基于NACEMD和改进非参数核密度估计的风功率波动性概率分布研究
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摘要
在大规模风电并网运行控制过程中,准确构建风电出力波动特性的概率分布模型具有重要意义。提出了一种结合复数据经验模态分解的噪声辅助信号分解和改进非参数核密度估计的风功率波动性概率建模方法。首先通过一种结合复数据经验模态分解的噪声辅助信号分解方法对风功率进行分解并提取波动量,然后结合非参数核密度估计法对其进行概率特性建模,并基于此模型进行自适应改进。最后,采用约束序优化算法对模型求解。仿真结果不仅验证了模型改进的有效性,还验证了建模的精确性和适用性。
For operation control process of large-scale wind farms, it is of great significance to accurately build probability distribution model of wind power fluctuation characteristics. According to the noise-assisted signal decomposition method based on complex empirical mode decomposition and adaptive nonparametric kernel density estimation method, a method of wind power fluctuation modeling is proposed. Firstly, the wind power is decomposed with the noise-assisted signal decomposition method based on complex empirical mode decomposition, and the fluctuation is extracted. Secondly, the probability characteristics are modeled based on upgraded nonparametric kernel density estimation and adaptively promoted based on the model. Finally, constrained ordinal optimization algorithm is utilized to solve the model. Accuracy and applicability of the modeling, and effectiveness of the model improvement are verified with simulation.
For operation control process of large-scale wind farms, it is of great significance to accurately build probability distribution model of wind power fluctuation characteristics. According to the noise-assisted signal decomposition method based on complex empirical mode decomposition and adaptive nonparametric kernel density estimation method, a method of wind power fluctuation modeling is proposed. Firstly, the wind power is decomposed with the noise-assisted signal decomposition method based on complex empirical mode decomposition, and the fluctuation is extracted. Secondly, the probability characteristics are modeled based on upgraded nonparametric kernel density estimation and adaptively promoted based on the model. Finally, constrained ordinal optimization algorithm is utilized to solve the model. Accuracy and applicability of the modeling, and effectiveness of the model improvement are verified with simulation.
引文
[1]王秀丽,李骏,黄镔,等.促进风电消纳的区省两级电力系统调度模型[J].电网技术,2015,39(7):1833-1838.Wang Xiuli,Li Jun,Huang Bin,et al.A two-stage optimal dispatching model for provincial and regional power grids connected with wind farms to promote accommodation of wind power[J].Power System Technology,2015,39(7):1833-1838(in Chinese).
[2]卓峻峰,金学洙,邓波,等.考虑断面安全约束的大规模风电有功控制[J].电网技术,2015,39(4):1014-1018.Zhuo Junfeng,Jin Xuezhu,Deng Bo,et al.An active power control method of large-scale wind farm considering security constraints of tie lines[J].Power System Technology,2015,39(4):1014-1018(in Chinese).
[3]Jin Y,Ju P,Rehtanz C,et al.Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm[J].Applied Energy,2018(222):485-496.
[4]Yan G,Wang Z,Li J,et al.Research on output power fluctuation characteristics of the clustering Photovoltaic-wind joint power generation system based on continuous output analysis[C]//International Conference on Power System Technology.Chengdu,China:IEEE PES,2014:2852-2857.
[5]Wang Yang.Analysis on fluctuation characteristics of wind power[J].Advanced Materials Research,2014(989-994):1256-1259.
[6]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+20.Lin Weixing,Wen Jing Yu,Ai Xiaomeng,et al.Probability density function of wind power variations[J].Proceedings of the CSEE,2012,32(1):38-46+20(in Chinese).
[7]万书亭,万杰.基于量化指标和概率密度分布的风电功率波动特性研究[J].太阳能学报,2015,36(2):362-368.Wan Shuting,Wan Jie.Research on wind power fluctuation characteristics based on quantitative index and probability density distribution[J].Acta Energiae Solaris Sinica,2015,36(2):362-368(in Chinese).
[8]崔杨,杨海威,李鸿博.基于高斯混合模型的风电场群功率波动概率密度分布函数研究[J].电网技术,2016,40(4):1107-1112.Cui Yang,Yang Haiwei,Li Hongbo.Probability density distribution function of wind power fluctuation of a wind farm group based on the gaussian mixture model[J].Power System Technology,2016,40(4):1107-1112(in Chinese).
[9]蔺红,孙立成,常喜强.新疆风电出力波动特性的概率建模[J].电网技术,2014,38(6):1616-1620.Lin Hong,Sun Licheng,Chang Xiqiang.A probabilistic model to simulate wind power output fluctuation of a certain wind farm cluster in Xinjiang region[J].Power System Technology,2014,38(6):1616-1620(in Chinese).
[10]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).
[11]周统汉,陈峦,李坚.基于有限混合Laplace模型的风功率波动特性研究[J].电网技术,2017,41(2):543-550.Zhou Tonghan,Chen Luan,Li Jian.Wind power fluctuation characteristic analysis based on finite Laplace mixture model[J].Power System Technology,2017,41(2):543-550(in Chinese).
[12]杨茂,马剑,李成凤,等.风电功率波动特性的混合Logistic分布模型[J].电网技术,2017,41(5):1376-1382.Yang Mao,Ma Jian,Li Chengfeng,et al.A mixed logistic distribution model for wind power fluctuation characteristics[J].Power System Technology,2017,41(5):1376-1382(in Chinese).
[13]杨楠,崔家展,周峥,等.基于模糊序优化的风功率概率模型非参数核密度估计方法[J].电网技术,2016,40(2):335-340.Yang Nan,Cui Jiazhan,Zhou Zheng,et al.Research on nonparametric kernel density estimation for modeling of wind power probability characteristics based on fuzzy ordinal optimization[J].Power System Technology,2016,40(2):335-340(in Chinese).
[14]徐青山,郑维高,卞海红,等.考虑游程检测法重构的EMD-Elman风电功率短时组合预测[J].太阳能学报,2015,36(12):2852-2859.Xu Qingshan,Zheng Weigao,Bian Haihong,et al.Short term forecast of wind power based on empirical mode decomposition and Elman neural network considering runs-test reconstruction[J].Acta Energiae Solaris Sinica,2015,36(12):2852-2859(in Chinese).
[15]Hu X,Peng S,Hwang W L.EMD revisited:a new understanding of the envelope and resolving the mode-mixing problem in AM-FMsignals[J].IEEE Transactions on Signal Processing,2012,60(3):1075-1086.
[16]朱宁辉,白晓民,董伟杰.基于EEMD的谐波检测方法[J].中国电机工程学报,2013,33(7):92-98.Zhu Ninghui,Bai Xiaomin,Dong Weijie.Harmonic detection method based on EEMD[J].Proceedings of the CSEE,2013,33(7):92-98(in Chinese).
[17]曲建岭,王小飞,高峰,等.基于复数据经验模态分解的噪声辅助信号分解方法[J].物理学报,2014,63(11):110201-9.Qu Jianling,Wang Xiaofei,Gao Feng,et al.Noise assisted signal decomposition method based on complex empirical mode decomposition[J].Acta Physica Sinica,2014,63(11):110201-9(in Chinese).
[18]杨楠,周峥,李臻,等.基于总体平均经验模态分解与改进Elman神经网络的风功率组合预测[J].电网与清洁能源,2015,31(10):112-117.Yang Nan,Zhou Zheng,Li Zhen,et al.Combined model based on EEMD and improved Elman neural network for short-term wind power prediction[J].Power System and Clean Energy,2015,31(10):112-117(in Chinese).
[19]Epanechnikov V A.Nonparametric estimation of a multidimensional probability density[J].Theory of Probability and Its Application,1969(14):156-161.
[20]Ho Y C,Sreenivas R S,Vakili P.Ordinal optimization of DEDS.Discrete event dynamic systems[J].Theory and Applications,1992,2(2):61-88.
[2]卓峻峰,金学洙,邓波,等.考虑断面安全约束的大规模风电有功控制[J].电网技术,2015,39(4):1014-1018.Zhuo Junfeng,Jin Xuezhu,Deng Bo,et al.An active power control method of large-scale wind farm considering security constraints of tie lines[J].Power System Technology,2015,39(4):1014-1018(in Chinese).
[3]Jin Y,Ju P,Rehtanz C,et al.Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm[J].Applied Energy,2018(222):485-496.
[4]Yan G,Wang Z,Li J,et al.Research on output power fluctuation characteristics of the clustering Photovoltaic-wind joint power generation system based on continuous output analysis[C]//International Conference on Power System Technology.Chengdu,China:IEEE PES,2014:2852-2857.
[5]Wang Yang.Analysis on fluctuation characteristics of wind power[J].Advanced Materials Research,2014(989-994):1256-1259.
[6]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+20.Lin Weixing,Wen Jing Yu,Ai Xiaomeng,et al.Probability density function of wind power variations[J].Proceedings of the CSEE,2012,32(1):38-46+20(in Chinese).
[7]万书亭,万杰.基于量化指标和概率密度分布的风电功率波动特性研究[J].太阳能学报,2015,36(2):362-368.Wan Shuting,Wan Jie.Research on wind power fluctuation characteristics based on quantitative index and probability density distribution[J].Acta Energiae Solaris Sinica,2015,36(2):362-368(in Chinese).
[8]崔杨,杨海威,李鸿博.基于高斯混合模型的风电场群功率波动概率密度分布函数研究[J].电网技术,2016,40(4):1107-1112.Cui Yang,Yang Haiwei,Li Hongbo.Probability density distribution function of wind power fluctuation of a wind farm group based on the gaussian mixture model[J].Power System Technology,2016,40(4):1107-1112(in Chinese).
[9]蔺红,孙立成,常喜强.新疆风电出力波动特性的概率建模[J].电网技术,2014,38(6):1616-1620.Lin Hong,Sun Licheng,Chang Xiqiang.A probabilistic model to simulate wind power output fluctuation of a certain wind farm cluster in Xinjiang region[J].Power System Technology,2014,38(6):1616-1620(in Chinese).
[10]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).
[11]周统汉,陈峦,李坚.基于有限混合Laplace模型的风功率波动特性研究[J].电网技术,2017,41(2):543-550.Zhou Tonghan,Chen Luan,Li Jian.Wind power fluctuation characteristic analysis based on finite Laplace mixture model[J].Power System Technology,2017,41(2):543-550(in Chinese).
[12]杨茂,马剑,李成凤,等.风电功率波动特性的混合Logistic分布模型[J].电网技术,2017,41(5):1376-1382.Yang Mao,Ma Jian,Li Chengfeng,et al.A mixed logistic distribution model for wind power fluctuation characteristics[J].Power System Technology,2017,41(5):1376-1382(in Chinese).
[13]杨楠,崔家展,周峥,等.基于模糊序优化的风功率概率模型非参数核密度估计方法[J].电网技术,2016,40(2):335-340.Yang Nan,Cui Jiazhan,Zhou Zheng,et al.Research on nonparametric kernel density estimation for modeling of wind power probability characteristics based on fuzzy ordinal optimization[J].Power System Technology,2016,40(2):335-340(in Chinese).
[14]徐青山,郑维高,卞海红,等.考虑游程检测法重构的EMD-Elman风电功率短时组合预测[J].太阳能学报,2015,36(12):2852-2859.Xu Qingshan,Zheng Weigao,Bian Haihong,et al.Short term forecast of wind power based on empirical mode decomposition and Elman neural network considering runs-test reconstruction[J].Acta Energiae Solaris Sinica,2015,36(12):2852-2859(in Chinese).
[15]Hu X,Peng S,Hwang W L.EMD revisited:a new understanding of the envelope and resolving the mode-mixing problem in AM-FMsignals[J].IEEE Transactions on Signal Processing,2012,60(3):1075-1086.
[16]朱宁辉,白晓民,董伟杰.基于EEMD的谐波检测方法[J].中国电机工程学报,2013,33(7):92-98.Zhu Ninghui,Bai Xiaomin,Dong Weijie.Harmonic detection method based on EEMD[J].Proceedings of the CSEE,2013,33(7):92-98(in Chinese).
[17]曲建岭,王小飞,高峰,等.基于复数据经验模态分解的噪声辅助信号分解方法[J].物理学报,2014,63(11):110201-9.Qu Jianling,Wang Xiaofei,Gao Feng,et al.Noise assisted signal decomposition method based on complex empirical mode decomposition[J].Acta Physica Sinica,2014,63(11):110201-9(in Chinese).
[18]杨楠,周峥,李臻,等.基于总体平均经验模态分解与改进Elman神经网络的风功率组合预测[J].电网与清洁能源,2015,31(10):112-117.Yang Nan,Zhou Zheng,Li Zhen,et al.Combined model based on EEMD and improved Elman neural network for short-term wind power prediction[J].Power System and Clean Energy,2015,31(10):112-117(in Chinese).
[19]Epanechnikov V A.Nonparametric estimation of a multidimensional probability density[J].Theory of Probability and Its Application,1969(14):156-161.
[20]Ho Y C,Sreenivas R S,Vakili P.Ordinal optimization of DEDS.Discrete event dynamic systems[J].Theory and Applications,1992,2(2):61-88.