基于多元线性回归的动态负荷模型参数实时选择

doi:10.3969/j.issn.1000-565X.2016.02.016

华南理工大学学报（自然科学版） ›› 2016, Vol. 44 ›› Issue (2): 107-116.doi: 10.3969/j.issn.1000-565X.2016.02.016

基于多元线性回归的动态负荷模型参数实时选择

黄玉龙¹ 刘明波² 陈迅³

1．暨南大学电气信息学院，广东珠海 519070; 2．华南理工大学电力学院，广东广州 510640; 3．广东电网公司电力科学研究院，广东广州 510600

收稿日期:2015-06-19 修回日期:2015-09-23 出版日期:2016-02-25 发布日期:2016-01-04
通信作者: 黄玉龙(1976-) ，男，博士，讲师，主要从事负荷建模，电力系统优化、运行与控制等的研究． E-mail:thuangyulong@jnu.edu.cn
作者简介:黄玉龙(1976-) ，男，博士，讲师，主要从事负荷建模，电力系统优化、运行与控制等的研究．
基金资助:
国家自然科学基金资助项目( 51377072)

Real-Time Selection of Dynamic Load Model Parameters Based on Multiple Linear Regression

HUANG Yu-long¹ LIU Ming-bo² CHEN Xun³

1.Electrical and Information College,Jinan University,Zhuhai 519070,Guangdong,China; 2.School of Electric Power,South China University of Technology,Guangzhou 510640,Guangdong,China; 3.Electric Power Research Institute of Guangdong Power Grid Corporation,Guangzhou 510600,Guangdong,China

Received:2015-06-19 Revised:2015-09-23 Online:2016-02-25 Published:2016-01-04
Contact: 黄玉龙(1976-) ，男，博士，讲师，主要从事负荷建模，电力系统优化、运行与控制等的研究． E-mail:thuangyulong@jnu.edu.cn
About author:黄玉龙(1976-) ，男，博士，讲师，主要从事负荷建模，电力系统优化、运行与控制等的研究．
Supported by:
Supported by the National Natural Science Foundation of China( 51377072)

摘要/Abstract

摘要： 在分析负荷模型参数影响因素的基础上，基于多元线性回归法提出一种动态负荷模型参数实时选择法．首先，对一段时间内负荷的全部历史扰动实测数据进行负荷模型参数辨识，积累成模型参数数据库，并按照电压振荡幅值将其分为小扰动、一般扰动和大扰动负荷模型参数数据库; 然后，根据仿真需要从相应类型的负荷模型参数数据库中用多元线性回归法搜索最匹配系统实时状况的负荷模型参数并分析其拟合精度; 最后，用某城市群两个变电站的实测数据验证了所提方法的有效性和准确性．

关键词: 动态负荷模型, 时变性, 多元线性回归, 电压振荡幅度, 扰动, 拟合精度

Abstract: This paper analyzes the factors influencing the load model parameters and proposes a real-time selection method of dynamic load model parameters based on the multiple linear regression.In the investigation,first,all historical load disturbance data measured in a certain duration are identified to obtain the load model parameters,and the identified parameters are used to construct a model parameter database that is further classified into three sub-databases respectively corresponding to small,common and large disturbances.Then,the parameters matching the real-time operation condition best are found from the sub-databases with correct disturbance type and the fitting accuracy is further analyzed.Finally,the effectiveness and accuracy of the proposed method are verified with the field measurement data collected from two substations in a metropolitan area in China.

Key words: dynamic load model, time-variability, multiple linear regression, voltage oscillation magnitude, disturbance, fitting accuracy

黄玉龙刘明波陈迅. 基于多元线性回归的动态负荷模型参数实时选择[J]. 华南理工大学学报（自然科学版）, 2016, 44(2): 107-116.

HUANG Yu-long LIU Ming-bo CHEN Xun. Real-Time Selection of Dynamic Load Model Parameters Based on Multiple Linear Regression[J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(2): 107-116.

参考文献

[1] IEEE Task Force on Load Representation for Dynamic Performance. Standard load models for power flow and dynamic performance simulation[J]. IEEE Trans. Power Syst., 1995, 10(3): 1302-1313. [2] Kundur P. Power System Stability and Control[M]. New York, McGraw-Hill, 1993. [3] Savulescu S. C., Pai M. A. Real-time stability in power systems： techniques for early detection of the risk of blackout[M]. New York, Springer Science+Business Media, 2005. [4] He R.-M., Ma J., David J. H. Composite load modeling via measurement approach[J]. IEEE Trans. Power Syst., 2006, 21(2): 663–672. [5] Bai H., Zhang P., Ajjarapu V. A Novel Parameter Identification Approach via Hybrid Learning for Aggregate Load Modeling[J]. IEEE Trans. Power Syst., 2009, 24(3): 1145-1154. [6] Ai Q., Gu D., Chen C. New load modeling approaches based on field tests for fast transient stability calculations[J]. IEEE Trans. Power Syst., 2006, 21(4): 1864–1873. [7] Shackshaft P. et al. General purpose model of power system loads[J]. Proc. Inst. Elect. Eng., 1977, 124(8): 715–723. [8] Liu Q.-S., Chen Y.-P., Duan D.-F. The load modeling and parameter identification for voltage stability analysis[C]// Proc. Int. Conf. Power System Technology, Oct. 13-17, 2002, Kunming, Peoples Republic China: 4p. [9] Wen J.-Y., Jiang L., Wu Q.-H., Cheng S. J. Power system load modeling by learning based on system measurements[J]. IEEE Trans. Power Del., 2003, 18(2): 364–371. [10] Ju P., Wu F., Shao Z.-Y., Zhang X.-P., et al. Composite load models based on field measurements and their applications in dynamic analysis[J]. IET Gen. Transm. Distrib., 2007, 1: 724–730. [11] 贺仁睦, 周文. 电力系统负荷模型的分类与综合[J]. 电力系统自动化, 1999, 23,(19): 12-16. He R.-M., Zhou W. The cluster and synthesis of electric power system load models[J]. Automation of Electric Power Systems, 1999, 23,(19): 12-16. [12] 李欣然, 陈元新, 苏盛. 感应电动机的参数加权平均综合建模[J]. 电力系统自动化, 2001, 25(15): 20-24. Li X.-R., Chen Y.-X., Su S. Parameters weighting-mean synthesis modeling for induction motor[J]. Automation of Electric Power Systems, 2001, 25(15): 20-24. [13] 鞠平, 金艳, 吴峰, 程颖. 综合负荷特性的分类综合方法及其应用[J]. 电力系统自动化, 2004, 28(1): 64-68. Ju P., Jin Y., Wu F., Cheng Y. Studies on classificationand synthesis of composite dynamic loads[J]. Automation of Electric Power Systems, 2004, 28(1): 64-68 . [14] 石景海, 贺仁睦. 基于量测的负荷建模—分类算法[J]. 中国电机工程学报, 2004, 24(2): 78-82. Shi J.-H., He R.-M. Measurement-based load modeling sorting algorithm[J]. Proceedings of the CSEE, 2004, 24(2): 78-82. [15] Lin S.-J., Li X.-R., Chen H.-H., et al. A New Classification and Synthesis Method for Load Dynamic Characteristics based on Field Measured Response[C]// Proc. Int. Conf. Power System Technology, Oct. 22-26, 2006, Chongqing, China: 7p. [16] 周开乐，杨善林. 基于改进模糊C均值算法的电力负荷特性分类[J], 电力系统保护与控制，2012,40(22): 58-63. Zhou K., Yang S. An improved fuzzy C-means algorithm for power load characteristics classification[J], Power System Protection and Control，2012,40(22): 58-63. [17] 白建勋，杨洪耕，吴传来，等. 基于自适应遗传算法的负荷特性分类[J], 电工电能新技术，2012,31(4): 92-96. Bai J., Yang H., Wu C., et al. Load characteristics classification based on adaptive genetic algorithm[J], Advanced Technology of Electrical Engineering and Energy，2012,31(4): 92-96. [18] 黄毅成，杨洪耕. 改进遗传K均值算法在负荷特性分类的应用[J], 电力系统及其自动化学报，2014,26(7): 70-75. Huang Y., Yang H. Application of improved genetic and k-means algorithm on load characteristics classification[J], Proceedings of the CSU-EPSA，2014,26(7): 70-75. [19] 郑晓雨，郑静媛，刘东，等. 负荷模型参数简化对负荷分类结果的影响[J]，中国电力，2013,46(11): 47-51. Zheng X., Zheng J., Liu D., et al. Impact of model parameter reduction on load classification[J]，Electric Power，2013,46(11): 47-51. [20] [x1]林济铿，刘露，张闻博，等. 基于随机模糊聚类的负荷建模与参数辨识[J]，电力系统自动化，2013,37(14): 50-58. Lin J., Liu L., Zhang W., et al. Load modeling and parameter identification based on random fuzziness clustering[J]. Automation of Electric Power Systems, 2013,37(14): 50-58. [21] Zheng J., Wang X., Zhu S. A novel real-time load modeling method for fast large-disturbance and short-term voltage stability analysis[J]. International Transactions on Electrical Energy Systems, 2013, 23: 1373–1395. [22] 张树卿，韩英铎，于松泰，等. 区域综合负荷动态等值参数在线测辨关键技术[J]，电力系统自动化，2012,36(19): 114-121. Zhang S., Han Y., Yu S., et al. Key techniques for composite load area integral equivalence and online parameter identification[J]. Automation of Electric Power Systems, 2012,36(19): 114-121. [23] Karlsson D., Hill D. J. Modeling and identification of nonlinear dynamic loads in power systems[J]. IEEE Trans. Power Syst., 1994, 9(1): 157–166. [24] 张旭，梁军，贠志皓，等. 考虑风电接入不确定性的广义负荷建模及应用[J]，电力系统自动化，2014,38(20): 61-67. Zhang X., Liang J., Yun Z., et al. Generalized load modeling and its application considering uncertainty of wind power integration[J]. Automation of Electric Power Systems, 2014,38(20): 61-67. [25] 鞠平, 马大强. 电力系统负荷建模[M]. 北京: 中国电力出版社, 2008. [26] 倪以信, 陈寿孙, 张宝霖. 动态电力系统分析[M]. 北京: 清华大学出版社, 2002. [27] 井艳清, 李兴源, 郭晓鸣, 等. 考虑感应电动机负荷模型的暂态电压稳定快速判据[J]. 电力系统自动化, 2011, 35(10): 104?107. Jing Y.-Q., Li X.-Y., Guo X.-M., et al. A quick criterion for transient voltage stability considering induction motor load models[J]. Automation of Electric Power Systems, 2011,35(5)：10-14. [28] 林舜江, 李欣然, 刘杨华, 等. 考虑负荷动态模型的暂态电压稳定快速判断方法[J]. 中国电机工程学报, 2009, 29（4）: 14-20. Lin S.-J., Li X.-R., Liu Y.-H., et al. Method for transient voltage stability quick judgment considering load dynamic model[J]. Proceedings of the CSEE, 2009, 29（4）: 14-20. [29] 周双喜, 朱凌志, 郭锡玖, 等. 电力系统电压稳定性及其控制[M]. 北京: 中国电力出版社, 2009. [30] 黄玉龙, 陈迅, 刘明波，等. 动态负荷模型参数辨识的微分进化算法[J]. 电工技术学报, 2013, 28(11), 270-277. Huang Y.-L., Chen X., Liu M.-B., et al. Differential evolution algorithm for dynamic load model parameter identification[J]. Transactions of China Electrotechnical Society, 2013, 28(11), 270-277. [31] Vapnik V. N. The Nature of Statistic Learning Theory[M]. New York: Springer-Verlag, 1995. [32] Radonja P., Stankovic S., Popovic Z. Specific process models derived from extremely small data sets and general process models[C]// Proc. 7th Seminar Neural Network Applications Electrical Engineering, Sep. 23-25 2004, Belgrade, Yugoslavia: 6p. [33] Soliman S.A., Persaud S., El-Nagar K., El-Hawary M.E. Application of least absolute value parameter estimation based on linear programming to short-term load forecasting[J]. Electr. Power Energy Syst. 1997, 19(3): 209-216. [34] 郑晓雨, 贺仁睦, 马进. 逐步多元回归法在负荷模型扩展中的应用[J]. 中国电机工程学报, 2011, 35(10): 104?107. Zheng X.-Y., He R.-M., Ma J. Application of stepwise multiple regression to load model extention[J]. Proceedings of the CSEE, 2011, 31(4): 72-77 . [35] Devore Jay L. Probability and statistics for engineering and the sciences[M]. Beijing: Higher Education Press, 2004. [36] 夏宁茂. 新编概率论与数理统计[M]．上海: 华东理工大学出版社, 2011. [37] 鞠平. 电力系统建模理论与方法[M]. 北京: 科学出版社, 2010. [38] 张小富，侯纲. 基于多元线性回归模型的西安住宅价格泡沫研究[J]. 价格月刊，2012,11(426)：41-44. Zhang XiaoFu, Hou Gang. Research on housing price bubble of Xi’an based on multiple linear regression model[J]. Prices Monthly，2012,11(426)：41-44. [39] 俞纯权. 回归估计样本容量的确定[J]. 数理统计与管理，1995,14(4)：33-36. Yu Chunquan. An approach for determining sample size in regression estimate[J]. Journal of Applied of Statistics and Management，1995,14(4)：33-36. [40] 江峰青, 郑淮, 李荣民. 上海电网负荷结构分析及特性预测[J]. 华东电力, 2008, 36(9): 51-52. Jiang F.-Q., Zheng H, Li R.-M. Structure analysis and characteristic forecasting for loads of Shanghai Power Grid[J]. East China Electric Power, 2008, 36(9): 51-52. [41] WECC load modeling task force [EB/OL]. 2014-07-31. http://www.wecc.biz/committees/StandingCommittees/PCC/TSS/MVWG/111306/Lists/Minutes/1/Workshop%20Load%20Modeling.pdf.

基于多元线性回归的动态负荷模型参数实时选择

Real-Time Selection of Dynamic Load Model Parameters Based on Multiple Linear Regression

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