一种基于广域测量信息的在线同调分群方法
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摘要
为了能够快速准确地识别同调机群,提出两种基于广域测量信息的同调分群指标。在此基础上,首先利用发电机间的功角差有效值指标快速地实现预分群;然后利用皮尔森相关系数和Hsim函数相结合构造新的相似度度量指标ρHsim,既考虑发电机功角曲线间的距离差异又考虑走势差异,实现了更严格的再分群。为了实现再分群的计算,采用属性阈值(QT)聚类和k-means聚类相结合的改进聚类算法。EPRI-36系统仿真计算的结果表明,在预分群阶段应用发电机功角差有效值指标可以迅速地对功角摆开差异较大的发电机进行分群,再分群阶段应用ρHsim相似度指标,可以实现各种精度的发电机同调分群。
In order to identify coherent generator groups quickly and accurately,two clustering indexes based on wide area measurement information are proposed. Firstly,the result of the pre-grouping could be attained by calculating the effective values of the power angle difference among all the generators.Then,a similarity measure method called ρHsim was constructed by combining the Pearson correlation coefficient and the Hsim function. It considered the distance difference and the trend difference of the generator power angle trajectories at the same time,which can be applied to attain the result of further-grouping. An improved clustering algorithm combining quality threshold( QT) clustering and k-means clustering was proposed to meet the requirements of online recognition of coherent generators. The effectiveness of the proposed method is verified by the EPRI-36 node system simulation example. Analytical results show that the generators with large power angle difference can be quickly assigned to different groups in the pre-grouping stage by using the effective values of the power angle difference. In the further-grouping stage,the result of coherency identification at different scales and levels of detail can be attained by using the new similarity measure index ρHsim.
In order to identify coherent generator groups quickly and accurately,two clustering indexes based on wide area measurement information are proposed. Firstly,the result of the pre-grouping could be attained by calculating the effective values of the power angle difference among all the generators.Then,a similarity measure method called ρHsim was constructed by combining the Pearson correlation coefficient and the Hsim function. It considered the distance difference and the trend difference of the generator power angle trajectories at the same time,which can be applied to attain the result of further-grouping. An improved clustering algorithm combining quality threshold( QT) clustering and k-means clustering was proposed to meet the requirements of online recognition of coherent generators. The effectiveness of the proposed method is verified by the EPRI-36 node system simulation example. Analytical results show that the generators with large power angle difference can be quickly assigned to different groups in the pre-grouping stage by using the effective values of the power angle difference. In the further-grouping stage,the result of coherency identification at different scales and levels of detail can be attained by using the new similarity measure index ρHsim.
引文
[1]倪敬敏,沈沉,谭伟,等.一种基于非平衡点处线性化的同调识别方法[J].电力系统自动化,2010,34(20):7.NI Jingmin,SHEN Chen,TAN Wei,et al. A coherence identifying method based on linearization at non-equilibrium point[J]. Automation of Electric Power Systems,2010,34(20):7.
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[8]宋洪磊,吴俊勇,冀鲁豫.基于慢同调理论和希尔伯特-黄变换的发电机在线同调识别[J].电力自动化设备,2013,33(8):70.SONG Honglei,WU Junyong,JI Luyu. Online identification of coherent generators based on slow coherency theory and HilbertHuang transform[J]. Electric Power Automation Equipment,2013,33(8):70.
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[12]宋方方,毕天姝,杨奇逊.基于WAMS的电力系统受扰轨迹预测[J].电力系统自动化,2006,30(23):27.SONG Fangfang,BI Tianshu,YANG Qixun. Perturbed trajectory prediction method based on wide area measurement systems[J].Automation of Electric Power Systems,2006,30(23):27.
[13]杨风召,朱扬勇.一种有效的量化交易数据相似性搜索方法[J].计算机研究与发展,2004,41(2):361.YANG Fengzhao,ZHU Yangyong. An efficient method for similarity search on quantitative transaction data[J]. Journal of Computer Research and Development,2004,41(2):361.
[14]宋智超,康健,孙广路,等.特征选择方法中三种度量的比较研究[J].哈尔滨理工大学学报,2018,23(1):111.SONG Zhichao,KANG Jian,SUN Guanglu,et al. The comparison of three measures in feature selection[J]. Journal of Harbin University of Science and Technology,2018,23(1):111.
[15]黄旭,吕强,钱培德.一种用于蛋白质结构聚类的聚类中心选择算法[J].自动化学报,2011,37(6):682.HUANG Xu,LQiang,QIAN Peide. An exemplar selection algorithm for protein structures clustering[J]. Acta Automatica Sinica,2011,37(6):682.
[16]王勇,唐靖,饶勤菲,等.高效率的K-means最佳聚类数确定算法[J].计算机应用,2014,34(5):1331.WANG Yong,TANG Jing,RAO Qinfei,et al. High efficient Kmeans algorithm for determining optimal number of clusters[J].Journal of Computer Applications,2014,34(5):1331.
[17]朱红霞,沈炯,李益国.基于满意模糊聚类的热工过程多模型建模方法[J].电机与控制学报,2016,20(10):94.ZHU Hongxia,SHEN Jiong,LI Yiguo. Satisfactory fuzzy clustering-based multi-model modeling method for thermal process[J].Electric Machines and Control,2016,20(10):94.
[18]牛培峰,刘超,李国强,等.基于双层聚类与GSA-LSSVM的汽轮机热耗率多模型预测[J].电机与控制学报,2016,20(3):90.NIU Peifeng,LIU Chao,LI Guoqiang,et al. Multi-model for turbine heat rate forecasting based on double layer clustering algorithm and GSA-LSSVM[J]. Electric Machines and Control,2016,20(3):90.
[19]李海燕.基于WAMS的电力系统暂态稳定预测方法研究[D].北京:华北电力大学,2006.
[20]冯康恒,张艳霞,刘志雄,等.基于广域信息的同调机群在线识别方法[J].电网技术,2014,38(8):2082.FENG Kangheng,ZHANG Yanxia,LIU Zhixiong,et al. A wide area information based online recognition of coherent generators in power system[J]. Power System Technology,2014,38(8):2082.
[2]谭伟,张雪敏,沈沉.新的同调识别方法及其在切机算法中的应用[J].西南交通大学学报,2009,44(4):507.TAN Wei,ZHANG Xuemin,SHEN Chen. New coherency identification approach and its application to generator tripping algorithm[J]. Journal of Southwest Jiaotong University,2009,44(4):507.
[3] AGHAMOHAMMADI M R,TABANDEH S M. A new approach for online coherency identification in power systems based on correlation characteristics of generators rotor oscillations[J]. International Journal of Electrical Power and Energy Systems,2016,83:470.
[4]文俊,刘天琪,李兴源,等.在线识别同调机群的优化支持向量机算法[J].中国电机工程学报,2008,28(25):80.WEN Jun,LIU Tianqi,LI Xingyuan,et al. Online identification of coherent generator using optimized LS-SVM[J]. Proceedings of the CSEE,2008,28(25):80.
[5]董超,廖清芬,唐飞,等.基于Teager能量算子的低频振荡节点同调分群[J].电网技术,2012,36(5):263.DONG Chao,LIAO Qingfen,TANG Fei,et al. Teager energy operator based coherent node grouping for power system low-frequency oscillation[J]. Power System Technology,2012,36(5):263.
[6]廖庭坚,刘光晔,雷强,等.计及电动机负荷的电力系统动态等值分析[J].电网技术,2016,40(5):1442.LIAO Tingjian,LIU Guangye,LEI Qiang,et al. Analysis of dynamic equivalence with consideration of motor loads in power systems[J]. Power System Technology,2016,40(5):1442.
[7]赵书强,常鲜戎,潘云江.电力系统同调机群识别的一种模糊聚类方法[J].电网技术,2001,25(4):10.ZHAO Shuqiang,CHANG Xianrong,PAN Yunjiang. A fuzzy clustering method for coherent generator groups recognition in power system[J]. Power System Technology,2001,25(4):10.
[8]宋洪磊,吴俊勇,冀鲁豫.基于慢同调理论和希尔伯特-黄变换的发电机在线同调识别[J].电力自动化设备,2013,33(8):70.SONG Honglei,WU Junyong,JI Luyu. Online identification of coherent generators based on slow coherency theory and HilbertHuang transform[J]. Electric Power Automation Equipment,2013,33(8):70.
[9]吴兴扬,卫志农,孙国强,等.基于非负矩阵分解的同调机群识别方法[J].电力系统自动化,2013,37(14):59.WU Xingyang,WEI Zhinong,SUN Guoqiang,et al. A method for identifying coherent generators based on non-negative matrix factorization[J]. Automation of Electic Power System,2013,37(14):59.
[10] AVDAKOVIC S,BECIROVIC E,NUHANOVIC A,et al. Generator coherency using the wavelet phase difference approach[J].IEEE Transactions on Power Systems,2014,29(1):271.
[11]张亚洲,张艳霞,蒙高鹏,等.基于广域信息的同调机群聚类识别方法[J].电网技术,2015,39(10):2889.ZHANG Yazhou,ZHANG Yanxia,MENG Gaopeng,et al. A wide area information based clustering recognition method of coherent generators[J]. Power System Technology,2015,39(10):2889.
[12]宋方方,毕天姝,杨奇逊.基于WAMS的电力系统受扰轨迹预测[J].电力系统自动化,2006,30(23):27.SONG Fangfang,BI Tianshu,YANG Qixun. Perturbed trajectory prediction method based on wide area measurement systems[J].Automation of Electric Power Systems,2006,30(23):27.
[13]杨风召,朱扬勇.一种有效的量化交易数据相似性搜索方法[J].计算机研究与发展,2004,41(2):361.YANG Fengzhao,ZHU Yangyong. An efficient method for similarity search on quantitative transaction data[J]. Journal of Computer Research and Development,2004,41(2):361.
[14]宋智超,康健,孙广路,等.特征选择方法中三种度量的比较研究[J].哈尔滨理工大学学报,2018,23(1):111.SONG Zhichao,KANG Jian,SUN Guanglu,et al. The comparison of three measures in feature selection[J]. Journal of Harbin University of Science and Technology,2018,23(1):111.
[15]黄旭,吕强,钱培德.一种用于蛋白质结构聚类的聚类中心选择算法[J].自动化学报,2011,37(6):682.HUANG Xu,LQiang,QIAN Peide. An exemplar selection algorithm for protein structures clustering[J]. Acta Automatica Sinica,2011,37(6):682.
[16]王勇,唐靖,饶勤菲,等.高效率的K-means最佳聚类数确定算法[J].计算机应用,2014,34(5):1331.WANG Yong,TANG Jing,RAO Qinfei,et al. High efficient Kmeans algorithm for determining optimal number of clusters[J].Journal of Computer Applications,2014,34(5):1331.
[17]朱红霞,沈炯,李益国.基于满意模糊聚类的热工过程多模型建模方法[J].电机与控制学报,2016,20(10):94.ZHU Hongxia,SHEN Jiong,LI Yiguo. Satisfactory fuzzy clustering-based multi-model modeling method for thermal process[J].Electric Machines and Control,2016,20(10):94.
[18]牛培峰,刘超,李国强,等.基于双层聚类与GSA-LSSVM的汽轮机热耗率多模型预测[J].电机与控制学报,2016,20(3):90.NIU Peifeng,LIU Chao,LI Guoqiang,et al. Multi-model for turbine heat rate forecasting based on double layer clustering algorithm and GSA-LSSVM[J]. Electric Machines and Control,2016,20(3):90.
[19]李海燕.基于WAMS的电力系统暂态稳定预测方法研究[D].北京:华北电力大学,2006.
[20]冯康恒,张艳霞,刘志雄,等.基于广域信息的同调机群在线识别方法[J].电网技术,2014,38(8):2082.FENG Kangheng,ZHANG Yanxia,LIU Zhixiong,et al. A wide area information based online recognition of coherent generators in power system[J]. Power System Technology,2014,38(8):2082.
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