基于样本协方差矩阵最大特征值的低信噪比环境电网异常状态检测
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摘要
为发展基于数据驱动的电网态势感知理论与方法,基于样本协方差矩阵的最大特征值(Maximum Eigenvalue of Sample Covariance Matrix, MESCM),提出了一种适用于低信噪比场景的电网异常状态检测方法。该方法源于随机矩阵理论,通过数据源矩阵的构造,窗口数据矩阵及其标准矩阵的构建,进而形成其样本协方差矩阵。通过该矩阵的最大特征值计算与越限判别,实现电网态势感知与预警。借助PSS/E软件,案例分析在一个IEEE 39节点系统及一个南方电网规划系统展开,涉及负荷异常跃变及三相短路接地故障。与传统平均谱半径分析法的计算结果比较表明该方法具有抗噪性能高,计算耗时少的优点,同时对于非完整性信息有一定的鲁棒性。
This paper proposes a novel method for abnormal state detection in low SNR environment by employing Maximum Eigenvalue of Sample Covariance Matrix(MESCM) for developing the theory and method of data-driven power grid situation awareness. Inspired by the random matrix theory, it firstly constructs a data source matrix, and obtains a moving window matrix and its standard matrix, then acquires the sample covariance matrix. In this way, the situation awareness and early warning for interconnected power systems could be achieved by MESCM calculation and its violation check. Utilizing PSS/E? software, the case studies have been carried on an IEEE 39-bus system and a planning system of China Southern Power Grid, involving two main working conditions such as abnormal load change and short circuit fault. The results show that the proposed methodology has the advantage of higher noise resistance and less computing time in comparison with the traditional mean spectral radius based method and preliminarily verifies that it would be robust under incomplete information.
This paper proposes a novel method for abnormal state detection in low SNR environment by employing Maximum Eigenvalue of Sample Covariance Matrix(MESCM) for developing the theory and method of data-driven power grid situation awareness. Inspired by the random matrix theory, it firstly constructs a data source matrix, and obtains a moving window matrix and its standard matrix, then acquires the sample covariance matrix. In this way, the situation awareness and early warning for interconnected power systems could be achieved by MESCM calculation and its violation check. Utilizing PSS/E? software, the case studies have been carried on an IEEE 39-bus system and a planning system of China Southern Power Grid, involving two main working conditions such as abnormal load change and short circuit fault. The results show that the proposed methodology has the advantage of higher noise resistance and less computing time in comparison with the traditional mean spectral radius based method and preliminarily verifies that it would be robust under incomplete information.
引文
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[22]徐心怡,贺兴,艾芊,等.基于随机矩阵理论的配电网运行状态相关性分析方法[J].电网技术,2016,40(3):781-790.XU Xinyi,HE Xing,AI Qian,et al.A correlation analysis method for operation status of distribution network based on random matrix theory[J].Power System Technology,2016,40(3):781-790.
[23]荣娜,李泽滔,韩松.改进的机电振荡模式相对局域性指标及其适应性[J].电力自动化设备,2017,37(2):140-144.RONG Na,LI Zetao,HAN Song.Improved relative-locality index of electromechanical oscillation mode and its adaptability[J].Electric Power Automation Equipment,2017,37(2):140-144.
[24]HAN S,XU Z,WU C.Mode shape estimation and mode checking for IAO using correlation analysis technique[J].Electric Power Systems Research,2011,81(6):1181-1187.
[2]赵晋泉,杨婷,吴凡,等.基于数据挖掘的输配网一体化事故响应决策[J].电力系统保护与控制,2018,46(5):1-8.ZHAO Jinquan,YANG Ting,WU Fan,et al.Data mining based accident response and decision-making for integrated transmission and distribution grid[J].Power System Protection and Control,2018,46(5):1-8.
[3]高志远,曹阳,徐昊亮,等.电力系统关键技术发展论刍议[J].电力系统保护与控制,2017,45(17):14-17.GAO Zhiyuan,CAO Yang,XU Haoliang,et al.Primary discussion on the key technologies development of power system[J].Power System Protection and Control,2017,45(17):14-17.
[4]马天男,王超,彭丽霖,等.多源异构大数据下综合能源系统用户用能行为预测分析研究[J].智慧电力,2018,46(10):86-95.MA Tiannan,WANG Chao,PENG Lilin,et al.Research on consumer energy use behavior forecasting and analysis of integrated energy system under multi-source heterogeneous data[J].Smart Power,2018,46(10):86-95.
[5]董飞飞,俞登科.我国电网技术的回顾与展望[J].智慧电力,2018,46(4):6-10.DONG Feifei,YU Dengke.Retrospect and prospect of china power grid technology[J].Smart Power,2018,46(4):6-10.
[6]李滨,覃芳璐,吴茵,等.基于模糊信息粒化与多策略灵敏度的短期日负荷曲线预测[J].电工技术学报,2017,32(9):149-159.LI Bin,QIN Fanglu,WU Yin,et al.Short-Term daily load curve forecasting based on fuzzy information granulation and multi-strategy sensitivity[J].Transactions of China Electrotechnical Society,2017,32(9):149-159.
[7]朱永利,李莉,宋亚奇,等.ODPS平台下的电力设备监测大数据存储与并行处理方法[J].电工技术学报,2017,32(9):199-210.ZHU Yongli,LI Li,SONG Yaqi,et al.Storage and parallel processing of big data of power equipment condition monitoring on ODPS platform[J].Transactions of China Electrotechnical Society,2017,32(9):199-210.
[8]HOU K,SHAO G,WANG H,et al.Research on practical power system stability analysis algorithm based on modified SVM[J].Protection and Control of Modern Power Systems,2018,3(3):119-125.DOI:10.1186/s41601-018-0086-0.
[9]薛禹胜,赖业宁.大能源思维与大数据思维的融合(二)应用及探索[J].电力系统自动化,2016,40(8):1-13.XUE Yusheng,LAI Yening.Integration of macro energy thinking and big data thinking part two applications and explorations[J].Automation of Electric Power Systems,2016,40(8):1-13.
[10]郝然,艾芊,肖斐.基于多元大数据平台的用电行为分析构架研究[J].电力自动化设备,2017,37(8):20-27.HAO Ran,AI Qian,XIAO Fei.Architecture based on multivariate big data platform for analyzing electricity consumption behavior[J].Electric Power Automation Equipment,2017,37(8):20-27.
[11]贺兴,艾芊,邱才明,等.随机矩阵理论在电力系统认知中的应用初探[J].电网技术,2017,41(4):1165-1173.HE Xing,AI Qian,QIU Caiming,et al.A primary study on the situation awareness of power systems using random matrix theory[J].Power System Technology,2017,41(4):1165-1173.
[12]XU X,HE X,AI Q,et al.A correlation analysis method for power systems based on random matrix theory[J].IEEE Transactions on Smart Grid,2017,8(4):1811-1820.
[13]刘威,张东霞,王新迎,等.基于随机矩阵理论的电力系统暂态稳定性分析[J].中国电机工程学报,2016,36(18):4854-4863.LIU Wei,ZHANG Dongxia,WANG Xinying,et al.Power System transient stability analysis based on random matrix theory[J].Proceedings of the CSEE,2016,36(18):4854-4863.
[14]吴茜,张东霞,刘道伟,等.基于随机矩阵理论的电网静态稳定态势评估方法[J].中国电机工程学报,2016,36(20):5414-5420.WU Qian,ZHANG Dongxia,LIU Daowei,et al.A method for power system steady stability situation assessment based on random matrix theory[J].Proceedings of the CSEE,2016,36(20):5414-5420.
[15]YAN Yingjie,SHENG Gehao,QIU Caiming,et al.Big data modeling and analysis for power transmission equipment:a novel random matrix theoretical approach[J].IEEE Access,2018,6:7148-7156.
[16]ZENG Y,KOH C L,LIANG Y C.Maximum eigenvalue detection:theory and application[C]//IEEE International Conference on Communications,May 19-23,2008,Beijing,China:4160-4164.
[17]KORTUN A,RATNARAJAH T,SELLATHURAI M,et al.On the eigenvalue-based spectrum sensing and secondary user throughput[J].IEEE Transactions on Vehicular Technology,2014,63(3):1480-1486.
[18]CARDOSO L S,DEBBAH M,BIANCHI P,et al.Cooperative spectrum sensing using random matrix theory[C]//International Symposium on Wireless Pervasive Computing,May 7-9,2008,Santorini,Greece:334-338.
[19]XIAO Yang.Security and privacy in smart grids[M].Boca Raton,USA:CRC Press,2017.
[20]QIU R C,ANTONIK P.Smart grid using big data analytics:a random matrix theory approach[M].Chichester,UK:John Wiley&Sons,2017.
[21]MARCHENKO V A,PASTUR L A.Distribution of eigenvalues for some sets of random matrices[J].Sbornik Mathematics,1967,1(1):507-536.
[22]徐心怡,贺兴,艾芊,等.基于随机矩阵理论的配电网运行状态相关性分析方法[J].电网技术,2016,40(3):781-790.XU Xinyi,HE Xing,AI Qian,et al.A correlation analysis method for operation status of distribution network based on random matrix theory[J].Power System Technology,2016,40(3):781-790.
[23]荣娜,李泽滔,韩松.改进的机电振荡模式相对局域性指标及其适应性[J].电力自动化设备,2017,37(2):140-144.RONG Na,LI Zetao,HAN Song.Improved relative-locality index of electromechanical oscillation mode and its adaptability[J].Electric Power Automation Equipment,2017,37(2):140-144.
[24]HAN S,XU Z,WU C.Mode shape estimation and mode checking for IAO using correlation analysis technique[J].Electric Power Systems Research,2011,81(6):1181-1187.