电力系统低频振荡在线分析关键技术研究
|
摘要
随着国民经济的持续发展,我国电能消耗量持续增长,电网规模不断扩大,电力系统稳定性问题更加复杂,分析和控制难度不断增加。其中,低频振荡问题在互联电力系统中屡有发生,对电力系统运行稳定性造成严重影响,已成为目前威胁互联电网安全和制约电网传输能力的关键问题。
传统的低频振荡分析方法,基于电力系统结构模型,以小干扰稳定性理论分析振荡模式,或者以计及非线性高阶项交互影响的正规型方法和模态级数法分析振荡模式。这些方法的分析精度受制于模型阶数、参数准确性、未建模动态以及模式交互因素的影响,已无法满足现代大规模互联电力系统稳定性分析的要求。目前,随着WAMS (Wide-Area Measurement System, WAMS)系统在电网中的应用,基于测量信号的低频振荡分析方法成为研究重点。
针对实测低频振荡信号,受扰轨迹真实反映了系统的动态过程,并且完全包含激振模式的特征信息。但是低频振荡信号是一种典型的非平稳信号,存在振幅随时间变化以及激振模式出现随机性等非平稳信号特点,传统低频振荡信号分析方法基于平稳信号假设模型,对低频振荡模式的非平稳特性参数辨识存在一定的局限性。基于此,本文研究HHT (Hilbert Huang Transform, HHT)方法分析低频振荡问题,围绕该方法应用中的主要理论和关键技术问题开展以下几方面的研究：
1.研究基于测量信号的低频振荡分析方法。针对低频振荡实测信号的特点,对比研究改进Prony方法和HHT方法分析低频振荡信号的理论模型和算法过程,测试分析了Prony方法存在模型定阶、拟合精度以及非平稳参数辨识局限性问题。着重研究了HHT方法分析振荡信号及辨识振荡模式特征参数的方法,讨论振荡模式辨识的物理意义,测试验证了该方法的模式辨识结果具有较高的有效性和精度。
2.讨论影响HHT方法分析低频振荡信号有效性和准确性的主要因素,研究提高HHT方法性能的关键技术和改进方法。针对现有HHT方法存在的端点效应、拟合误差、模式混叠和阻尼损失等问题,通过低通数字滤波技术、基于AR模型的端点延拓技术、B样条插值技术、筛分控制因子和复小波分析等改进技术,实现了一种改进的低频振荡信号分析算法,极大地提高了振荡模式特征参数辨识的精度。
3.针对WAMS提供的实测信号,研究基于HHT方法的在线低频振荡模式分析技术。特别针对EMD (Empirical Mode Decomposition, EMD)过程的模式混叠和低频振荡信号的非平稳参数辨识问题,提出改进频率外差方法和自适应滑动窗口技术,解决了EMD分解中的间歇性和倍频模式混叠问题,同时确定出不同振荡模式的存在时间,并且实现了1s时间进制的近实时低频振荡信号在线分析算法。该算法能够在线自适应分析出系统低频振荡情况,辨识低频振荡模式参数准确度高、实时性强。
4.讨论了基于模型的小干扰稳定性理论在判定振荡模式相关性方面的局限性。研究基于实测信号分析振荡模式相关性,从能量转换的角度建立一种基于HHT方法的贡献因子,量化定义了扰动与低频振荡模式和发电机状态变量之间的相关性关系。设计了基于HHT方法的贡献因子在线算法,通过实例验证了该方法分析与弱阻尼振荡模式和功率迫振模式相关的振荡源定位有效性,进一步完善了HHT方法分析低频振荡问题的理论体系,拓展了该方法的应用范围。
With the development of our national economy, energy consumption and grid scale continue to grow. This makes the stability problems in power system become more complex and its analysis and control are getting harder. In fact, the low frequency oscillation in power system which seriously affects the stable operation of the power system has repeatedly occurred and become one of the key issues which threaten the safety and stability of interconnected power grid and restrict the power transmission capacity.
Traditional methods are based on the power system structure model, such as applying the small signal stability theory to analyze low frequency oscillation modes or Normal Form and modal series method with the non-linear higher order terms considered to analyze low frequency oscillation modes. The analysis accuracy of these methods is subject to the order of the model, the parameters accuracy, un-modeled dynamic and interactive modal factors, so that traditional low frequency oscillation analysis methods have been unable to meet the requirements of stability analysis of modern power system. Nowadays, with the extensive application of WAMS (Wide-Area Measurement System) in the wide area power grid, the analysis of low frequency oscillation of power system based on the measurement signals becomes a hot research topic.
With measured low frequency oscillation signals, the disturbed trajectories reflect the true dynamic process of the system and contain the complete characteristic information of the excitation modes. However, the low frequency oscillation signal is a typical non-stationary signal, which demonstrates non-stationary signal characteristics such as the amplitude changing over time and the random emergence of excitation modes. Traditional measurement signal based analysis methods assume the signal is stable, which limits its use to analyze non-stationary characteristics of the low frequency oscillation modes. Therefore, the data-driven HHT (Hilbert Huang Transform) method is proposed to analyze the low frequency oscillation. In this paper, the main theories and key technical issues of HHT method are studied as following:
1. Low frequency oscillation analysis based on the measured signals. Comparative study is performed on theoretical models and algorithms process of the improved Prony method and the HHT method analysis of low frequency oscillation signal. The limitation of Prony analysis to analyze low frequency oscillation signal is tested and discussed, such as model order, fitting precision and non-stationary parameters identification. Using the HHT method to deal with non-stationary oscillation signal and identify oscillation mode characteristic parameters is studied in detail, including discussing the physical meaning of the method to identify the low frequency oscillation mode and analyzing the results validity and accuracy of low-frequency oscillation mode parameters identification.
2. Analyze the main factors affecting the validity and accuracy of the low frequency oscillation signal based on HHT method, then some key technologies and improved methods to improve the performance of HHT method are studied in detail. For the problem of end effect, fitting error, mode mixing and damping loss existed in HHT method, the low-pass digital filtering technology, the endpoint extension technology based on AR model, the B-spline interpolation technologies, the sifting control factors and complex wavelet analysis method are proposed to achieve an improved EMD (Empirical Mode Decomposition) process, which greatly improves the accuracy of low frequency oscillation mode characteristic parameters identification.
3. Especially in terms of real-time measurement oscillation signals provided by WAMS, the online low frequency oscillation analysis techniques based on HHT is studied in detail. As the EMD process has mode mixing and non-stationary parameter identification problems in low frequency oscillation signal, improved frequency heterodyne method and adaptive sliding window technologies are proposed to deal with the intermittent and octave mode mixing problem within EMD decomposition process. The proposed method is able to determine the presence time of different low frequency oscillation modes and implement the Is hexadecimal near-real-time analysis and identification algorithm of low frequency oscillation signal. The algorithm can adaptively implement the online analysis of measured signal of low frequency oscillation, which has high accuracy and instantaneity to identify the low frequency oscillation mode parameters.
4. The limitations of small signal stability analysis theory to analyze the low frequency oscillation modes correlation with the structure-based model is discussed in detail. The contribution factors, considered from the perspective of energy conversion are established based on the HHT method and measured oscillation signals, which can define the correlation between the disturbance and the low frequency oscillation mode quantitatively. Furthermore, an online algorithm to compute contribution factors based on the HHT method is proposed to solve the correlation problem between the source generators and the oscillation mode when weakly damped oscillation mode and forced oscillation mode appear. The proposed method can further consolidate the theoretical base of low frequency oscillation analysis based on the HHT method and expand its applications.
传统的低频振荡分析方法,基于电力系统结构模型,以小干扰稳定性理论分析振荡模式,或者以计及非线性高阶项交互影响的正规型方法和模态级数法分析振荡模式。这些方法的分析精度受制于模型阶数、参数准确性、未建模动态以及模式交互因素的影响,已无法满足现代大规模互联电力系统稳定性分析的要求。目前,随着WAMS (Wide-Area Measurement System, WAMS)系统在电网中的应用,基于测量信号的低频振荡分析方法成为研究重点。
针对实测低频振荡信号,受扰轨迹真实反映了系统的动态过程,并且完全包含激振模式的特征信息。但是低频振荡信号是一种典型的非平稳信号,存在振幅随时间变化以及激振模式出现随机性等非平稳信号特点,传统低频振荡信号分析方法基于平稳信号假设模型,对低频振荡模式的非平稳特性参数辨识存在一定的局限性。基于此,本文研究HHT (Hilbert Huang Transform, HHT)方法分析低频振荡问题,围绕该方法应用中的主要理论和关键技术问题开展以下几方面的研究：
1.研究基于测量信号的低频振荡分析方法。针对低频振荡实测信号的特点,对比研究改进Prony方法和HHT方法分析低频振荡信号的理论模型和算法过程,测试分析了Prony方法存在模型定阶、拟合精度以及非平稳参数辨识局限性问题。着重研究了HHT方法分析振荡信号及辨识振荡模式特征参数的方法,讨论振荡模式辨识的物理意义,测试验证了该方法的模式辨识结果具有较高的有效性和精度。
2.讨论影响HHT方法分析低频振荡信号有效性和准确性的主要因素,研究提高HHT方法性能的关键技术和改进方法。针对现有HHT方法存在的端点效应、拟合误差、模式混叠和阻尼损失等问题,通过低通数字滤波技术、基于AR模型的端点延拓技术、B样条插值技术、筛分控制因子和复小波分析等改进技术,实现了一种改进的低频振荡信号分析算法,极大地提高了振荡模式特征参数辨识的精度。
3.针对WAMS提供的实测信号,研究基于HHT方法的在线低频振荡模式分析技术。特别针对EMD (Empirical Mode Decomposition, EMD)过程的模式混叠和低频振荡信号的非平稳参数辨识问题,提出改进频率外差方法和自适应滑动窗口技术,解决了EMD分解中的间歇性和倍频模式混叠问题,同时确定出不同振荡模式的存在时间,并且实现了1s时间进制的近实时低频振荡信号在线分析算法。该算法能够在线自适应分析出系统低频振荡情况,辨识低频振荡模式参数准确度高、实时性强。
4.讨论了基于模型的小干扰稳定性理论在判定振荡模式相关性方面的局限性。研究基于实测信号分析振荡模式相关性,从能量转换的角度建立一种基于HHT方法的贡献因子,量化定义了扰动与低频振荡模式和发电机状态变量之间的相关性关系。设计了基于HHT方法的贡献因子在线算法,通过实例验证了该方法分析与弱阻尼振荡模式和功率迫振模式相关的振荡源定位有效性,进一步完善了HHT方法分析低频振荡问题的理论体系,拓展了该方法的应用范围。
With the development of our national economy, energy consumption and grid scale continue to grow. This makes the stability problems in power system become more complex and its analysis and control are getting harder. In fact, the low frequency oscillation in power system which seriously affects the stable operation of the power system has repeatedly occurred and become one of the key issues which threaten the safety and stability of interconnected power grid and restrict the power transmission capacity.
Traditional methods are based on the power system structure model, such as applying the small signal stability theory to analyze low frequency oscillation modes or Normal Form and modal series method with the non-linear higher order terms considered to analyze low frequency oscillation modes. The analysis accuracy of these methods is subject to the order of the model, the parameters accuracy, un-modeled dynamic and interactive modal factors, so that traditional low frequency oscillation analysis methods have been unable to meet the requirements of stability analysis of modern power system. Nowadays, with the extensive application of WAMS (Wide-Area Measurement System) in the wide area power grid, the analysis of low frequency oscillation of power system based on the measurement signals becomes a hot research topic.
With measured low frequency oscillation signals, the disturbed trajectories reflect the true dynamic process of the system and contain the complete characteristic information of the excitation modes. However, the low frequency oscillation signal is a typical non-stationary signal, which demonstrates non-stationary signal characteristics such as the amplitude changing over time and the random emergence of excitation modes. Traditional measurement signal based analysis methods assume the signal is stable, which limits its use to analyze non-stationary characteristics of the low frequency oscillation modes. Therefore, the data-driven HHT (Hilbert Huang Transform) method is proposed to analyze the low frequency oscillation. In this paper, the main theories and key technical issues of HHT method are studied as following:
1. Low frequency oscillation analysis based on the measured signals. Comparative study is performed on theoretical models and algorithms process of the improved Prony method and the HHT method analysis of low frequency oscillation signal. The limitation of Prony analysis to analyze low frequency oscillation signal is tested and discussed, such as model order, fitting precision and non-stationary parameters identification. Using the HHT method to deal with non-stationary oscillation signal and identify oscillation mode characteristic parameters is studied in detail, including discussing the physical meaning of the method to identify the low frequency oscillation mode and analyzing the results validity and accuracy of low-frequency oscillation mode parameters identification.
2. Analyze the main factors affecting the validity and accuracy of the low frequency oscillation signal based on HHT method, then some key technologies and improved methods to improve the performance of HHT method are studied in detail. For the problem of end effect, fitting error, mode mixing and damping loss existed in HHT method, the low-pass digital filtering technology, the endpoint extension technology based on AR model, the B-spline interpolation technologies, the sifting control factors and complex wavelet analysis method are proposed to achieve an improved EMD (Empirical Mode Decomposition) process, which greatly improves the accuracy of low frequency oscillation mode characteristic parameters identification.
3. Especially in terms of real-time measurement oscillation signals provided by WAMS, the online low frequency oscillation analysis techniques based on HHT is studied in detail. As the EMD process has mode mixing and non-stationary parameter identification problems in low frequency oscillation signal, improved frequency heterodyne method and adaptive sliding window technologies are proposed to deal with the intermittent and octave mode mixing problem within EMD decomposition process. The proposed method is able to determine the presence time of different low frequency oscillation modes and implement the Is hexadecimal near-real-time analysis and identification algorithm of low frequency oscillation signal. The algorithm can adaptively implement the online analysis of measured signal of low frequency oscillation, which has high accuracy and instantaneity to identify the low frequency oscillation mode parameters.
4. The limitations of small signal stability analysis theory to analyze the low frequency oscillation modes correlation with the structure-based model is discussed in detail. The contribution factors, considered from the perspective of energy conversion are established based on the HHT method and measured oscillation signals, which can define the correlation between the disturbance and the low frequency oscillation mode quantitatively. Furthermore, an online algorithm to compute contribution factors based on the HHT method is proposed to solve the correlation problem between the source generators and the oscillation mode when weakly damped oscillation mode and forced oscillation mode appear. The proposed method can further consolidate the theoretical base of low frequency oscillation analysis based on the HHT method and expand its applications.
引文
[1]卢强,梅生伟.面向21世纪的电力系统重大基础研究[J].自然科学进展,2000,10(10)：70-876
[2]白雪峰,倪以信.电力系统动态安全分析综述[J].电网技术,2004,28(16)：14-20
[3]余贻鑫,李鹏.大区域电网弱互联对互联系统阻尼和动态稳定性的影响[J].中国电机工程学报,2005,25(11)：6-11
[4]刘振亚.加快建设坚强国家电网促进中国能源可持续发展[J].中国电力,2006,39(9)：1-3
[5]国家电力调度通信中心.电网典型事故分析[M].北京：中国电力出版社,2008
[6]F. R. Sehleif. Damping for the northwest southwest tie line oscillations an analog study[J]. IEEE Transactions on Power Apparatus and Systems.1966,85(12):1239-1247
[7]石辉,张勇军,徐涛.我国智能电网背景下的低频振荡应对研究综述[J].电力系统保护与控制,2010,38(24)：242-247
[8]D. K. Kosterev, C. Taylor, W. A. Mittelstadt. Model validation for the August 10,1996 WSCC system outage[J]. IEEE Transactions on Power Systems,1999,14(3):967-979
[9]D. K. Kosterev, W. A. Mittelstadt, M. Viles. Modal validation and analysis of WSCC system oscillations following Alberta separation on August 4,2000[M]. Final Report by Bonneville Power Administration and BC Hydro,2001:967-979
[10]V. Sulzberger, T. Gallagher. Reliability and security:the NERC new standards development process[J]. Power and Energy Magazine,2004,2(2):56-61
[ll]葛睿,董昱,李跃春.欧洲“11.4”大停电事故分析及对我国电网运行工作的启示[J].电网技术,2007,31(3)：1-6
[12]李丹,韩福坤,肖晋宇,等.华北电网广域实时监测系统[J].电网技术,2004,28(23)：52-56
[13]贺仁睦,韩志勇,周密,等.互联电力系统未知机理低频振荡分析[J].华北电力大学学报(自然科学版),2009,36(1)：1-5
[14]董明齐,杨东俊,黄涌.2008年冰灾期间华中电网WAMS系统实测低频振荡事件分析[J].华中电力,2(5)：22-25
[15]M. Klein, G. J. Rogers, P. Kundur. A fundamental study of inter-area oscillations in power systems[J]. IEEE Transactions on Power Systems,1991,6(3):914-921
[16]J. V. Milanovie, I. A. Hiskens. Effeets of load dynamies on power system damping[J]. IEEE Transactions on Power Systems,1995,10(2):1022-1028
[17]I. A. Hiskens, J. V. Milanovie. Load modeling in studied of power system damping[J]. IEEE Transactions on Power Systems,1995,10(4):1781-1788
[18]W. S. Kao. The effect of load models on unstable low frequency oscillation damping in taipower system experience w/wo power system stabilizers[J]. IEEE Transactions on Power Systems, 2001,16(3):463-472
[19]王铁强,贺仁睦,王卫国,等.电力系统低频振荡机理的研究[J].中国电机工程学报,2002,22(2)：21-25
[20]汤涌.电力系统强迫功率振荡分析[J].电网技术,1995,19(12)：6-10
[21]韩志勇,贺仁睦,徐衍会.汽轮机压力脉动引发电力系统低频振荡的共振机理分析[J].中国电机工程学报,2008,28(1)：47-51
[22]余一平,闵勇,陈磊,等.周期性负荷扰动引发强迫功率振荡分析[J].电力系统自动化,2010,34(6)：7-11
[23]薛禹胜,郝思鹏,刘俊勇,等.关于低频振荡分析方法的评述[J].电力系统自动化,2009,33(3)：1-8
[24]D. R. Ostojic. Spectral monitoring of power system dynamic performances [J]. IEEE Transactions on Power Systems,1993,8(2):445-451
[25]P. A. OShea. A High resolution spectral analysis algorithm for power system disturbance monitoring. IEEE Transactions on Power Systems,2002,17(3):676-680
[26]张鹏飞,薛禹胜,张启平.电力系统时变振荡特性的小波脊分析[J].电力系统自动化,2004,28(16)：32-35
[27]马景兰,张永丽,万京生.分岔理论及其在电力系统稳定性分析中的应用[J].北京石油化工学院学报,2002,10(3)：1-4
[28]Z. Ao, T. S. Sidhu, R. J. Fleming. Stability investigation of longitudinal power system and its stabilization by a coordinated application of power system stabilizers [J]. IEEE transaction on Energy Conversion,2004,9(2):466-474
[29]C. E. Grund, J. J.Paserba, J. F. Hauer. Comparison of Prony and eigen analysis for power system control decision[J]. IEEE Transactions on Power Systems,1993,8(3):964-971
[30]M. A. Johnson, I. P. Zarafonitis, M. Calligaris. Prony analysis and power system stability-some recent theoretical and applications research[C]. Proceedings of Power Engineering Society Summer Meeting,2000,3(1):1918-1923
[31]D. J. Trudnowski, J. M. Johnson, J. F. Hauer. Making Prony analysis more accurate using multiple signals[J]. IEEE Transaction on Power System,1999,14(1):226-231
[32]J. Zygarlicki, M. Zygarlicka, J. Mroczka, et al. A reduced Prony's method in power quality analysis parameters selection[J]. IEEE Transaction on Power Delivery,2010,25(2):979-986
[33]D. J. Trudnowski, J. R. Smith. An application of Prony methods in PSS design for multimachine systems[J]. IEEE Transactions on Power Systems,1991,6(1):118-126
[34]C. Y. Chung, C. T. Bian, X. Y, David. Probabilistic eigenvalue sensitivity analysis and PSS design in multimachine systems[J]. IEEE Transactions on Power Systems,2003,18(4): 1439-2445
[35]M. Klein, G. J. Rogers, S. Moorty, et al. Analytical investigation of factors influencing power system stabilizers performance[J]. IEEE Transactions on Energy Conversion,1992,7(3): 382-390
[36]C. A. Canizares, Z. T. Faur. Analysis of SVC and TCSC controllers in voltage Collapse[J]. IEEE Transactions on Power Systems,1999,14(1):158-165
[37]S. K Tso, J. Liang, Q. Y. Zeng, et al. Coordination of TCSC and SVC for stability improvement of power systems[C]. IEE International Conference on Advances in Power System Control, Operation and Management. APSCOM'97, HongKong,1997:371-376
[38]田芳,周孝信.利用留数进行可控串补控制装置参数的配置[J].电网技术,1999,23(2):1-5
[39]郭春林,童陆国.多机系统中可控串补(TCSC)抑制功率振荡的研究[J].中国电机工程学报,2004,24(6)：1-6
[40]E. Vaahedi. Dynamic load modeling in large scale stability studies[J]. IEEE Transactions on Power Systems,1988,13(3):1039-1045
[41]M. M. Farsangi, Y. H. Song, K. Y. Lee. Choice of FACTS device control inputs for damping inter-area oscillations[J]. IEEE Transactions on Power Systems,2004,19(2):1135-1143
[42]Y. Zhang, A. Bose. Design of wide-area damping controllers for inter-area oscillations[J]. IEEE Transactions on Power Systems,2008,23(3):1136-1143
[43]郑宝森,郭日彩.中国互联电网的发展[J].电网技术,2003,27(2)：1-3
[44]郭权利.电力系统低频振荡[J].电力系统保护与控制,2008,36(22)：114-116
[45]薛禹胜,徐伟,万秋兰.关于广域测量系统及广域控制保护系统的评述[J].电力系统自动化,2007,31(15)：1-5
[46]D. Karlsson, M. Hemmingsson, S. Lindahl. Wide area system monitoring and control terminology phenomena and solution implementation strategies[J]. IEEE Power and Energy Magazine,2004,2(5):68-76
[47]R. O. Burnet, M. M. Butts, P. S. Sterlina. Power system applications for phasor Measurement Units[J]. IEEE Computer Applications in Power System,1994,7(1):8-13
[48]T. W. Ceaser, B. Feldhans. Real time monitoring of the TVA power system[J]. IEEE Computer Applications in Power System,1994,7(3):47-51
[49]A. P. Meliopoulas, B. Fardanesh, S. Zelingher. Harmonic measurement system via synchronized measurements[C]. Proceedings of EEEE PES Summer Meeting.2000:1094-1100
[50]鞠平,谢欢,孟远景,等.基于广域测量信息在线辨识低频振荡[J].中国电机工程学报,2005,25(22)：59-63
[51]汪永华,王正风.电网动态监测预警与辅助决策系统的应用与发展[J].电力系统保护与控制,2010,38(10)：70-74
[52]陈树恒,李兴源.基于WAMS的低频振荡模式在线辨识算法[J].继电器,2007,35(20)：17-22
[53]杨东俊,丁坚勇,周宏,等.基于WAMS量测数据的低频振荡机理分析[J].电力系统自动化,2009,33(23)：24-28
[54]周小谦.我国“西电东送”的发展历史、规划和实施[J].电网技术,2003,27(5)：1-5
[55]朱方,赵红光,刘增煌,等.大区电网互联对电力系统动态稳定性的影响[J].中国电机工程学报,2007,27(1)：1-7
[56]樊爱军,雷宪章,刘红超,李兴源.研究大规模互联电网区域间振荡的特征值分析方法[J].电网技术,2005,29(17)：35-39
[57]顾丽鸿,周孝信,严剑峰,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
[58]陈树恒,李兴源.基于WAMS的交直流并联输电系统模型辨识算法[J].中国电机工程学报,2008,28(4)：48-53
[59]贾勇,何正友.基于受扰轨迹的低频振荡分析方法综述[J].电力系统保护与控制,2012,40(11)：140-148
[60]W. D. Clercq, B. Vanrumste, J. M. Papy, et al. Modeling common dynamics in multichannel signals with applications to artifact and background removal in EEG recordings [J]. Biomedical Engineering, IEEE Transactions on Biomedical Engineering,2005,52(12):2006-2015
[61]J. Jensen, R. Heusdens. A comparison of differential schemes for low-rate sinusoidal audio coding[C]. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics,2003:205-208
[62]毛炜,金荣洪,耿军平.一种基于改进Hilbert-Huang变换的非平稳信号时频分析法及其应用[J].上海交通大学学报,2006,40(5)：724-729
[63]B. M. Battista, C. Knapp, T. McGee, et al. Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data[J]. Geophysics,2007,72(2):29-37
[64]N. Senroy. Generator coherency using the Hilbert-Huang transform[J]. IEEE Transactions on Power Systems,2008,23(4):1701-1708
[65]M. A. Andrade, A. R. Messina, C. A. Rivera, et al. Identification of instantaneous attributes of torsional shaft signals using the Hilbert transform[J]. IEEE Transactions on Power Systems, 2004,19(3):1422-1429
[66]李天云,高磊,赵妍.基于HHT的电力系统低频振荡分析[J].中国电机工程学报,2006,26(14)：24-30
[67]穆钢,史坤鹏,安军,等.结合经验模态分解的信号能量法及其在低频振荡研究中的应用[J].中国电机工程学报,2008.28(19)：36-41
[68]杨德昌,C. Rehtanz,李勇,等.基于希尔伯特—黄改进算法的电力系统低频振荡分析[J].中国电机工程学报,2011,31(10)：102-108
[69]蔡国伟,杨德友,张俊丰.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
[70]李天云,袁明哲,李军强,等.基于EMD和SSI的电力系统低频振荡模态参数辨识方法[J].电力系统保护与控制,2011,39(8)：6-10
[71]H. F. Wang. On the connections among the electric torque, residue, functional sensitivity, participation and partial modal decomposition[C]. International Conference on Control,1998: 1005-1010
[72]R. J. Piwko, H. A. Othman, O. A. Alvarez. Eigenvalue and frequency-domain analysis of the intermountain power project and the WSCC network[J]. IEEE Transactions on Power Systems, 1991,6(1):238-244
[73]E. Z. Zhou. Functional sensitivity concept and its application to power system damping analysis[J]. IEEE Transactions on Power Systems,1994,9(1):518-524
[74]L. Rouco. Eigenvalue based methods for analysis and control of power system oscillations[C]. IEE Colloquium on Power System Dynamics Stabilization,1998,3:1-6
[75]D. Wong, G. J. Rogers, B. Porretta, et al. Eigenvalue analysis of very large power system[J]. IEEE Transactions on Power Systems,1988,3(2):472-480
[76]吴复霞.电力系统低频振荡的分析和控制[D].杭州,浙江大学,2007
[77]倪以信,陈寿孙,张宝霖.动态电力系统的理论和分析[M].北京：清华大学出版社,2002
[78]G. Angelidis, A. Semlyen. Improved methodologies for the calculation of eigenvalue in small signal stability analysis[J]. IEEE Transactions on Power Systems,1996,11(3):1209-1217
[79]L. Rouco, I. J. Perezarriga. Multi-area analysis of small signal stability in large electric power system by SMA[J]. IEEE Transactions on Power Systems,1993,8(3):1257-1265
[80]P. Kundur, G. J. Rogers, D. Y. Wong, et al. A comprehensive computer program package for small signal stability analysis of power system[J]. IEEE Transactions on Power Systems,1990, 5(4):1076-1083
[81]P. Kundur. Power system stability and control[M]. McGraw-Hilll Inc., New York,1994
[82]C. Y. Chung, K. W. Wang, C. T. Tse, et al. Probabilistic eigenvalue sensitivity analysis and PSS design in multimachine systems[J]. IEEE Transactions on Power Systems,2003,18(4): 1439-1445
[83]赵书强,常鲜戎,贺仁睦.基于等值二机模型的多机PSS极点配置[J].电力系统自动化,1999,23(22)：28-31
[84]A. Dysko, W. E. Leithead, J. O'Reilly. Enhanced power system stability by coordinated PSS design[J]. IEEE Transactions on Power Systems,2009,25(1):413-422
[85]郝思鹏,薛禹胜,张晓明,等.基于EEAC理论分析低频振荡[J].电力系统自动化,2009,33(4)：1l-15
[86]F. P. DeMello, C. Coreordia. Concept of synchronous machine stability as affected by excitation control[J]. IEEE Transactions on Power Apparatus and Systems,1969,88(4):316-329
[87]N. Pariz, H. M. Shanechi, E. Vaahedi. Explaining and validating stressed power systems behavior using modal series[J]. IEEE Transactions on Power Systems,2003,18(2):778-785
[88]N. Pariz, H. M. Shanechi, E. Vaahedi. General nonlinear modal representation of large scale power systems[J]. IEEE Transactions on Power Systems,2003,18(3):1103-1109
[89]汤涌.电力系统强迫功率振荡的基础理论[J].电网技术,2006,30(10)：29-33
[90]王铁强.电力系统低频振荡共振机理的研究[D].北京：华北电力大学,2001
[91]徐东杰.互联电力系统低频振荡分析方法与控制策略研究[D].北京：华北电力大学,2004
[92]H. G. Kwatny, X. M. Yu. Energy analysis of load-induced flutter instability in classical modes electric power networks[J]. IEEE Transactions on Circuits and Systems,1989,38:1544-1557
[93]I. Dobson, J. Zhang, S. Greene, et al.Is strong modal resonance a precursor to power system oscillation[J]. IEEE Transactions on Circuits and Systems,2001,48(3):340-349
[94]F. Howell, V. Venkatasubramanian. Transient stability assessment with unstable limit cycle approximation[J]. IEEE Transactions on Power Systems,1999,14(2):667-677
[95]J. Li, V.Venkatasubramanian. Study of unstable limit cycle power system models[C]. Power Engineering Society Summer Meeting,2000:842-847
[96]H. D. Chiang, C. W. Liu, P. Varaiya, et al. Chaos in a simple power system[J]. IEEE Transactions on Power Systems,1993,8(4):1407-1417
[97]W. Ji, V. Venkatasubramanian. Hard limit induced chaos in a single machine infinite bus power system[C]. Proceedings of the 34th Conference on Decision and Control,1995,4:3465-3470
[98]张卫东,张伟年.电力系统混沌振荡的参数分析[J].电网技术,2000,24(12)：17-20
[99]贾宏杰,余贻鑫,工成山.考虑励磁顶值与PSS的混沌和分岔现象[J].电力系统自动化.2001,25(1)：11-14
[100]刘隽,李兴源,邹全平,等.互联电网低频振荡的相关问题及研究[J].继电器,2005,33(16)：70-78
[101]P. W. Sauer, C. Rajagopalan. M. A. Pai. An explanation and generalization of the AESOPS and PEALS algorithms[J]. IEEE Transactions on Power Systems,1991,6(1):293-299
[102]L. Rouco. I. L Perez-Arriga. Multi-area analysis of small signal stability in large power system by SMA[J]. IEEE Transactions on Power Systems,1993,8(3):1257-1265
[103]D. J. Stadnicki, J. V. Ness. Invariant subspace method for eigenvalue computation[J]. IEEE Transactions on Power Systems,1993,8(2):572-550
[104]S. Duff, J. A. Seott. Computing seleeted eigenvalues of sparse unsymmetric matrice using subspace iteration[J]. ACM Transaction on Mathematical Software,1993,19(2):137-159.
[105]杜正春,刘伟,方万良,等.大规模电力系统关键特征值计算的Amoldi-Chebyshev方法[J].西安交通大学学报,2004,35(10)：995-999
[106]杜正春,刘伟,方万良,等.基于JACOBI-DAVIDSON方法的小干扰稳足性分析中关键特征值计算[J].中国电机工程学报,2005,25(14)：19-24
[107]L.C.Ming, V. Vittal, W. Klieman. Investigation of modal interaction and its effects on control performance in stressed power systems using normal forms of vector fields[J]. IEEE Transactions on Power Systems,1996,11(2):781-787
[108]G. Jang, V. Vittal, W. Klieman. Effect of nonlinear modal interaction on control performance: using normal forms technique in control design, Part 1:General theory and procedure[J]. IEEE Transactions on Power Systems,1998,13(2):401-407
[109]G. Jang, V. Vittal, W. Klieman. Effect of nonlinear modal interaction on control performance: using normal forms technique in control design, Part 2:Case studies[J]. IEEE Transactions on Power Systems,1998,13(2):408-413
[110]I. Dobson, E. Baroeio. Scaling of normal form analysis coefficients under coordinate change[J]. IEEE Transactions on Power Systems,2004,19(3):1438-1444
[111]邓集祥,华瑶,韩雪飞.大干扰稳定中低频振荡模式的作用研究[J].中国电机工程学报,2003,23(11)：60-64
[112]邓集祥,赵丽丽.主导低频振荡模式二阶作线性相关作用的研究[J].中国电机工程学报,2005,25(7)：75-80
[113]邓集祥,赵丽丽.励磁调节器对模态非线性相关作用的影响[J].电网技术,2005,29(1)：69-74
[114]吴复霞,吴浩,韩祯祥,等.电力系统非线性模式分析方法的比较[J].中国电机工程学报,2007,27(34)：：9-25
[115]郑云海,李兴源.基于模态级数法的交直流系统的非线性模式交互作用分析[J].电工电能新技术,2005,24(3)：45-48
[116]邓集祥,许自然.应用模态级数方法分析电力系统模态谐振[J].现代电力,2006,23(3)：11-15
[117]Y. Zhang, A. Bose. Design of wide-area damping controllers for inter-area oscillations[J]. IEEE Transactions on Power Systems,2008,23(3):1136-1143
[118]叶华.大规模电力系统低频振荡分析与广域自适应控制研究[D].济南：山东大学,2009
[119]A. G. Phadke. Synchronized phasor measurements in power systems[J]. IEEE Computer Applications in Power,1993,6(2):10-15
[120]张鹏飞,薛禹胜,张启平,等.基于PMU实测摇摆曲线的暂态稳定量化分析[J].电力系统自动化,2004,28(20)：17-20
[121]宋方方,毕天妹,杨奇逊.基于广域测量系统的电力系统多摆稳定性评估方法[J].中国电机工程学报,2006,26(16)：38-45
[122]N. Kakimoto, S. G. Masahiro, T. Makino, et al. Monitoring of interarea oscillation mode by synchronized phasor measurement[J]. IEEE Transactions on Power Systems,2006,21(1): 260-268
[123]M. Qiu, W. Gao, M. Chen, et al. Energy efficient security algorithm for power grid wide area monitoring system[J]. IEEE Transactions on Smart Grid,2011,2(4):715-723
[124]李强,周京阳,于尔铿.基于混合量测的电力系统状态估计混合算法[J].电力系统自动化, 2005,29(19)：31-35
[125]贺仁睦.电网动态实时监控及管理系统的构想[J].电力系统自动化,2002,26(5)：1-4
[126]黄志刚,邬炜,韩英铎.基于同步相量测量的电压稳定评估算法[J].电力系统自动化,2002,26(2)：28-33
[127]赵金利,余贻鑫.基于本地相量测量的电压失稳指标工作条件分析[J].电力系统自动化,2006,30(24)：1-4
[128]彭疆南,孙元章,王海风.基于广域量测数据和导纳参数在线辨识的受扰轨迹预测[J].电力系统自动化,2003,27(22)：6-11
[129]宋方方,毕天妹,杨奇逊.基于WAMS的电力系统受扰轨迹预测[J].电力系统自动化,2006,30(23)：27-32
[130]宋晓娜,毕天妹,吴京涛,等.基于WAMS的电网扰动识别方法[J].电力系统自动化,2006,30(5)：24-28
[131]I. Kamwa, R. Grondin, Y. Hebert. Wide-area measurement based stabilizing control of large power systems a decentralized hierarchical approach[J]. IEEE Transactions on Power Systems, 2001,16(1):136-153
[132]谢小荣,肖晋宇,童陆园,等.采用广域测量信号的互联电网区间阻尼控制[J].电力系统自动化,2004,28(2)：37-40
[133]江全元,白碧蓉,邹振宇.计及广域测量系统时滞影响的TCSC控制器设计[J].电力系统自动化,2004,28(20)：21-25
[134]袁野,程林,孙元章.采用广域测量信号的2级PSS控制策略[J].电力系统自动化,2006,30(24)：11-16
[135]J. F. Hauer, C. J. Demeure, L. L. Scharf. Initial results in prony analysis of power system response signals[J]. IEEE Transactions on Power Systems,1990,5(1):80-89
[136]C. E. Grund, J. J. Paserba, J. F. Hauer, et al. Comparison of Prony and eigen analysis for power system control design[J]. IEEE Transactions on Power Systems,1993,8(3):964-971
[137]侯王宾,刘天琪,李兴源.基于自适应神经模糊滤波的低频振荡Prony分析[J].电网技术,2010,34(6)：53-58
[138]刘森,赵书强,于赞梅,等.基于小波预处理技术的低频振荡Prony分析[J].电力自动化设备,2007,27(4)：64-68
[139]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
[140]赵礼杰.基于EMD的Prony算法在低频振荡模态参数辨识中的应用[J]. 电力系统保护 与控制,2009,37(23)：9-15
[141]肖晋宇,谢小荣,胡志祥,等.电力系统低频振荡在线辨识的改进Prony算法[J].清华大学学报,2004,44(7)：883-887
[142]王铁强,贺仁睦,徐东杰,等.Prony算法分析低频振荡的有效性研究[J].中国电力,2001,34(11)：38-41.
[143]R. W. Wies, J. W. Pierre, D. J. Trudnowski. Use of ARMA block processing for estimating stationary low-frequency electromechanical modes of power systems[J]. IEEE Transactions on Power Systems,2003,18(1):167-182.
[144]N. Zhou, J. W. Pierre, D. J. Trudnowski, et al. Robust RLS methods for online estimation of power system electromechanical modes[J]. IEEE Transactions on Power Systems,2007,22(3): 1240-1249
[145]N. Zhou, D. J. Trudnowski, J. W. Pierre, et al. Electromechanical mode online estimation using regularized robust RLS methods[J]. IEEE Transactions on Power Systems,2008,23(4): 1670-1680
[146]J. J. Gasca, J. H. Chow. Computation of power system low-order models from time domain simulations using a Hankel matrix[J]. IEEE Transactions on Power Systems,1997,12(4): 1461-1467
[147]王宇静,于继来.电力系统振荡模态的矩阵束辨识法[J].中国电机工程学报,2007,27(19)：12-17
[148]G. Liu, J. Quintero, V. Venkatasubramanian. Oscillation monitoring system based on wide area synchrophasors in power systems[C]. Bulk Power System Dynamics and Control-VII. Revitalizing Operational Reliability,2007 iREP Symposium,2007
[149]J. J. Gasca, J. H. Chow. Performance comparison of three identification methods for the analysis of electromechanical oscillations[J]. IEEE Transactions on Power Systems,1999,14(3): 995-1002
[150]P. O'Shea. The use of sliding spectral windows for parameter estimation in power system disturbance monitoring[J]. IEEE Transactions on Power Systems,2000,15(4):1261-1267
[151]N. Huang, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Roral Society A,1998,45:903-995
[152]G. Rilling, P. Flandrin, P. Goncalves. On empirical mode decomposition and its algorithms[C]. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing,2003:27-33.
[153]A.R.Messina, V. Vittal. Nonlinear non-Stationary analysis of inter-area oscillations via Hilbert spectral analysis[J]. IEEE Transactions on Power Systems,2006,21(3):1234-1241
[154]A. R. Messina, V. Vittal, D. Ruiz-Vega. Interpretation and visualization of wide-area PMU measurements using Hilbert analysis[J]. IEEE Transactions on Power Systems,2006,21(4): 1763-1771
[155]N.E. Huang, M. Wu, S. Long, et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis[J]. Proceedings of the Roral Society A,2003,45(9):2317-2345
[156]N. Senroy, S. Suryanarayanan, P. F. Ribeiro. An improved Hilbert-Huang method for analysis of time-varying waveforms in power quality[J]. IEEE Transactions on Power Systems,2007, 22(4):1843-1850
[157]许宝杰,张建民.抑制EMD端点效应方法的研究[J].北京理工大学学报,2006,26(3)：196-200
[158]蔡艳平,李艾华,张玮.HHT端点效应的最大Lyapunov指数边界延拓方法[J].仪器仪表学报,2011,32(6)：1330-1336
[159]李天云,谢家安,张方彦,等.HHT在电力系统低频振荡模态参数提取中的应用[J].中国电机工程学报,2007,27(28)：79-83
[160]D. S. Laila, A. R. Messina, B. C. Pal. A refined Hilbert Huang transform with applications to inter-area oscillation monitoring[J]. IEEE Transactions on Power Systems,2009,24(2):610-620.
[161]李成鑫,刘俊勇,姚良忠,等.基于改进频移经验模态分解的低频振荡参数提取[J].电力系统自动化,2012,36(15)：8-13
[162]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
[163]吴超,陆超,韩英铎,等.Prony方法和ARMA法在低频振荡模式辨识中的适用性比较[J].电力自动化设备,2010,83(3)：30-34
[164]J. Xin, Z. Han, G. Wu, et al. Energy analysis of power system low frequency oscillation of resonance mechanism[C]. The Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies,2008:251-263
[165]G. Rilling, P. Flandrin. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Transaction on Signal Process,2008,56(1):85-95
[166]R. Deering, J. F. Kaiser. The use of masking signal to improve empirical mode decomposition[C]. IEEE International Conference Acoustics, Speech Signal Processing,2005:485-488
[167]N. Senroy, S. Suryanarayanan. Two techniques to enhance empirical mode decomposition for power quality applications[C]. IEEE PES General Meeting,2007:1-6
[168]何正友,钱清泉.电力系统暂态信号分析中小波基的选择原则[J].电力系统自动化,2003,27(10)：45-49
[169]杨淑英,王丽宏,杜荣华.电力系统暂态保护中小波基的选择与应用[J].电力系统及其自动化学,2008,20(5)：106-111
[170]L. Jose, R. Carlos, A. Juarez, et al. Wavelet-based analysis of power system low-frequency electromechanical oscillations[J]. IEEE Transactions on Power System,2011,26(3):1733-1744
[171]D. S. Laila, A. R. Messina, C. Bikash. A refined Hilbert-Huang transform with applications to inter-area oscillation monitoring[J]. Transactions on Power Systems,2009,24(2):610-620
[172]A. Prince, N. Senroy. Targeted approach to apply masking signal-based empirical mode decomposition for mode identification from dynamic power system wide area measurement signal data[J]. IET Generation Transmission & Distribution,2011,5(10):1025-1032
[173]彭谦,马晨光,杨雪梅,等.线性模态分析中的参与因子与贡献因子[J].电网技术,2010,34(2)：92-97
[174]韩志勇,贺仁睦,徐衍会,等.基于能量角度的共振机理电力系统低频振荡分析[J].电网技术,2007,31(8)：13-16
[175]董清,张玲,颜湘武.低频振荡扰动源机组的自动定位方法[J].电网技术,2012,36(10)：265-270
[176]余一平,闵勇,陈磊.多机电力系统强迫功率振荡稳态响应特性分析[J].电力系统自动化,2009,33(22)：5-10
[177]余一平,闵勇,陈磊,等.基于能量函数的强迫功率振荡扰动源定位[J].电力系统自动化,2010,34(5)：1-6
[2]白雪峰,倪以信.电力系统动态安全分析综述[J].电网技术,2004,28(16)：14-20
[3]余贻鑫,李鹏.大区域电网弱互联对互联系统阻尼和动态稳定性的影响[J].中国电机工程学报,2005,25(11)：6-11
[4]刘振亚.加快建设坚强国家电网促进中国能源可持续发展[J].中国电力,2006,39(9)：1-3
[5]国家电力调度通信中心.电网典型事故分析[M].北京：中国电力出版社,2008
[6]F. R. Sehleif. Damping for the northwest southwest tie line oscillations an analog study[J]. IEEE Transactions on Power Apparatus and Systems.1966,85(12):1239-1247
[7]石辉,张勇军,徐涛.我国智能电网背景下的低频振荡应对研究综述[J].电力系统保护与控制,2010,38(24)：242-247
[8]D. K. Kosterev, C. Taylor, W. A. Mittelstadt. Model validation for the August 10,1996 WSCC system outage[J]. IEEE Transactions on Power Systems,1999,14(3):967-979
[9]D. K. Kosterev, W. A. Mittelstadt, M. Viles. Modal validation and analysis of WSCC system oscillations following Alberta separation on August 4,2000[M]. Final Report by Bonneville Power Administration and BC Hydro,2001:967-979
[10]V. Sulzberger, T. Gallagher. Reliability and security:the NERC new standards development process[J]. Power and Energy Magazine,2004,2(2):56-61
[ll]葛睿,董昱,李跃春.欧洲“11.4”大停电事故分析及对我国电网运行工作的启示[J].电网技术,2007,31(3)：1-6
[12]李丹,韩福坤,肖晋宇,等.华北电网广域实时监测系统[J].电网技术,2004,28(23)：52-56
[13]贺仁睦,韩志勇,周密,等.互联电力系统未知机理低频振荡分析[J].华北电力大学学报(自然科学版),2009,36(1)：1-5
[14]董明齐,杨东俊,黄涌.2008年冰灾期间华中电网WAMS系统实测低频振荡事件分析[J].华中电力,2(5)：22-25
[15]M. Klein, G. J. Rogers, P. Kundur. A fundamental study of inter-area oscillations in power systems[J]. IEEE Transactions on Power Systems,1991,6(3):914-921
[16]J. V. Milanovie, I. A. Hiskens. Effeets of load dynamies on power system damping[J]. IEEE Transactions on Power Systems,1995,10(2):1022-1028
[17]I. A. Hiskens, J. V. Milanovie. Load modeling in studied of power system damping[J]. IEEE Transactions on Power Systems,1995,10(4):1781-1788
[18]W. S. Kao. The effect of load models on unstable low frequency oscillation damping in taipower system experience w/wo power system stabilizers[J]. IEEE Transactions on Power Systems, 2001,16(3):463-472
[19]王铁强,贺仁睦,王卫国,等.电力系统低频振荡机理的研究[J].中国电机工程学报,2002,22(2)：21-25
[20]汤涌.电力系统强迫功率振荡分析[J].电网技术,1995,19(12)：6-10
[21]韩志勇,贺仁睦,徐衍会.汽轮机压力脉动引发电力系统低频振荡的共振机理分析[J].中国电机工程学报,2008,28(1)：47-51
[22]余一平,闵勇,陈磊,等.周期性负荷扰动引发强迫功率振荡分析[J].电力系统自动化,2010,34(6)：7-11
[23]薛禹胜,郝思鹏,刘俊勇,等.关于低频振荡分析方法的评述[J].电力系统自动化,2009,33(3)：1-8
[24]D. R. Ostojic. Spectral monitoring of power system dynamic performances [J]. IEEE Transactions on Power Systems,1993,8(2):445-451
[25]P. A. OShea. A High resolution spectral analysis algorithm for power system disturbance monitoring. IEEE Transactions on Power Systems,2002,17(3):676-680
[26]张鹏飞,薛禹胜,张启平.电力系统时变振荡特性的小波脊分析[J].电力系统自动化,2004,28(16)：32-35
[27]马景兰,张永丽,万京生.分岔理论及其在电力系统稳定性分析中的应用[J].北京石油化工学院学报,2002,10(3)：1-4
[28]Z. Ao, T. S. Sidhu, R. J. Fleming. Stability investigation of longitudinal power system and its stabilization by a coordinated application of power system stabilizers [J]. IEEE transaction on Energy Conversion,2004,9(2):466-474
[29]C. E. Grund, J. J.Paserba, J. F. Hauer. Comparison of Prony and eigen analysis for power system control decision[J]. IEEE Transactions on Power Systems,1993,8(3):964-971
[30]M. A. Johnson, I. P. Zarafonitis, M. Calligaris. Prony analysis and power system stability-some recent theoretical and applications research[C]. Proceedings of Power Engineering Society Summer Meeting,2000,3(1):1918-1923
[31]D. J. Trudnowski, J. M. Johnson, J. F. Hauer. Making Prony analysis more accurate using multiple signals[J]. IEEE Transaction on Power System,1999,14(1):226-231
[32]J. Zygarlicki, M. Zygarlicka, J. Mroczka, et al. A reduced Prony's method in power quality analysis parameters selection[J]. IEEE Transaction on Power Delivery,2010,25(2):979-986
[33]D. J. Trudnowski, J. R. Smith. An application of Prony methods in PSS design for multimachine systems[J]. IEEE Transactions on Power Systems,1991,6(1):118-126
[34]C. Y. Chung, C. T. Bian, X. Y, David. Probabilistic eigenvalue sensitivity analysis and PSS design in multimachine systems[J]. IEEE Transactions on Power Systems,2003,18(4): 1439-2445
[35]M. Klein, G. J. Rogers, S. Moorty, et al. Analytical investigation of factors influencing power system stabilizers performance[J]. IEEE Transactions on Energy Conversion,1992,7(3): 382-390
[36]C. A. Canizares, Z. T. Faur. Analysis of SVC and TCSC controllers in voltage Collapse[J]. IEEE Transactions on Power Systems,1999,14(1):158-165
[37]S. K Tso, J. Liang, Q. Y. Zeng, et al. Coordination of TCSC and SVC for stability improvement of power systems[C]. IEE International Conference on Advances in Power System Control, Operation and Management. APSCOM'97, HongKong,1997:371-376
[38]田芳,周孝信.利用留数进行可控串补控制装置参数的配置[J].电网技术,1999,23(2):1-5
[39]郭春林,童陆国.多机系统中可控串补(TCSC)抑制功率振荡的研究[J].中国电机工程学报,2004,24(6)：1-6
[40]E. Vaahedi. Dynamic load modeling in large scale stability studies[J]. IEEE Transactions on Power Systems,1988,13(3):1039-1045
[41]M. M. Farsangi, Y. H. Song, K. Y. Lee. Choice of FACTS device control inputs for damping inter-area oscillations[J]. IEEE Transactions on Power Systems,2004,19(2):1135-1143
[42]Y. Zhang, A. Bose. Design of wide-area damping controllers for inter-area oscillations[J]. IEEE Transactions on Power Systems,2008,23(3):1136-1143
[43]郑宝森,郭日彩.中国互联电网的发展[J].电网技术,2003,27(2)：1-3
[44]郭权利.电力系统低频振荡[J].电力系统保护与控制,2008,36(22)：114-116
[45]薛禹胜,徐伟,万秋兰.关于广域测量系统及广域控制保护系统的评述[J].电力系统自动化,2007,31(15)：1-5
[46]D. Karlsson, M. Hemmingsson, S. Lindahl. Wide area system monitoring and control terminology phenomena and solution implementation strategies[J]. IEEE Power and Energy Magazine,2004,2(5):68-76
[47]R. O. Burnet, M. M. Butts, P. S. Sterlina. Power system applications for phasor Measurement Units[J]. IEEE Computer Applications in Power System,1994,7(1):8-13
[48]T. W. Ceaser, B. Feldhans. Real time monitoring of the TVA power system[J]. IEEE Computer Applications in Power System,1994,7(3):47-51
[49]A. P. Meliopoulas, B. Fardanesh, S. Zelingher. Harmonic measurement system via synchronized measurements[C]. Proceedings of EEEE PES Summer Meeting.2000:1094-1100
[50]鞠平,谢欢,孟远景,等.基于广域测量信息在线辨识低频振荡[J].中国电机工程学报,2005,25(22)：59-63
[51]汪永华,王正风.电网动态监测预警与辅助决策系统的应用与发展[J].电力系统保护与控制,2010,38(10)：70-74
[52]陈树恒,李兴源.基于WAMS的低频振荡模式在线辨识算法[J].继电器,2007,35(20)：17-22
[53]杨东俊,丁坚勇,周宏,等.基于WAMS量测数据的低频振荡机理分析[J].电力系统自动化,2009,33(23)：24-28
[54]周小谦.我国“西电东送”的发展历史、规划和实施[J].电网技术,2003,27(5)：1-5
[55]朱方,赵红光,刘增煌,等.大区电网互联对电力系统动态稳定性的影响[J].中国电机工程学报,2007,27(1)：1-7
[56]樊爱军,雷宪章,刘红超,李兴源.研究大规模互联电网区域间振荡的特征值分析方法[J].电网技术,2005,29(17)：35-39
[57]顾丽鸿,周孝信,严剑峰,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
[58]陈树恒,李兴源.基于WAMS的交直流并联输电系统模型辨识算法[J].中国电机工程学报,2008,28(4)：48-53
[59]贾勇,何正友.基于受扰轨迹的低频振荡分析方法综述[J].电力系统保护与控制,2012,40(11)：140-148
[60]W. D. Clercq, B. Vanrumste, J. M. Papy, et al. Modeling common dynamics in multichannel signals with applications to artifact and background removal in EEG recordings [J]. Biomedical Engineering, IEEE Transactions on Biomedical Engineering,2005,52(12):2006-2015
[61]J. Jensen, R. Heusdens. A comparison of differential schemes for low-rate sinusoidal audio coding[C]. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics,2003:205-208
[62]毛炜,金荣洪,耿军平.一种基于改进Hilbert-Huang变换的非平稳信号时频分析法及其应用[J].上海交通大学学报,2006,40(5)：724-729
[63]B. M. Battista, C. Knapp, T. McGee, et al. Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data[J]. Geophysics,2007,72(2):29-37
[64]N. Senroy. Generator coherency using the Hilbert-Huang transform[J]. IEEE Transactions on Power Systems,2008,23(4):1701-1708
[65]M. A. Andrade, A. R. Messina, C. A. Rivera, et al. Identification of instantaneous attributes of torsional shaft signals using the Hilbert transform[J]. IEEE Transactions on Power Systems, 2004,19(3):1422-1429
[66]李天云,高磊,赵妍.基于HHT的电力系统低频振荡分析[J].中国电机工程学报,2006,26(14)：24-30
[67]穆钢,史坤鹏,安军,等.结合经验模态分解的信号能量法及其在低频振荡研究中的应用[J].中国电机工程学报,2008.28(19)：36-41
[68]杨德昌,C. Rehtanz,李勇,等.基于希尔伯特—黄改进算法的电力系统低频振荡分析[J].中国电机工程学报,2011,31(10)：102-108
[69]蔡国伟,杨德友,张俊丰.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
[70]李天云,袁明哲,李军强,等.基于EMD和SSI的电力系统低频振荡模态参数辨识方法[J].电力系统保护与控制,2011,39(8)：6-10
[71]H. F. Wang. On the connections among the electric torque, residue, functional sensitivity, participation and partial modal decomposition[C]. International Conference on Control,1998: 1005-1010
[72]R. J. Piwko, H. A. Othman, O. A. Alvarez. Eigenvalue and frequency-domain analysis of the intermountain power project and the WSCC network[J]. IEEE Transactions on Power Systems, 1991,6(1):238-244
[73]E. Z. Zhou. Functional sensitivity concept and its application to power system damping analysis[J]. IEEE Transactions on Power Systems,1994,9(1):518-524
[74]L. Rouco. Eigenvalue based methods for analysis and control of power system oscillations[C]. IEE Colloquium on Power System Dynamics Stabilization,1998,3:1-6
[75]D. Wong, G. J. Rogers, B. Porretta, et al. Eigenvalue analysis of very large power system[J]. IEEE Transactions on Power Systems,1988,3(2):472-480
[76]吴复霞.电力系统低频振荡的分析和控制[D].杭州,浙江大学,2007
[77]倪以信,陈寿孙,张宝霖.动态电力系统的理论和分析[M].北京：清华大学出版社,2002
[78]G. Angelidis, A. Semlyen. Improved methodologies for the calculation of eigenvalue in small signal stability analysis[J]. IEEE Transactions on Power Systems,1996,11(3):1209-1217
[79]L. Rouco, I. J. Perezarriga. Multi-area analysis of small signal stability in large electric power system by SMA[J]. IEEE Transactions on Power Systems,1993,8(3):1257-1265
[80]P. Kundur, G. J. Rogers, D. Y. Wong, et al. A comprehensive computer program package for small signal stability analysis of power system[J]. IEEE Transactions on Power Systems,1990, 5(4):1076-1083
[81]P. Kundur. Power system stability and control[M]. McGraw-Hilll Inc., New York,1994
[82]C. Y. Chung, K. W. Wang, C. T. Tse, et al. Probabilistic eigenvalue sensitivity analysis and PSS design in multimachine systems[J]. IEEE Transactions on Power Systems,2003,18(4): 1439-1445
[83]赵书强,常鲜戎,贺仁睦.基于等值二机模型的多机PSS极点配置[J].电力系统自动化,1999,23(22)：28-31
[84]A. Dysko, W. E. Leithead, J. O'Reilly. Enhanced power system stability by coordinated PSS design[J]. IEEE Transactions on Power Systems,2009,25(1):413-422
[85]郝思鹏,薛禹胜,张晓明,等.基于EEAC理论分析低频振荡[J].电力系统自动化,2009,33(4)：1l-15
[86]F. P. DeMello, C. Coreordia. Concept of synchronous machine stability as affected by excitation control[J]. IEEE Transactions on Power Apparatus and Systems,1969,88(4):316-329
[87]N. Pariz, H. M. Shanechi, E. Vaahedi. Explaining and validating stressed power systems behavior using modal series[J]. IEEE Transactions on Power Systems,2003,18(2):778-785
[88]N. Pariz, H. M. Shanechi, E. Vaahedi. General nonlinear modal representation of large scale power systems[J]. IEEE Transactions on Power Systems,2003,18(3):1103-1109
[89]汤涌.电力系统强迫功率振荡的基础理论[J].电网技术,2006,30(10)：29-33
[90]王铁强.电力系统低频振荡共振机理的研究[D].北京：华北电力大学,2001
[91]徐东杰.互联电力系统低频振荡分析方法与控制策略研究[D].北京：华北电力大学,2004
[92]H. G. Kwatny, X. M. Yu. Energy analysis of load-induced flutter instability in classical modes electric power networks[J]. IEEE Transactions on Circuits and Systems,1989,38:1544-1557
[93]I. Dobson, J. Zhang, S. Greene, et al.Is strong modal resonance a precursor to power system oscillation[J]. IEEE Transactions on Circuits and Systems,2001,48(3):340-349
[94]F. Howell, V. Venkatasubramanian. Transient stability assessment with unstable limit cycle approximation[J]. IEEE Transactions on Power Systems,1999,14(2):667-677
[95]J. Li, V.Venkatasubramanian. Study of unstable limit cycle power system models[C]. Power Engineering Society Summer Meeting,2000:842-847
[96]H. D. Chiang, C. W. Liu, P. Varaiya, et al. Chaos in a simple power system[J]. IEEE Transactions on Power Systems,1993,8(4):1407-1417
[97]W. Ji, V. Venkatasubramanian. Hard limit induced chaos in a single machine infinite bus power system[C]. Proceedings of the 34th Conference on Decision and Control,1995,4:3465-3470
[98]张卫东,张伟年.电力系统混沌振荡的参数分析[J].电网技术,2000,24(12)：17-20
[99]贾宏杰,余贻鑫,工成山.考虑励磁顶值与PSS的混沌和分岔现象[J].电力系统自动化.2001,25(1)：11-14
[100]刘隽,李兴源,邹全平,等.互联电网低频振荡的相关问题及研究[J].继电器,2005,33(16)：70-78
[101]P. W. Sauer, C. Rajagopalan. M. A. Pai. An explanation and generalization of the AESOPS and PEALS algorithms[J]. IEEE Transactions on Power Systems,1991,6(1):293-299
[102]L. Rouco. I. L Perez-Arriga. Multi-area analysis of small signal stability in large power system by SMA[J]. IEEE Transactions on Power Systems,1993,8(3):1257-1265
[103]D. J. Stadnicki, J. V. Ness. Invariant subspace method for eigenvalue computation[J]. IEEE Transactions on Power Systems,1993,8(2):572-550
[104]S. Duff, J. A. Seott. Computing seleeted eigenvalues of sparse unsymmetric matrice using subspace iteration[J]. ACM Transaction on Mathematical Software,1993,19(2):137-159.
[105]杜正春,刘伟,方万良,等.大规模电力系统关键特征值计算的Amoldi-Chebyshev方法[J].西安交通大学学报,2004,35(10)：995-999
[106]杜正春,刘伟,方万良,等.基于JACOBI-DAVIDSON方法的小干扰稳足性分析中关键特征值计算[J].中国电机工程学报,2005,25(14)：19-24
[107]L.C.Ming, V. Vittal, W. Klieman. Investigation of modal interaction and its effects on control performance in stressed power systems using normal forms of vector fields[J]. IEEE Transactions on Power Systems,1996,11(2):781-787
[108]G. Jang, V. Vittal, W. Klieman. Effect of nonlinear modal interaction on control performance: using normal forms technique in control design, Part 1:General theory and procedure[J]. IEEE Transactions on Power Systems,1998,13(2):401-407
[109]G. Jang, V. Vittal, W. Klieman. Effect of nonlinear modal interaction on control performance: using normal forms technique in control design, Part 2:Case studies[J]. IEEE Transactions on Power Systems,1998,13(2):408-413
[110]I. Dobson, E. Baroeio. Scaling of normal form analysis coefficients under coordinate change[J]. IEEE Transactions on Power Systems,2004,19(3):1438-1444
[111]邓集祥,华瑶,韩雪飞.大干扰稳定中低频振荡模式的作用研究[J].中国电机工程学报,2003,23(11)：60-64
[112]邓集祥,赵丽丽.主导低频振荡模式二阶作线性相关作用的研究[J].中国电机工程学报,2005,25(7)：75-80
[113]邓集祥,赵丽丽.励磁调节器对模态非线性相关作用的影响[J].电网技术,2005,29(1)：69-74
[114]吴复霞,吴浩,韩祯祥,等.电力系统非线性模式分析方法的比较[J].中国电机工程学报,2007,27(34)：：9-25
[115]郑云海,李兴源.基于模态级数法的交直流系统的非线性模式交互作用分析[J].电工电能新技术,2005,24(3)：45-48
[116]邓集祥,许自然.应用模态级数方法分析电力系统模态谐振[J].现代电力,2006,23(3)：11-15
[117]Y. Zhang, A. Bose. Design of wide-area damping controllers for inter-area oscillations[J]. IEEE Transactions on Power Systems,2008,23(3):1136-1143
[118]叶华.大规模电力系统低频振荡分析与广域自适应控制研究[D].济南：山东大学,2009
[119]A. G. Phadke. Synchronized phasor measurements in power systems[J]. IEEE Computer Applications in Power,1993,6(2):10-15
[120]张鹏飞,薛禹胜,张启平,等.基于PMU实测摇摆曲线的暂态稳定量化分析[J].电力系统自动化,2004,28(20)：17-20
[121]宋方方,毕天妹,杨奇逊.基于广域测量系统的电力系统多摆稳定性评估方法[J].中国电机工程学报,2006,26(16)：38-45
[122]N. Kakimoto, S. G. Masahiro, T. Makino, et al. Monitoring of interarea oscillation mode by synchronized phasor measurement[J]. IEEE Transactions on Power Systems,2006,21(1): 260-268
[123]M. Qiu, W. Gao, M. Chen, et al. Energy efficient security algorithm for power grid wide area monitoring system[J]. IEEE Transactions on Smart Grid,2011,2(4):715-723
[124]李强,周京阳,于尔铿.基于混合量测的电力系统状态估计混合算法[J].电力系统自动化, 2005,29(19)：31-35
[125]贺仁睦.电网动态实时监控及管理系统的构想[J].电力系统自动化,2002,26(5)：1-4
[126]黄志刚,邬炜,韩英铎.基于同步相量测量的电压稳定评估算法[J].电力系统自动化,2002,26(2)：28-33
[127]赵金利,余贻鑫.基于本地相量测量的电压失稳指标工作条件分析[J].电力系统自动化,2006,30(24)：1-4
[128]彭疆南,孙元章,王海风.基于广域量测数据和导纳参数在线辨识的受扰轨迹预测[J].电力系统自动化,2003,27(22)：6-11
[129]宋方方,毕天妹,杨奇逊.基于WAMS的电力系统受扰轨迹预测[J].电力系统自动化,2006,30(23)：27-32
[130]宋晓娜,毕天妹,吴京涛,等.基于WAMS的电网扰动识别方法[J].电力系统自动化,2006,30(5)：24-28
[131]I. Kamwa, R. Grondin, Y. Hebert. Wide-area measurement based stabilizing control of large power systems a decentralized hierarchical approach[J]. IEEE Transactions on Power Systems, 2001,16(1):136-153
[132]谢小荣,肖晋宇,童陆园,等.采用广域测量信号的互联电网区间阻尼控制[J].电力系统自动化,2004,28(2)：37-40
[133]江全元,白碧蓉,邹振宇.计及广域测量系统时滞影响的TCSC控制器设计[J].电力系统自动化,2004,28(20)：21-25
[134]袁野,程林,孙元章.采用广域测量信号的2级PSS控制策略[J].电力系统自动化,2006,30(24)：11-16
[135]J. F. Hauer, C. J. Demeure, L. L. Scharf. Initial results in prony analysis of power system response signals[J]. IEEE Transactions on Power Systems,1990,5(1):80-89
[136]C. E. Grund, J. J. Paserba, J. F. Hauer, et al. Comparison of Prony and eigen analysis for power system control design[J]. IEEE Transactions on Power Systems,1993,8(3):964-971
[137]侯王宾,刘天琪,李兴源.基于自适应神经模糊滤波的低频振荡Prony分析[J].电网技术,2010,34(6)：53-58
[138]刘森,赵书强,于赞梅,等.基于小波预处理技术的低频振荡Prony分析[J].电力自动化设备,2007,27(4)：64-68
[139]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
[140]赵礼杰.基于EMD的Prony算法在低频振荡模态参数辨识中的应用[J]. 电力系统保护 与控制,2009,37(23)：9-15
[141]肖晋宇,谢小荣,胡志祥,等.电力系统低频振荡在线辨识的改进Prony算法[J].清华大学学报,2004,44(7)：883-887
[142]王铁强,贺仁睦,徐东杰,等.Prony算法分析低频振荡的有效性研究[J].中国电力,2001,34(11)：38-41.
[143]R. W. Wies, J. W. Pierre, D. J. Trudnowski. Use of ARMA block processing for estimating stationary low-frequency electromechanical modes of power systems[J]. IEEE Transactions on Power Systems,2003,18(1):167-182.
[144]N. Zhou, J. W. Pierre, D. J. Trudnowski, et al. Robust RLS methods for online estimation of power system electromechanical modes[J]. IEEE Transactions on Power Systems,2007,22(3): 1240-1249
[145]N. Zhou, D. J. Trudnowski, J. W. Pierre, et al. Electromechanical mode online estimation using regularized robust RLS methods[J]. IEEE Transactions on Power Systems,2008,23(4): 1670-1680
[146]J. J. Gasca, J. H. Chow. Computation of power system low-order models from time domain simulations using a Hankel matrix[J]. IEEE Transactions on Power Systems,1997,12(4): 1461-1467
[147]王宇静,于继来.电力系统振荡模态的矩阵束辨识法[J].中国电机工程学报,2007,27(19)：12-17
[148]G. Liu, J. Quintero, V. Venkatasubramanian. Oscillation monitoring system based on wide area synchrophasors in power systems[C]. Bulk Power System Dynamics and Control-VII. Revitalizing Operational Reliability,2007 iREP Symposium,2007
[149]J. J. Gasca, J. H. Chow. Performance comparison of three identification methods for the analysis of electromechanical oscillations[J]. IEEE Transactions on Power Systems,1999,14(3): 995-1002
[150]P. O'Shea. The use of sliding spectral windows for parameter estimation in power system disturbance monitoring[J]. IEEE Transactions on Power Systems,2000,15(4):1261-1267
[151]N. Huang, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Roral Society A,1998,45:903-995
[152]G. Rilling, P. Flandrin, P. Goncalves. On empirical mode decomposition and its algorithms[C]. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing,2003:27-33.
[153]A.R.Messina, V. Vittal. Nonlinear non-Stationary analysis of inter-area oscillations via Hilbert spectral analysis[J]. IEEE Transactions on Power Systems,2006,21(3):1234-1241
[154]A. R. Messina, V. Vittal, D. Ruiz-Vega. Interpretation and visualization of wide-area PMU measurements using Hilbert analysis[J]. IEEE Transactions on Power Systems,2006,21(4): 1763-1771
[155]N.E. Huang, M. Wu, S. Long, et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis[J]. Proceedings of the Roral Society A,2003,45(9):2317-2345
[156]N. Senroy, S. Suryanarayanan, P. F. Ribeiro. An improved Hilbert-Huang method for analysis of time-varying waveforms in power quality[J]. IEEE Transactions on Power Systems,2007, 22(4):1843-1850
[157]许宝杰,张建民.抑制EMD端点效应方法的研究[J].北京理工大学学报,2006,26(3)：196-200
[158]蔡艳平,李艾华,张玮.HHT端点效应的最大Lyapunov指数边界延拓方法[J].仪器仪表学报,2011,32(6)：1330-1336
[159]李天云,谢家安,张方彦,等.HHT在电力系统低频振荡模态参数提取中的应用[J].中国电机工程学报,2007,27(28)：79-83
[160]D. S. Laila, A. R. Messina, B. C. Pal. A refined Hilbert Huang transform with applications to inter-area oscillation monitoring[J]. IEEE Transactions on Power Systems,2009,24(2):610-620.
[161]李成鑫,刘俊勇,姚良忠,等.基于改进频移经验模态分解的低频振荡参数提取[J].电力系统自动化,2012,36(15)：8-13
[162]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
[163]吴超,陆超,韩英铎,等.Prony方法和ARMA法在低频振荡模式辨识中的适用性比较[J].电力自动化设备,2010,83(3)：30-34
[164]J. Xin, Z. Han, G. Wu, et al. Energy analysis of power system low frequency oscillation of resonance mechanism[C]. The Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies,2008:251-263
[165]G. Rilling, P. Flandrin. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Transaction on Signal Process,2008,56(1):85-95
[166]R. Deering, J. F. Kaiser. The use of masking signal to improve empirical mode decomposition[C]. IEEE International Conference Acoustics, Speech Signal Processing,2005:485-488
[167]N. Senroy, S. Suryanarayanan. Two techniques to enhance empirical mode decomposition for power quality applications[C]. IEEE PES General Meeting,2007:1-6
[168]何正友,钱清泉.电力系统暂态信号分析中小波基的选择原则[J].电力系统自动化,2003,27(10)：45-49
[169]杨淑英,王丽宏,杜荣华.电力系统暂态保护中小波基的选择与应用[J].电力系统及其自动化学,2008,20(5)：106-111
[170]L. Jose, R. Carlos, A. Juarez, et al. Wavelet-based analysis of power system low-frequency electromechanical oscillations[J]. IEEE Transactions on Power System,2011,26(3):1733-1744
[171]D. S. Laila, A. R. Messina, C. Bikash. A refined Hilbert-Huang transform with applications to inter-area oscillation monitoring[J]. Transactions on Power Systems,2009,24(2):610-620
[172]A. Prince, N. Senroy. Targeted approach to apply masking signal-based empirical mode decomposition for mode identification from dynamic power system wide area measurement signal data[J]. IET Generation Transmission & Distribution,2011,5(10):1025-1032
[173]彭谦,马晨光,杨雪梅,等.线性模态分析中的参与因子与贡献因子[J].电网技术,2010,34(2)：92-97
[174]韩志勇,贺仁睦,徐衍会,等.基于能量角度的共振机理电力系统低频振荡分析[J].电网技术,2007,31(8)：13-16
[175]董清,张玲,颜湘武.低频振荡扰动源机组的自动定位方法[J].电网技术,2012,36(10)：265-270
[176]余一平,闵勇,陈磊.多机电力系统强迫功率振荡稳态响应特性分析[J].电力系统自动化,2009,33(22)：5-10
[177]余一平,闵勇,陈磊,等.基于能量函数的强迫功率振荡扰动源定位[J].电力系统自动化,2010,34(5)：1-6