一种考虑风电场并网的电力系统在线同调识别策略
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摘要
现有的大电网同调机组分群策略,大都仅针对功角轨迹之间的距离进行研究,忽略了风电场并网对电力系统固有振荡模式的影响。针对上述存在的同调分群不准确问题,提出了一种两阶段高风电渗透率下大电网受扰机群同调分群策略。在第1阶段,通过修正系统的收缩导纳矩阵将风功率以电流的形式进行等值,并与其电气距离最近的同步机组进行联合分析,进而在不同潮流水平和典型工况下,离线计算其等效功角获得含风电场电力系统的改进发电机耦合程度拉普拉斯矩阵,求解其特征向量并得到离线的发电机耦合程度分类结果。在第2阶段,构建电力系统邻接图并将所得分类结果作为邻接图功角权值矩阵的约束,对高风电渗透率下大电网的改进功角拉普拉斯矩阵进行在线修正,通过特征映射算法提取其特征信息,进而通过修正的余弦相似度因子算法在线获得当前的同调分群结果。最后通过IEEE 39节点和118节点系统仿真,验证了所提策略的正确性和有效性。
Most of existing coherency identification strategies of generators in large power grid only focuse on the distances between power angle curves, ignoring the influence of wind power integration on the inherent oscillation mode of power system. Aiming at the problem of inaccurate coherency identification, a two-stage coherency identification strategy for large power grid under high wind power penetration is proposed. In the first stage, the wind power is equivalent with form of current by modifying the admittance matrix of the system. The wind generator and the synchronous generator nearest in electrical distance are analyzed together. Then at different power flow levels and under typical operation conditions, the equivalent power angle is calculated off-line so the Laplace matrix of the improved generator coupling degree in wind power system is obtained. The Laplace matrix is used to solve eigenvectors and get off-line classification results of generator coupling degree. In the second stage, an adjacency graph of power system is constructed. The obtained classification results are taken as the constraints of the power angle weight matrix of the adjacency graph for online modification of improved power angle Laplace matrix for large power grid under high wind power penetration. The feature information is extracted with feature mapping algorithm, and then the current coherency identification results are obtained with the modified cosine similarity factor algorithm. Finally,correctness and effectiveness of the proposed method are verified with simulation in IEEE 39-bus system and 118-bus system.
Most of existing coherency identification strategies of generators in large power grid only focuse on the distances between power angle curves, ignoring the influence of wind power integration on the inherent oscillation mode of power system. Aiming at the problem of inaccurate coherency identification, a two-stage coherency identification strategy for large power grid under high wind power penetration is proposed. In the first stage, the wind power is equivalent with form of current by modifying the admittance matrix of the system. The wind generator and the synchronous generator nearest in electrical distance are analyzed together. Then at different power flow levels and under typical operation conditions, the equivalent power angle is calculated off-line so the Laplace matrix of the improved generator coupling degree in wind power system is obtained. The Laplace matrix is used to solve eigenvectors and get off-line classification results of generator coupling degree. In the second stage, an adjacency graph of power system is constructed. The obtained classification results are taken as the constraints of the power angle weight matrix of the adjacency graph for online modification of improved power angle Laplace matrix for large power grid under high wind power penetration. The feature information is extracted with feature mapping algorithm, and then the current coherency identification results are obtained with the modified cosine similarity factor algorithm. Finally,correctness and effectiveness of the proposed method are verified with simulation in IEEE 39-bus system and 118-bus system.
引文
[1]王旭东.基于图论的智能电网最优孤岛划分模型和算法[D].天津:天津大学,2011.
[2]张祥宇.变速风电机组的虚拟惯性与系统阻尼控制研究[D].北京:华北电力大学,2013.
[3]杨明海.基于发电机同调分群的PMU优化配置方法[D].天津:天津大学,2006.
[4]王华芳.基于模糊聚类理论的电力系统同调机群识别研究[D].南京:河海大学,2005.
[5]夏成军,周良松,彭波,等.基于Epsilon分解的发电机同调识别新方法[J].电力系统自动化,2000,24(24):30-33.Xia Chengjun,Zhou Liangsong,Peng Bo,et al.A new method for coherent identification of generator based on Epsilon decomposition[J].Automation of Electric Power Systems,2000,24(24):30-33(in Chinese).
[6]倪敬敏,沈沉,李颖,等.主动解列控制中电网弱连接的一种在线识别方法[J].中国电机工程学报,2011,31(4):24-30.Ni Jingmin,Shen Chen,Li Ying,et al.An online identification method for actively disconnecting weak connections in power grids[J].Proceedings of the CSEE,2011,31(4):24-30(in Chinese).
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[9]倪敬敏,沈沉,陈乾.基于慢同调的自适应主动解列控制(一):理论基础探究[J].中国电机工程学报,2014,34(25):4374-4384.Ni Jingmin,Shen Chen,Chen Gan.Adaptive active de-column control based on slow coherence(I):theoretical basis research[J].Proceedings of the CSEE,2014,34(25):4374-4384(in Chinese).
[10]马振玢.低惯量电力系统的模式分析与同调识别研究[D].济南:山东大学,2017.
[11]胡世骏,罗萍萍,林济铿.计及断线的机组同调辨识方法[J].电力系统自动化,2018,42(2):64-69.Hu Shijun,Luo Pingping,Lin Jikeng.Method for coherent identification of units considering disconnection[J].Automation of Electric Power Systems,2018,42(2):64-69(in Chinese).
[12]胥威汀,刘俊勇,姚良忠,等.考虑节点电压可控性的聚类型电网解列分析[J].电力系统自动化,2013,37(21):72-79.Zhai Weiting,Liu Junyong,Yao Liangzhong,et al.De-column analysis of poly-type power grid considering node voltage controllability[J].Automation of Electric Power Systems,2013,37(21):72-79(in Chinese).
[13]Tu S T,Chen J Y,Yang W,et al.Laplacian eigenmaps-based polarimetric dimensionality reduction for sar image classification[J].IEEE Transactions on Geoscience&Remote Sensing,2011,50(1):170-179.
[14]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):2889-2893.Zhang Yazhou,Zhang Yanxia,Meng Gaopeng,et al.Clustering identification method for coherent clusters based on wide-area information[J].Power System Technology,2015,39(10):2889-2893(in Chinese).
[15]潘炜,刘文颖,杨以涵.采用受扰轨迹和独立分量分析技术识别同调机群的方法[J].中国电机工程学报,2008,28(25):86-92.Pan Wei,Liu Wenying,Yang Yihan.Method for identifying coherent groups using disturbed trajectories and independent component analysis techniques[J].Proceedings of the CSEE,2008,28(25):86-92(in Chinese).
[16]朱乔木,陈金富,段献忠,等.基于奇异值分解的同调机群识别方法[J].电工技术学报,2018,33(3):591-600.Zhu Qiaomu,Chen Jinfu,Duan Xianzhong,et al.Method of homology group identification based on singular value decomposition[J].Transactions of China Electrotechnical Society,2018,33(3):591-600(in Chinese).
[17]吴鹏.基于张量法的病态潮流计算及发电机组同调辨识[D].天津:天津大学,2012.
[18]陈跃燕.风光储协调控制策略研究[D].北京:华北电力大学,2014.
[19]卢卫星,倪以信,方明,等.计及复杂操作序列暂态稳定分析快速网络处理[J].清华大学学报(自然科学版),1996(5):29-35.Lu Weixing,Ni Yixin,Fang Ming,et al.Fast network processing considering transient stability analysis of complex operation sequences[J].Journal of Tsinghua University(Science and Technology),1996(5):29-35(in Chinese).
[20]汤蕾,沈沉,张雪敏.大规模风电集中接入对电力系统暂态功角稳定性的影响(一):理论基础[J].中国电机工程学报,2015,35(15):3832-3842.Tang Lei,Shen Shen,Zhang Xuemin.Influence of large-scale wind power centralized access on power system transient power angle stability(I):theoretical basis[J].Proceedings of the CSEE,2015,35(15):3832-3842(in Chinese).
[21]林俐,杨以涵.基于扩展等面积定则的含大规模风电场电力系统暂态稳定性分析[J].电力系统保护与控制,2012,40(12):105-110.Lin Wei,Yang Yihan.Transient stability analysis of power system with large scale wind farm based on extended equal area rule[J].Power System Protection and Control,2012,40(12):105-110(in Chinese).
[22]唐飞,贾骏,刘涤尘,等.一种考虑发电机同调分群的大电网快速主动解列策略[J].电工技术学报,2016,31(17):32-40.Tang Fei,Jia Jun,Liu Dichen,et al.A fast active disengagement strategy for large power grids considering generator coherence grouping[J].Transactions of China Electrotechnical Society,2016,31(17):32-40(in Chinese).
[23]Fan R K C.Spectral graph theory[M].American Mathematical Society,1997.
[24]汪芳宗.基于高度并行松弛牛顿方法的暂态稳定性实时分析计算的并行算法[J].中国电机工程学报,1999,19(11):18-21.Wang Fangzong.Parallel algorithm for real-time analysis and calculation of transient stability based on highly parallel relaxation Newton method[J].Proceedings of the CSEE,1999,19(11):18-21(in Chinese).
[25]宋洪磊,吴俊勇,郝亮亮,等.基于WAMS和改进拉普拉斯特征映射的同调机群在线识别[J].电网技术,2013,37(8):2157-2164.Song Honglei,Wu Junyong,Hao Liangliang,et al.On-line identification of homophone cluster based on WAMS and improved Laplacian feature mapping[J].Power System Technology,2013,37(8):2157-2164(in Chinese).
[26]马莉,杜小荣.基于监督学习的核拉普拉斯特征映射的FCM算法[J].工业仪表与自动化装置,2016(4):9-12.Ma Li,Du Xiaorong.FCM algorithm for nuclear Laplacian feature mapping based on supervised learning[J].Industrial Instrument&Automation,2016(4):9-12(in Chinese).
[27]耿志强,胡海霞,韩永明.基于余弦相似度的复杂网络故障检测方法及应用[J].北京化工大学学报(自然科学版),2017,44(2):87-94.Zhai Zhiqiang,Hu Haixia,Han Yongming.Complex network fault detection method based on cosine similarity and its application[J].Journal of Beijing University of Chemical Technology(Natural Science Edition),2017,44(2):87-94(in Chinese).
[28]安军,穆钢,徐炜彬.基于主成分分析法的电力系统同调机群识别[J].电网技术,2009,33(3):25-28.An Jun,Mu Gang,Xu Yubin.Identification of power system coherent group based on principal component analysis[J].Power System Technology,2009,33(3):25-28(in Chinese).
[2]张祥宇.变速风电机组的虚拟惯性与系统阻尼控制研究[D].北京:华北电力大学,2013.
[3]杨明海.基于发电机同调分群的PMU优化配置方法[D].天津:天津大学,2006.
[4]王华芳.基于模糊聚类理论的电力系统同调机群识别研究[D].南京:河海大学,2005.
[5]夏成军,周良松,彭波,等.基于Epsilon分解的发电机同调识别新方法[J].电力系统自动化,2000,24(24):30-33.Xia Chengjun,Zhou Liangsong,Peng Bo,et al.A new method for coherent identification of generator based on Epsilon decomposition[J].Automation of Electric Power Systems,2000,24(24):30-33(in Chinese).
[6]倪敬敏,沈沉,李颖,等.主动解列控制中电网弱连接的一种在线识别方法[J].中国电机工程学报,2011,31(4):24-30.Ni Jingmin,Shen Chen,Li Ying,et al.An online identification method for actively disconnecting weak connections in power grids[J].Proceedings of the CSEE,2011,31(4):24-30(in Chinese).
[7]Ding L,Ma Z,Wall P,et al.Graph spectra based controlled islanding for low inertia power systems[J].IEEE Transactions on Power Delivery,2017,32(1):302-309.
[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):70-76.Song Honglei,Wu Junyong,Qi Luyu.On-line homology identification of generator based on slow coherence theory and Hilbert-Huang transform[J].Electric Power Automation Equipment,2013,33(8):70-76(in Chinese).
[9]倪敬敏,沈沉,陈乾.基于慢同调的自适应主动解列控制(一):理论基础探究[J].中国电机工程学报,2014,34(25):4374-4384.Ni Jingmin,Shen Chen,Chen Gan.Adaptive active de-column control based on slow coherence(I):theoretical basis research[J].Proceedings of the CSEE,2014,34(25):4374-4384(in Chinese).
[10]马振玢.低惯量电力系统的模式分析与同调识别研究[D].济南:山东大学,2017.
[11]胡世骏,罗萍萍,林济铿.计及断线的机组同调辨识方法[J].电力系统自动化,2018,42(2):64-69.Hu Shijun,Luo Pingping,Lin Jikeng.Method for coherent identification of units considering disconnection[J].Automation of Electric Power Systems,2018,42(2):64-69(in Chinese).
[12]胥威汀,刘俊勇,姚良忠,等.考虑节点电压可控性的聚类型电网解列分析[J].电力系统自动化,2013,37(21):72-79.Zhai Weiting,Liu Junyong,Yao Liangzhong,et al.De-column analysis of poly-type power grid considering node voltage controllability[J].Automation of Electric Power Systems,2013,37(21):72-79(in Chinese).
[13]Tu S T,Chen J Y,Yang W,et al.Laplacian eigenmaps-based polarimetric dimensionality reduction for sar image classification[J].IEEE Transactions on Geoscience&Remote Sensing,2011,50(1):170-179.
[14]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):2889-2893.Zhang Yazhou,Zhang Yanxia,Meng Gaopeng,et al.Clustering identification method for coherent clusters based on wide-area information[J].Power System Technology,2015,39(10):2889-2893(in Chinese).
[15]潘炜,刘文颖,杨以涵.采用受扰轨迹和独立分量分析技术识别同调机群的方法[J].中国电机工程学报,2008,28(25):86-92.Pan Wei,Liu Wenying,Yang Yihan.Method for identifying coherent groups using disturbed trajectories and independent component analysis techniques[J].Proceedings of the CSEE,2008,28(25):86-92(in Chinese).
[16]朱乔木,陈金富,段献忠,等.基于奇异值分解的同调机群识别方法[J].电工技术学报,2018,33(3):591-600.Zhu Qiaomu,Chen Jinfu,Duan Xianzhong,et al.Method of homology group identification based on singular value decomposition[J].Transactions of China Electrotechnical Society,2018,33(3):591-600(in Chinese).
[17]吴鹏.基于张量法的病态潮流计算及发电机组同调辨识[D].天津:天津大学,2012.
[18]陈跃燕.风光储协调控制策略研究[D].北京:华北电力大学,2014.
[19]卢卫星,倪以信,方明,等.计及复杂操作序列暂态稳定分析快速网络处理[J].清华大学学报(自然科学版),1996(5):29-35.Lu Weixing,Ni Yixin,Fang Ming,et al.Fast network processing considering transient stability analysis of complex operation sequences[J].Journal of Tsinghua University(Science and Technology),1996(5):29-35(in Chinese).
[20]汤蕾,沈沉,张雪敏.大规模风电集中接入对电力系统暂态功角稳定性的影响(一):理论基础[J].中国电机工程学报,2015,35(15):3832-3842.Tang Lei,Shen Shen,Zhang Xuemin.Influence of large-scale wind power centralized access on power system transient power angle stability(I):theoretical basis[J].Proceedings of the CSEE,2015,35(15):3832-3842(in Chinese).
[21]林俐,杨以涵.基于扩展等面积定则的含大规模风电场电力系统暂态稳定性分析[J].电力系统保护与控制,2012,40(12):105-110.Lin Wei,Yang Yihan.Transient stability analysis of power system with large scale wind farm based on extended equal area rule[J].Power System Protection and Control,2012,40(12):105-110(in Chinese).
[22]唐飞,贾骏,刘涤尘,等.一种考虑发电机同调分群的大电网快速主动解列策略[J].电工技术学报,2016,31(17):32-40.Tang Fei,Jia Jun,Liu Dichen,et al.A fast active disengagement strategy for large power grids considering generator coherence grouping[J].Transactions of China Electrotechnical Society,2016,31(17):32-40(in Chinese).
[23]Fan R K C.Spectral graph theory[M].American Mathematical Society,1997.
[24]汪芳宗.基于高度并行松弛牛顿方法的暂态稳定性实时分析计算的并行算法[J].中国电机工程学报,1999,19(11):18-21.Wang Fangzong.Parallel algorithm for real-time analysis and calculation of transient stability based on highly parallel relaxation Newton method[J].Proceedings of the CSEE,1999,19(11):18-21(in Chinese).
[25]宋洪磊,吴俊勇,郝亮亮,等.基于WAMS和改进拉普拉斯特征映射的同调机群在线识别[J].电网技术,2013,37(8):2157-2164.Song Honglei,Wu Junyong,Hao Liangliang,et al.On-line identification of homophone cluster based on WAMS and improved Laplacian feature mapping[J].Power System Technology,2013,37(8):2157-2164(in Chinese).
[26]马莉,杜小荣.基于监督学习的核拉普拉斯特征映射的FCM算法[J].工业仪表与自动化装置,2016(4):9-12.Ma Li,Du Xiaorong.FCM algorithm for nuclear Laplacian feature mapping based on supervised learning[J].Industrial Instrument&Automation,2016(4):9-12(in Chinese).
[27]耿志强,胡海霞,韩永明.基于余弦相似度的复杂网络故障检测方法及应用[J].北京化工大学学报(自然科学版),2017,44(2):87-94.Zhai Zhiqiang,Hu Haixia,Han Yongming.Complex network fault detection method based on cosine similarity and its application[J].Journal of Beijing University of Chemical Technology(Natural Science Edition),2017,44(2):87-94(in Chinese).
[28]安军,穆钢,徐炜彬.基于主成分分析法的电力系统同调机群识别[J].电网技术,2009,33(3):25-28.An Jun,Mu Gang,Xu Yubin.Identification of power system coherent group based on principal component analysis[J].Power System Technology,2009,33(3):25-28(in Chinese).