基于改进参数辨识的三绕组变压器绕组状态在线监测方法
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摘要
电气参数辨识是实现电力变压器绕组状态监测的重要手段,为此,提出了一种基于短路电抗辨识的三绕组变压器绕组状态在线监测方法。针对参数辨识方程性态造成误差放大的问题,在探究变压器不同负荷分布与系数矩阵条件数之间关系的基础上,将条件数作为评估依据,提出了根据负荷分布情况调整待求参数数目的方法;进而依托大数据平台,通过合理筛选数据与各负荷状态下辨识方案的自适应匹配,建立了变压器绕组健康状态的在线监测方法;最后,通过数值仿真验证了条件数影响参数辨识精度的分析,并采用实际工程数据演示了在线监测的实现效果。结果表明:参数辨识方程系数矩阵的条件数对辨识结果的精度影响明显,条件数大小与辨识结果的误差呈正相关关系;同时,采用所提方法对实际工程数据的分析结果与变压器真实运行状况相符。该研究结果可以为三绕组变压器的参数辨识及绕组状态在线监测提供参考。
The electrical parameters identification is an important means to realize the winding condition monitoring of power transformers. Therefore, was proposed a method of on-line monitoring based on short-circuit reactance identification for three-winding transformer. In order to improve the accuracy of parameter identification, we investigated the relationship between different load distributions of the transformer and the condition number of the coefficient matrix.Using the condition number as a criterion, we adjusted the number of solution parameters according to different load states of the transformer. Furthermore, relying on the big data platform, we established an on-line monitoring method for transformer windings by reasonable screening data and identification scheme adaptive matching. Finally, numerical simulation was used to verify the influence of condition number on accuracy of parameter identification, while the engineering data were used to demonstrate the performance of on-line monitoring. The results show that the condition number of the coefficient matrix has an obvious influence on the accuracy of parameter identification, where the condition number is positively correlated with the error of the identification result. At the same time, the analysis of the actual engineering data by this method is consistent with the real state of the transformer. The results of this study can provide references for parameter identification and on-line status monitoring of three-winding transformer.
The electrical parameters identification is an important means to realize the winding condition monitoring of power transformers. Therefore, was proposed a method of on-line monitoring based on short-circuit reactance identification for three-winding transformer. In order to improve the accuracy of parameter identification, we investigated the relationship between different load distributions of the transformer and the condition number of the coefficient matrix.Using the condition number as a criterion, we adjusted the number of solution parameters according to different load states of the transformer. Furthermore, relying on the big data platform, we established an on-line monitoring method for transformer windings by reasonable screening data and identification scheme adaptive matching. Finally, numerical simulation was used to verify the influence of condition number on accuracy of parameter identification, while the engineering data were used to demonstrate the performance of on-line monitoring. The results show that the condition number of the coefficient matrix has an obvious influence on the accuracy of parameter identification, where the condition number is positively correlated with the error of the identification result. At the same time, the analysis of the actual engineering data by this method is consistent with the real state of the transformer. The results of this study can provide references for parameter identification and on-line status monitoring of three-winding transformer.
引文
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[8]姚陈果,赵仲勇,李成祥,等.基于暂态过电压特性的电力变压器绕组变形故障在线检测[J].高电压技术,2015,41(3):873-877.YAO Chenguo,ZHAO Zhongyong,LI Chengxiang,et al.Online detecting winding deformation of power transformer based on transientovervoltage[J].High Voltage Engineering,2015,41(3):873-877.
[9]邓祥力,熊小伏,高亮,等.基于参数辨识的变压器绕组变形在线监测方法[J].中国电机工程学报,2014,34(28):4950-4958.DENG Xiangli,XIONG Xiaofu,GAO Liang,et al.Method of on-line monitoring of transformer winding deformation based on parameter identification[J].Proceedings of the CSEE,2014,34(28):4950-4958.
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[11]邓祥力,高亮,刘建峰,等.特高压变压器模型漏感参数的稳态识别方法[J].电工技术学报,2014,29(5):254-256.DENG Xiangli,GAO Liang,LIU Jianfeng,et al.Steady-state parameter identification of UHV transformer model[J].Transactions of China Electrotechnical Society,2014,29(5):254-256.
[12]何桦,张瑜,宣丽娜.基于线性内点法及正交变换的抗差参数估计[J].电力系统自动化,2007,31(20):36-38.HE Hua,ZHANG Yu,XUAN Lina.WLAV robust parameter estimation based on linear interior point algorithm and orthogonal transformationl[J].Automation of Electric Power Systems,2007,31(20):36-38..
[13]何旭初.数值相关性理论及其应用[J].高等学校计算数学学报,1979(1):11-19.HE Xuchu.Theory of numerical linear dependence and its applications[J].Numerical Mathematics A Journal of Chinese Universities,1979(1):11-19.
[14]归庆明,郭建锋,边少锋.基于特征系统的病态性诊断[J].测绘科学,2002,27(2):13-15.GUI Qingming,GUO Jianfeng,BIAN Shaofeng.Ill-conditioning diagnostics based on eigensystem[J].Science of Surveying and Mapping,2002,27(2):13-15.
[15]IEEE standard test code for liquid-immersed distribution,power and regulating transformers:C57.12.90-2015[S],2015.
[16]OLIVEIRA L M R,CARDOSO A J M.Leakage inductances calculation for power transformers interturn fault studies[J].IEEETransactions on Power Delivery,2015,30(3):1213-1220.
[17]索南加乐,焦在滨,康小宁,等.Y/Δ接线变压器漏感参数的识别方法[J].中国电机工程学报,2008,28(13):84-88.SUONAN Jiale,JIAO Zaibin,KANG Xiaoning,et al.Algorithm to identify leakage inductances of power transformer with Y-delta connection[J].Proceedings of the CSEE,2008,28(13):84-88.
[18]李建生,梁军,张利,等.基于端口模型的电气设备状态变化检测[J].高电压技术,2014,40(2):540-547.LI Jiansheng,LIANG Jun,ZHANG Li,et al.Detection of change in equipment state by port model[J].High Voltage Engineering,2014,40(2):540-547.
[19]曾麟钧,林湘宁,黄景光,等.特高压自耦变压器的建模和电磁暂态仿真[J].中国电机工程学报,2010,30(1):91-97.ZENG Linjun,LIN Xiangning,HUANG Jingguang,et al.Modeling and electromagnetic transient simulation of UHV autotransformer[J].Proceedings of the CSEE,2010,30(1):91-97.
[20]江秀臣,盛戈皞.电力设备状态大数据分析的研究和应用[J].高电压技术,2018,44(4):1041-1050.JIANG Xiuchen,SHENG Gehao.Research and application of big data analysis of power equipment condition[J].High Voltage Engineering,2018,44(4):1041-1050.
[21]ALVAREZ-MARINO C,LEON F D,LOPEZ-FERNANDEZ X M.Equivalent circuit for the leakage inductance of multiwinding transformers:unification of terminal and duality models[J].IEEETransactions on Power Delivery,2012,27(1):353-361.
[2]BAGHERI M,NADERI M S,BLACKBURN T.Advanced transformer winding deformation diagnosis:moving from off-line to on-line[J].IEEE Transactions on Dielectrics and Electrical Insulation,2012,19(6):1860-1870.
[3]李刚,于长海,刘云鹏,等.电力变压器故障预测与健康管理:挑战与展望[J].电力系统自动化,2017,41(23):156-162.LI Gang,YU Changhai,LIU Yunpeng,et al.Challenges and prospects of fault prognostic and health management for power transformer[J].Automation of Electric Power Systems,2017,41(23):156-162.
[4]郑茂然,余江,陈宏山,等.基于大数据的输电线路故障预警模型设计[J].南方电网技术,2017,11(4):30-36.ZHENG Maoran,YU Jiang,CHEN Hongshan,et al.Design of fault warning model of transmission line based on big data[J].Southern Power System Technology,2017,11(4):30-36.
[5]李建生,梁军,贠志皓,等.基于电气信息评估设备状态渐进变化的概率分析方法[J].电工技术学报,2013,28(10):355-358.LI Jiansheng,LIANG Jun,YUN Zhihao,et al.Probability analysis of progressive changes of equipment state based on electrical information[J].Transactions of China Electrotechnical Society,2013,28(10):355-358.
[6]HONG K X,HUANG H,ZHOU J P.Winding condition assessment of power transformers based on vibration correlation[J].IEEE Transactions on Power Delivery,2015,30(4):1735-1742.
[7]张重远,王彦波,张林康,等.基于NICS脉冲信号注入法的变压器绕组变形在线监测装置研究[J].高电压技术,2015,41(7):2259-2266.ZHANG Zhongyuan,WANG Yanbo,ZHANG Linkang,et al.Research on an online monitoring device for transformer winding deformation using NICS pulse injection method[J].High Voltage Engineering,2015,41(7):2259-2266.
[8]姚陈果,赵仲勇,李成祥,等.基于暂态过电压特性的电力变压器绕组变形故障在线检测[J].高电压技术,2015,41(3):873-877.YAO Chenguo,ZHAO Zhongyong,LI Chengxiang,et al.Online detecting winding deformation of power transformer based on transientovervoltage[J].High Voltage Engineering,2015,41(3):873-877.
[9]邓祥力,熊小伏,高亮,等.基于参数辨识的变压器绕组变形在线监测方法[J].中国电机工程学报,2014,34(28):4950-4958.DENG Xiangli,XIONG Xiaofu,GAO Liang,et al.Method of on-line monitoring of transformer winding deformation based on parameter identification[J].Proceedings of the CSEE,2014,34(28):4950-4958.
[10]ABU-SIADA A,ISLAM S.A novel online technique to detect power transformer winding faults[J].IEEE Transactions on Power Delivery,2012,27(2):849-857.
[11]邓祥力,高亮,刘建峰,等.特高压变压器模型漏感参数的稳态识别方法[J].电工技术学报,2014,29(5):254-256.DENG Xiangli,GAO Liang,LIU Jianfeng,et al.Steady-state parameter identification of UHV transformer model[J].Transactions of China Electrotechnical Society,2014,29(5):254-256.
[12]何桦,张瑜,宣丽娜.基于线性内点法及正交变换的抗差参数估计[J].电力系统自动化,2007,31(20):36-38.HE Hua,ZHANG Yu,XUAN Lina.WLAV robust parameter estimation based on linear interior point algorithm and orthogonal transformationl[J].Automation of Electric Power Systems,2007,31(20):36-38..
[13]何旭初.数值相关性理论及其应用[J].高等学校计算数学学报,1979(1):11-19.HE Xuchu.Theory of numerical linear dependence and its applications[J].Numerical Mathematics A Journal of Chinese Universities,1979(1):11-19.
[14]归庆明,郭建锋,边少锋.基于特征系统的病态性诊断[J].测绘科学,2002,27(2):13-15.GUI Qingming,GUO Jianfeng,BIAN Shaofeng.Ill-conditioning diagnostics based on eigensystem[J].Science of Surveying and Mapping,2002,27(2):13-15.
[15]IEEE standard test code for liquid-immersed distribution,power and regulating transformers:C57.12.90-2015[S],2015.
[16]OLIVEIRA L M R,CARDOSO A J M.Leakage inductances calculation for power transformers interturn fault studies[J].IEEETransactions on Power Delivery,2015,30(3):1213-1220.
[17]索南加乐,焦在滨,康小宁,等.Y/Δ接线变压器漏感参数的识别方法[J].中国电机工程学报,2008,28(13):84-88.SUONAN Jiale,JIAO Zaibin,KANG Xiaoning,et al.Algorithm to identify leakage inductances of power transformer with Y-delta connection[J].Proceedings of the CSEE,2008,28(13):84-88.
[18]李建生,梁军,张利,等.基于端口模型的电气设备状态变化检测[J].高电压技术,2014,40(2):540-547.LI Jiansheng,LIANG Jun,ZHANG Li,et al.Detection of change in equipment state by port model[J].High Voltage Engineering,2014,40(2):540-547.
[19]曾麟钧,林湘宁,黄景光,等.特高压自耦变压器的建模和电磁暂态仿真[J].中国电机工程学报,2010,30(1):91-97.ZENG Linjun,LIN Xiangning,HUANG Jingguang,et al.Modeling and electromagnetic transient simulation of UHV autotransformer[J].Proceedings of the CSEE,2010,30(1):91-97.
[20]江秀臣,盛戈皞.电力设备状态大数据分析的研究和应用[J].高电压技术,2018,44(4):1041-1050.JIANG Xiuchen,SHENG Gehao.Research and application of big data analysis of power equipment condition[J].High Voltage Engineering,2018,44(4):1041-1050.
[21]ALVAREZ-MARINO C,LEON F D,LOPEZ-FERNANDEZ X M.Equivalent circuit for the leakage inductance of multiwinding transformers:unification of terminal and duality models[J].IEEETransactions on Power Delivery,2012,27(1):353-361.