基于Daubechies6离散小波的风电集群功率汇聚效应的时频特性分析
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摘要
风电输出功率具有波动性,随着风电集群规模的增大,风电输出功率的波动并不是成倍增加的,各机组出力之间存在一定程度上的平抑效果。该文针对风电集群功率的汇聚效应,提出面向电网运行的风电频段划分方法,建立时频特性分析指标,利用小波变换对汇聚效应的时频特性进行分析。算例针对特定风电集群输出功率各频段时频特性进行分析,并对不同汇聚规模下时频特性指标的变化趋势进行对比分析。研究结果表明,随着风电集群规模的增大,各频段时频特性指标趋于平缓,且汇聚效应存在某极限值。
The output of wind power is fluctuating. With the increase of clustering size for wind power, the fluctuation of wind power is not multiplied, and there is a certain degree of stabilizing effect between the output of each unit. In this paper, based on the clustering effect of wind power, the division of frequency band method of wind power for power grid was proposed, and the indexes of time-frequency characteristics for analyzing were established. The time-frequency characteristics for clustering effect of wind power were analyzed by wavelet transform. The example analyzed the time-frequency characteristics of output power for a wind power cluster in each frequency band. The trend for indexes of time-frequency characteristics under different clustering size was analyzed and compared. The results show that with the increase of the clustering size for wind power, the indexes of time-frequency characteristics in each frequency band tend to be gentle, and there is a limit to the clustering effect of wind power.
The output of wind power is fluctuating. With the increase of clustering size for wind power, the fluctuation of wind power is not multiplied, and there is a certain degree of stabilizing effect between the output of each unit. In this paper, based on the clustering effect of wind power, the division of frequency band method of wind power for power grid was proposed, and the indexes of time-frequency characteristics for analyzing were established. The time-frequency characteristics for clustering effect of wind power were analyzed by wavelet transform. The example analyzed the time-frequency characteristics of output power for a wind power cluster in each frequency band. The trend for indexes of time-frequency characteristics under different clustering size was analyzed and compared. The results show that with the increase of the clustering size for wind power, the indexes of time-frequency characteristics in each frequency band tend to be gentle, and there is a limit to the clustering effect of wind power.
引文
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[2]林卫星,文劲宇,艾小猛,等.风电功率波动特性的概率分布研究[J].中国电机工程学报,2012,32(1):38-46.Lin Weixing,Wen Jinyu,Ai Xiaomeng,et al.Probability density function of wind power variations[J].Proceedings of the CSEE,2012,32(1):38-46(in Chinese).
[3]杨茂,董骏城.基于混合分布模型的风电功率波动特性研究[J].中国电机工程学报,2016,36(S1):69-78.Yang Mao,Dong Juncheng.Study on characteristics of wind power fluctuation based on mixed distribution model[J].Proceedings of the CSEE,2016,36(S1):69-78(in Chinese).
[4]杨茂,杨春霖.基于模糊粒计算的风电功率实时预测研究[J].东北电力大学学报,2017,37(5):1-7.Yang Mao,Yang Chunlin.Research on wind power real-time forecasting based on fuzzy granular computing[J].Journal of Electric Power University,2017,37(5):1-7(in Chinese).
[5]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 Yusheng,Lei Xing,Xue Feng,et al.A review on impacts of wind power uncertainties on power systems[J].Proceedings of the CSEE,2014,34(29):5029-5040(in Chinese).
[6]杜文娟,卜思齐,王海风.考虑并网风电随机波动的电力系统小干扰概率稳定性分析[J].中国电机工程学报,2011,31(S1):7-11.Du Wenjuan,Bu Siqi,Wang Haifeng.Effect of stochastic variation of grid-connected wind generation on power system small-signal probabilistic stability[J].Proceedings of the CSEE,2011,31(S1):7-11(in Chinese).
[7]黄林宏,宋丽莉,周荣卫,等.大型风电基地风电波动特征分析[J].中国电机工程学报,2017,37(6):1599-1609.Huang Linhong,Song Lili,Zhou Rongwei,et al.Characteristics analysis of wind power fluctuations for large-scale wind farms[J].Proceedings of the CSEE,2017,37(6):1599-1609(in Chinese).
[8]孙玉树,唐西胜,孙晓哲,等.风电波动平抑的储能容量配置方法研究[J].中国电机工程学报,2017,37(S1):88-97.Sun Yushu,Tang Xisheng,Shun Xiaozhe,et al.Research on energy storage capacity allocation method for smoothing wind power fluctuations[J].Proceedings of the CSEE,2017,37(S1):88-97(in Chinese).
[9]叶林,李智,汤涌,等.基于随机预测控制理论和功率波动相关性的风电集群优化调度[J].中国电机工程学报,2018,38(11):3172-3183.Ye Lin,Li Zhi,Tang Yong,et al.Optimal dispatch of system integrated wind farm clusters based on stochastic model predictive control considering temporal correlation of wind power[J/OL].Proceedings of the CSEE,2018,38(11):3172-3183(in Chinese).
[10]Li Wei,Joós G,Abbey C.Wind power impact on system frequency deviation and an ESS based power filtering algorithm solution[C]//Proceedings of 2006 IEEE PESPower Systems Conference and Exposition.Atlanta,USA:IEEE,2006:2077-2084.
[11]Banarkar H,Luo Changling,Ooi B K.Power system response to wind power fluctuations[C]//Proceedings of2005/2006 IEEE/PES Transmission and Distribution Conference and Exposition.Dallas,USA:IEEE,2006:1445-1452.
[12]尹佳楠,葛延峰,高凯.风电场群出力的汇聚效应分析[J].电测与仪表,2015,52(5):104-108.Yin Jianan,Ge Yanfeng,Gao Kai.Analysis on clustering effect of wind generations[J].Electrical Measurement&Instrumentation,2015,52(5):104-108(in Chinese).
[13]穆钢,崔杨,严干贵.确定风电场群功率汇聚外送输电容量的静态综合优化方法[J].中国电机工程学报,2011,31(1):15-19.Mu Gang,Cui Yang,Yan Gangui.A static optimization method to determine integrated power transmission capacity of clustering wind farms[J].Proceedings of the CSEE,2011,31(1):15-19(in Chinese).
[14]申颖,赵千川,李明扬.多时空尺度下风电平滑效应的分析[J].电网技术,2015,39(2):400-405.Shen Ying,Zhao Qianchuan,Li Mingyang.Analysis on wind power smoothing effect in multiple temporal and spatial scales[J].Power System Technology,2015,39(2):400-405(in Chinese).
[15]杨树德,同向前.风电功率波动特性描述方法比较研究[C]//第三届全国电能质量学术会议论文集.镇江:中国电源学会,2013:305-315.Yang Shude,Tong Xiangqian.A comparative study on the description methods of wind power fluctuation[C]//Proceedings of the Third National Conference on Power Quality and the Power Quality Industry Development Forum.Zhenjiang:CPSS,2013:305-315(in Chinese).
[16]周文晖,李青,周兆经.采用小波多分辨率信号分解的电能质量检测[J].电工技术学报,2001,16(6):81-84.Zhou Wenhui,Li Qing,Zhou Zhaojing.Power quality detection using wavelet-multiresolution signal decomposition[J].Transactions of China Electrotechnical Society,2001,16(6):81-84(in Chinese).
[17]薛蕙,杨仁刚,罗红,等.利用小波变换分析配电网电能质量扰动[J].电网技术,2003,27(7):60-65.Xue Hui,Yang Rengang,Luo Hong,et al.Analysis of power quality disturbance in distribution network by wavelet transform[J].Power System Technology,2003,27(7):60-65(in Chinese).
[18]Miao Fufeng,Tang Xisheng,Qi Zhiping.Fluctuation feature extraction of wind power[C]//Proceedings of 2012IEEE Innovative Smart Grid Technologies-Asia(ISGTAsia).Tianjin,China:IEEE,2012:1-5.
[19]Chu Xiaodong,Zhang Wen,Nwachukwu T,et al.Characterization of daily wind farm power fluctuations using wavelet transform[C]//Proceedings of the Fourth International Conference on Natural Computation.Jinan,China:IEEE,2008:481-485.
[20]安周鹏,肖志怀,陈宇凡,等.小波包能量谱和功率谱分析在水电机组故障诊断中的应用[J].水力发电学报,2015,34(6):182-190.An Zhoupeng,Xiao Zhihuai,Chen Yufan,et al.Analysis of wavelet packet energy spectrum and power spectrum and its application to fault diagnosis of hydropower units[J].Journal of Hydroelectric Engineering,2015,34(6):182-190(in Chinese).