基于改进形态滤波与TLS-ESPRIT算法的电力系统低频振荡模态辨识
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摘要
针对广域测量低频振荡辨识过程中的噪声干扰和定阶问题,提出一种高精度低频振荡模态辨识方法。该方法基于粒子群优化算法(PSO-GA)设计广义形态滤波器的加权参数,改进后的滤波器可以较好去除噪声;将低频振荡信号通过该滤波器滤波后再使用改进总体最小二乘法-旋转不变技术(TLS-ESPRIT)算法进行模态辨识,可以准确获得各个模态参数。对于辨识算法的定阶问题,把奇异值差值与最大奇异值比值引入到TLS-ESPRIT算法中,采用该方式进行系统定阶,不仅计算量和受主观因素影响小,而且还可以提高辨识效率以及辨识的准确性。通过系统模型仿真以及电网实际案例证明提出的方法能够较快速准确地辨识低频振荡参数,且在抗噪性及辨识精度方面有较大的优势。
A high-accuracy and low-frequency oscillation mode identification method was presented to compensate the weakness of the existing means, especially for the noise jamming and order determination in low-frequency oscillation identification process of wide-area measurement. Weighted parameters of generalized morphological filter were designed via the method based on PSO-GA,which can effectively eliminate the noise. Besides, parameters of each mode can be obtained accurately by filtering low frequency oscillation signal by filters and using the improved TLS-ESPRIT algorithm for identification. For order determination of identification algorithm,not only are the calculated amount and influence of subjective factor small,but also identification efficiency and accuracy can be improved by introducing the specific value of singular value difference and maximum singular value to TLS-ESPRIT algorithm for order determination of system. System model simulation and practical grid case show that the proposed method can quickly and accurately identify low frequency oscillation parameters,and it has greater advantage in noise immunity and identification accuracy.
A high-accuracy and low-frequency oscillation mode identification method was presented to compensate the weakness of the existing means, especially for the noise jamming and order determination in low-frequency oscillation identification process of wide-area measurement. Weighted parameters of generalized morphological filter were designed via the method based on PSO-GA,which can effectively eliminate the noise. Besides, parameters of each mode can be obtained accurately by filtering low frequency oscillation signal by filters and using the improved TLS-ESPRIT algorithm for identification. For order determination of identification algorithm,not only are the calculated amount and influence of subjective factor small,but also identification efficiency and accuracy can be improved by introducing the specific value of singular value difference and maximum singular value to TLS-ESPRIT algorithm for order determination of system. System model simulation and practical grid case show that the proposed method can quickly and accurately identify low frequency oscillation parameters,and it has greater advantage in noise immunity and identification accuracy.
引文
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[17]董飞飞,刘涤尘,涂炼,等.基于MM-ARMA算法的次同步振荡模态参数辨识[J].高电压技术,2013,39(5):1252-1257.
[18]李天云,袁明哲,郑波,等.谐波和间谐波三参数识别的SSI-LS方法[J].电力系统保护与控制,2011,39(10):42-46.
[19]马燕峰,刘伟东,赵书强.基于包络线拟合的低频振荡性质在线判别[J].电力系统自动化,2014,38(23):46-53.
[20]易建波,黄琦,丁理杰,等.提升经验模态分解检测低频振荡模式精度的改进算法研究[J].电力系统保护与控制,2011,41(22):71-78.
[21]孙英云,游亚雄,侯建兰,等.基于差分正交匹配追踪和Prony算法的低频振荡模态辨识[J].电力系统自动化,2015,39(10):69-74.
[2]蔡国伟,杨德友,张俊丰,等.基于实测信号的电力系统低频振荡模态辨识[J].电网技术,2011,35(1):59-65.
[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.
[4]顾丽鸿,周孝信,严剑秋,等.特高压联网区域实时小干扰稳定分析策略[J].中国电机工程学报,2010,30(13):1-7.
[5]DARSHANA P W,UDAYA D A,KRISH N.Identification of dominant low-frequency modes in ring-down oscillations using multiple Prony models[J].IET Gener Transm Distrib,2015,9(15):2206-2214.
[6]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.
[7]SEYYED A H,NIMA A,MOHAMMAD H V.A fourier based wavelet approach using Heisenberg’s uncertainty principle and shannon'sentropy criterion to monitor power system small signal oscillations[J].IEEE Transactions on Power Systems,2015,30(6):3314-3325.
[8]KAKIMOTO N,SUGUMI M,MAKINO T,et al.Monitoring of inter-Area oscillation mode by synchronized phasor measurement[J].IEEE Transactions on Power System,2006,21(1):260-268.
[9]JONSSON M,BEGOVIC M,DAALDER J.A new method suitable for real-time generator coherency determination[J].IEEE Transactions on Power Systems,2004,19(3):1473-1482.
[10]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.
[11]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.
[12]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.
[13]RUEDA J L,JUAREZ C A,ERLICH I.Wavelet-based analysis of power system low-frequency electromechanical oscillations[J].IEEE Transactions on Power Systems,2011,26(3):1733-1743.
[14]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.
[15]董超,刘涤尘,廖清芬,等.基于数学形态学滤波技术和TLS-ESPRIT算法的低频振荡模式辨识研究[J].电力系统保护与控制,2012,40(3):114-118.
[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.
[17]董飞飞,刘涤尘,涂炼,等.基于MM-ARMA算法的次同步振荡模态参数辨识[J].高电压技术,2013,39(5):1252-1257.
[18]李天云,袁明哲,郑波,等.谐波和间谐波三参数识别的SSI-LS方法[J].电力系统保护与控制,2011,39(10):42-46.
[19]马燕峰,刘伟东,赵书强.基于包络线拟合的低频振荡性质在线判别[J].电力系统自动化,2014,38(23):46-53.
[20]易建波,黄琦,丁理杰,等.提升经验模态分解检测低频振荡模式精度的改进算法研究[J].电力系统保护与控制,2011,41(22):71-78.
[21]孙英云,游亚雄,侯建兰,等.基于差分正交匹配追踪和Prony算法的低频振荡模态辨识[J].电力系统自动化,2015,39(10):69-74.