal of physics: conference series, 2014(524): 413-424.
[8] 张伟, 雷阳, 张中泉. 风电机组偏航校正分析方法研究[J]. 华电技术, 2017, 39(4): 71-73, 80.
ZHANG W, LEI Y, ZHANG Z Q.Research on yaw correction analysis method of wind turbines[J]. Huadian technology, 2017, 39(4): 71-73, 80.
[9] 高峰, 凌新梅, 刘强. 基于SCADA数据的风电机组偏航控制参数优化[J]. 太阳能学报, 2019, 40(6): 1739-1746.
GAO F, LING X M, LIU Q.Optimization of wind turbine yaw control parameters based on SCADA data[J]. Acta energia solaris sinica, 2019, 40(6): 1739-1746.
[10] 王欣, 吴根勇, 潘东浩, 等. 基于运行数据的风电机组偏航优化控制方法研究[J]. 可再生能源, 2016, 34(3): 413-420.
WANG X, WU G Y, PAN D H, et al.Research on wind turbine yaw optimization control method based on operating data[J]. Renewable energy, 2016, 34(3): 413-420.
[11] 沈小军, 杜万里. 大型风力发电机偏航系统控制策略研究现状及展望[J]. 电工技术学报, 2015, 30(10): 196-203.
SHEN X, DU W L.Research status and prospects of yaw system control strategies for large wind turbines[J]. Proceedings of the CSEE, 2015, 30(10): 196-203.
[12] LEE K L, CHINNICI A, JAFARIAN M, et al.Experimental investigation of the effects of wind speed and yaw angle on heat losses from a heated cavity[J]. Solar energy, 2018, 165:178-188.
[13] 宋鹏, 柳玉, 郭鹏, 等. 风电机组风向波动概率特性研究[J]. 可再生能源, 2020, 38(4): 491-496.
SONG P, LIU Y, GUO P, et al.Research on the probability characteristics of wind direction fluctuation of wind turbines[J]. Renewable energy, 2020, 38(4): 491-496.
[14] GUGLIANI G K, SARKAR A, LEY C, et al.New methods to assess wind resources in terms of wind speed, load, power and direction[J]. Renewable energy, 2018, 129: 168-182.
[15] 齐晴, 沈正飞, 曹健, 等. 一种面向不确定标签样本的K-近邻高效决策算法[J]. 应用科学学报, 2020, 38(5): 659-671.
QI Q, SHEN Z F, CAO J, et al.An efficient K-nearest neighbor decision algorithm for uncertain label samples[J]. Journal of applied sciences, 2020, 38(5): 659-671.
基金
辽宁省“兴辽英才计划”（XLYC1802041）; 辽宁省中央引导地方科技发展资金计划（2021JH6/10500166）
PDF(1634 KB)
