基于相邻系数TQWT与改进TLS-ESPRIT算法的电力系统低频振荡模态辨识
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摘要
针对广域测量系统低频振荡过程中的高斯噪声干扰和定阶问题,提出了相邻系数的可调Q因子小波变换(TQWT)与改进TLS-ESPRIT算法相结合的方法对电力系统低频振荡信号进行模态辨识。该方法利用TQWT对含噪低频振荡信号进行分解,得到初始的小波系数,再利用相邻系数阈值规则对得到的小波系数进行处理,再对处理后的小波系数进行重构,达到去噪的目的;最后将去噪的信号运用改进总体最小二乘–旋转不变技术(TLS-ESPRIT)算法辨识,得到低频振荡模态参数。数值算例仿真、IEEE四机两区域仿真及北美电网实际案例仿真等结果表明,该方法能够准确辨识出低频振荡模态参数,相比于其他方法具有抗噪性能好、拟合精度高等优点。该辨识方法具有较强的实用性,能够较好地实现在线辨识,为下一步的低频振荡抑制研究奠定了基础。
Aiming at the problem of Gaussian noise interference and order in the process of low-frequency oscillation of wide area measurement system, we propose a new method based on adjacent coefficient tunable Q-factor wavelet transform(TQWT) and improved TLS-ESPRIT algorithm to identify the modes of low-frequency oscillation signal in power grid. In the proposed method, the TQWT is used to decompose the power signal to obtain the initial wavelet coefficients,and the adjacent coefficient threshold rule is used to deal with the wavelet coefficients, and reconstruct the processed wavelet coefficients using inverse TQWT; then an improved TLS-ESPRIT algorithm is utilized to identify the low-frequency oscillation modes parameters. The results of numerical simulations, the IEEE four-machine two-area simulations, and the actual case simulations of North American power grid show that the proposed method can accurately identify low-frequency oscillation modes parameters, and has better anti-noise performance and higher fitting accuracy than other methods.The proposed method has strong practicability, and can realize on-line identification better, which will lay the foundation for the further research of low-frequency oscillation suppression.
Aiming at the problem of Gaussian noise interference and order in the process of low-frequency oscillation of wide area measurement system, we propose a new method based on adjacent coefficient tunable Q-factor wavelet transform(TQWT) and improved TLS-ESPRIT algorithm to identify the modes of low-frequency oscillation signal in power grid. In the proposed method, the TQWT is used to decompose the power signal to obtain the initial wavelet coefficients,and the adjacent coefficient threshold rule is used to deal with the wavelet coefficients, and reconstruct the processed wavelet coefficients using inverse TQWT; then an improved TLS-ESPRIT algorithm is utilized to identify the low-frequency oscillation modes parameters. The results of numerical simulations, the IEEE four-machine two-area simulations, and the actual case simulations of North American power grid show that the proposed method can accurately identify low-frequency oscillation modes parameters, and has better anti-noise performance and higher fitting accuracy than other methods.The proposed method has strong practicability, and can realize on-line identification better, which will lay the foundation for the further research of low-frequency oscillation suppression.
引文
[1]郑超,李媛,吕盼,等.规模化光伏并网对暂态稳定影响及应对措施[J].高电压技术,2017,43(10):3403-3411.ZHENG Chao, LI Yuan, LüPan, et al. Influence of large-scaled pho-tovoltaicgridconnectedonthetransientstabilityandcounter-measures[J]. High Voltage Engineering, 2017, 43(10):3403-3411.
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[6]张宇辉,陈峰,李慧敏,等.基于小波变化和矩阵束算法的同步电机参数辨识[J].电力系统保护与控制,2012,40(9):87-92.ZHANGYuhui,CHENFeng,LIHuimin,etal.Basedonwavelettransform and matrix pencil ofalgorithm parameter identification ofsynchronousmotor[J].PowerSystemProtectionandControl,2012,40(9):87-92.
[7]刘森,赵书强,于赞梅,等.基于小波预处理的低频振荡Prony分析[J].电力自动化设备,2007,27(4):64-67.LIU Sen, ZHAO Shuqiang, YU Zanmei, et al. Prony analysis of lowfrequencyoscillationbasedonwaveletpretreatmenttechnology[J].Electric Power Automation Equipment, 2007, 27(4):64-67.
[8]弥潇,王杰,王芮.基于能量函数与不完全椭圆积分的多机系统低频振荡频率分析[J].高电压技术,2018,44(1):321-328.MI Xiao, WANG Jie, WANG Rui. Frequency analysis of low frequen-cy oscillation in multi-machine system based on energy function andincomplete elliptic integral[J]. High Voltage Engineering, 2018, 44(1):321-328.
[9]KAKIMOTO N, SUGUMI M, MAKINO T, et al. Monitoring of in-ter-areaoscillationmodebysynchronizedphasormeasurement[J].IEEE Transactions on Power System, 2006, 21(1):260-268.
[10]栾某德,刘涤尘,廖清芬,等.基于改进小波系数奇异值分解和小波去噪的低频振荡时变模式辨识[J].电网技术,2012,36(6):141-147.LUAN Moude, LIU Dichen, LIAO Qingfen, et al. A method to identi-fytime-varyingmodeoflowfrequencyoscillationbycontinuouswavelettransformbasedonraisingsingularvaluedecompositionofwavelet coefficient and wavelet denoising[J]. Power System Technol-ogy, 2012, 36(6):141-147.
[11]张程,金涛.基于ISPM和SDM-Prony算法的电力系统低频振荡模式辨识[J].电网技术,2016,40(4):1209-1216.ZHANG Cheng, JIN Tao. Identification of low frequency oscillationsin powersystemsusing an improved smoothness priorsmethod andsecond-derivative method-Prony[J]. Power System Technology, 2016,40(4):1209-1216.
[12]DARSHANA P W, UDAYA D A, KRISH N. Identification of domi-nantlow-frequencymodesinring-downoscillationsusingmultipleProny models[J]. IET Generation, Transmission&Distribution, 2015,9(15):2206-2214.
[13]唐西胜,孙玉树,齐智平.基于HHT的风电功率波动及其对电力系统低频振荡的影响分析[J].电网技术,2015,39(8):2115-2121.TANGXisheng,SUNYushu,QIZhiping.Analysisofwindpowerfluctuationcharacteristicsanditsimpactonpowersystemlowfre-quency oscillation based on HHT[J]. Power System Technology, 2015,39(8):2115-2121.
[14]马燕峰,赵书强.用改进的Hilbert-Huang变换辨识电力系统低频振荡[J].高电压技术,2012,38(6):1492-1499.MA Yanfeng, ZHAO Shuqiang. Identification of low frequency oscil-lationsinpowersystembasedonimprovedHilbert-HuangTransform[J]. High Voltage Engineering, 2012, 38(6):1492-1499.
[15]王祥超,张鹏,甄威,等.基于自然激励技术和TLS-ESPRIT方法的低频振荡模式辨识[J].电力系统自动化,2015,39(10):75-80.WANG Xiangchao, ZHANG Peng, ZHEN Wei, et al. Identification oflowfrequencyoscillationmodesbasedonNExTandTLS-ESPRITalgorithm[J].AutomationofElectricPowerSystems,2015,39(10):75-80.
[16]曾正,赵荣祥,杨欢.基于奇异熵TLS-ESPRIT算法的微电网小信号稳定性分析[J].电力自动化设备,2012,32(5):7-12.ZENG Zheng, ZHAO Rongxiang, YANG Huan. Small signal stabilityanalysisbasedonsingularentropyandTLS-ESPRITalgorithmformicrogrid[J].ElectricPowerAutomationEquipment,2012,32(5):7-12.
[17]SELESNICK I W. Wavelet transform with tunable Q-factor[J]. IEEETransactions on Signal Processing, 2011, 59(8):3560-3575.
[18]DAUBECHIES I, HEIL C. Ten lectures on wavelets[M].[S.l.]:Societyfor Industrial and Applied Mathematics, 1992.
[19]CAITT,SILVERMANBW.Incorporatinginformationonneigh-bouringcoefficientsintowaveletestimation[J].Sankhyā:theIndianJournal of Statistics, Series B, 2001, 63(2):127-148.
[20]郭成,李群湛.基于改进PSO算法的SSSC广域阻尼控制器设计[J].电工技术学报,2010,25(1):151-158.GUO Cheng, LI Qunzhan. SSSC wide-area damping controller designbasedonimprovedparticleswarmoptimizationI[J].TransactionsofChina Electrotechnical Society, 2010, 25(1):151-158.
[21]陈刚,段晓,张继红,等.基于ARMA模型的低频振荡模式在线辨识技术研究[J].电网技术,2010,34(11):48-54.CHEN Gang, DUAN Xiao, ZHANG Jihong, et al. A new approach foronline identification of low frequency oscillation modes based on au-to-regressivemoving-averagemode[J].PowerSystemTechnology,2010, 34(11):48-54.
[22]KAY B M. Modern spectral estimation[M]. Englewood Cliffs, USA:Prentice-Hall, 1988.
[2]董恒锋,徐政,黄弘扬,等.大电源特高压集中外送系统低频振荡的网侧控制技术[J].高电压技术,2016,42(2):571-580.DONGHengfeng,XUZheng,HUANGHongyang,etal.Gridsidedampingcontroltechnologiesoflowfrequencyoscillationofultrahighvoltagetransmissionsystemsforlarge-scaleenergybases[J].High Voltage Engineering, 2016, 42(2):571-580.
[3]宋墩文,杨学涛,丁巧林,等.大规模互联电网低频振荡分析与控制方法综述[J].电网技术,2011,35(10):22-27.SONGDunwen,YANGXuetao,DINGQiaolin,etal.Asurveyonanalysisonlowfrequencyoscillationinlarge-scaleinterconnectedpowergridanditscontrolmeasure[J].PowerSystemTechnology,2011, 35(10):22-27.
[4]李大虎,曹一家.基于模糊滤波和Prony算法的低频振荡模式在线辨识方法[J].电力系统自动化,2007,31(1):14-19.LI Dahu, CAO Yijia. Online low-frequency oscillation detection basedonfussyfilteringandPronyalgorithm[J].AutomationofElectricPower Systems, 2007, 31(1):14-19.
[5]侯王宾,刘天琪,李兴源.基于经验模态分解滤波的低频振荡Prony分析[J].物理学报,2010,59(5):3531-3537.HOU Wangbin, LIU Tianqi, LI Xingyuan. Prony analysis of low fre-quencyoscillationsbasedonempiricalmodedecompositionfiltering[J]. Acta Physica Sinaca, 2010, 59(5):3531-3537.
[6]张宇辉,陈峰,李慧敏,等.基于小波变化和矩阵束算法的同步电机参数辨识[J].电力系统保护与控制,2012,40(9):87-92.ZHANGYuhui,CHENFeng,LIHuimin,etal.Basedonwavelettransform and matrix pencil ofalgorithm parameter identification ofsynchronousmotor[J].PowerSystemProtectionandControl,2012,40(9):87-92.
[7]刘森,赵书强,于赞梅,等.基于小波预处理的低频振荡Prony分析[J].电力自动化设备,2007,27(4):64-67.LIU Sen, ZHAO Shuqiang, YU Zanmei, et al. Prony analysis of lowfrequencyoscillationbasedonwaveletpretreatmenttechnology[J].Electric Power Automation Equipment, 2007, 27(4):64-67.
[8]弥潇,王杰,王芮.基于能量函数与不完全椭圆积分的多机系统低频振荡频率分析[J].高电压技术,2018,44(1):321-328.MI Xiao, WANG Jie, WANG Rui. Frequency analysis of low frequen-cy oscillation in multi-machine system based on energy function andincomplete elliptic integral[J]. High Voltage Engineering, 2018, 44(1):321-328.
[9]KAKIMOTO N, SUGUMI M, MAKINO T, et al. Monitoring of in-ter-areaoscillationmodebysynchronizedphasormeasurement[J].IEEE Transactions on Power System, 2006, 21(1):260-268.
[10]栾某德,刘涤尘,廖清芬,等.基于改进小波系数奇异值分解和小波去噪的低频振荡时变模式辨识[J].电网技术,2012,36(6):141-147.LUAN Moude, LIU Dichen, LIAO Qingfen, et al. A method to identi-fytime-varyingmodeoflowfrequencyoscillationbycontinuouswavelettransformbasedonraisingsingularvaluedecompositionofwavelet coefficient and wavelet denoising[J]. Power System Technol-ogy, 2012, 36(6):141-147.
[11]张程,金涛.基于ISPM和SDM-Prony算法的电力系统低频振荡模式辨识[J].电网技术,2016,40(4):1209-1216.ZHANG Cheng, JIN Tao. Identification of low frequency oscillationsin powersystemsusing an improved smoothness priorsmethod andsecond-derivative method-Prony[J]. Power System Technology, 2016,40(4):1209-1216.
[12]DARSHANA P W, UDAYA D A, KRISH N. Identification of domi-nantlow-frequencymodesinring-downoscillationsusingmultipleProny models[J]. IET Generation, Transmission&Distribution, 2015,9(15):2206-2214.
[13]唐西胜,孙玉树,齐智平.基于HHT的风电功率波动及其对电力系统低频振荡的影响分析[J].电网技术,2015,39(8):2115-2121.TANGXisheng,SUNYushu,QIZhiping.Analysisofwindpowerfluctuationcharacteristicsanditsimpactonpowersystemlowfre-quency oscillation based on HHT[J]. Power System Technology, 2015,39(8):2115-2121.
[14]马燕峰,赵书强.用改进的Hilbert-Huang变换辨识电力系统低频振荡[J].高电压技术,2012,38(6):1492-1499.MA Yanfeng, ZHAO Shuqiang. Identification of low frequency oscil-lationsinpowersystembasedonimprovedHilbert-HuangTransform[J]. High Voltage Engineering, 2012, 38(6):1492-1499.
[15]王祥超,张鹏,甄威,等.基于自然激励技术和TLS-ESPRIT方法的低频振荡模式辨识[J].电力系统自动化,2015,39(10):75-80.WANG Xiangchao, ZHANG Peng, ZHEN Wei, et al. Identification oflowfrequencyoscillationmodesbasedonNExTandTLS-ESPRITalgorithm[J].AutomationofElectricPowerSystems,2015,39(10):75-80.
[16]曾正,赵荣祥,杨欢.基于奇异熵TLS-ESPRIT算法的微电网小信号稳定性分析[J].电力自动化设备,2012,32(5):7-12.ZENG Zheng, ZHAO Rongxiang, YANG Huan. Small signal stabilityanalysisbasedonsingularentropyandTLS-ESPRITalgorithmformicrogrid[J].ElectricPowerAutomationEquipment,2012,32(5):7-12.
[17]SELESNICK I W. Wavelet transform with tunable Q-factor[J]. IEEETransactions on Signal Processing, 2011, 59(8):3560-3575.
[18]DAUBECHIES I, HEIL C. Ten lectures on wavelets[M].[S.l.]:Societyfor Industrial and Applied Mathematics, 1992.
[19]CAITT,SILVERMANBW.Incorporatinginformationonneigh-bouringcoefficientsintowaveletestimation[J].Sankhyā:theIndianJournal of Statistics, Series B, 2001, 63(2):127-148.
[20]郭成,李群湛.基于改进PSO算法的SSSC广域阻尼控制器设计[J].电工技术学报,2010,25(1):151-158.GUO Cheng, LI Qunzhan. SSSC wide-area damping controller designbasedonimprovedparticleswarmoptimizationI[J].TransactionsofChina Electrotechnical Society, 2010, 25(1):151-158.
[21]陈刚,段晓,张继红,等.基于ARMA模型的低频振荡模式在线辨识技术研究[J].电网技术,2010,34(11):48-54.CHEN Gang, DUAN Xiao, ZHANG Jihong, et al. A new approach foronline identification of low frequency oscillation modes based on au-to-regressivemoving-averagemode[J].PowerSystemTechnology,2010, 34(11):48-54.
[22]KAY B M. Modern spectral estimation[M]. Englewood Cliffs, USA:Prentice-Hall, 1988.
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