Small Signal Monitoring of Power System using Subspace System Identification
Keywords:small signal, subspace identification, power system, monitoring
In this paper, small signal analysis of power systems is investigated using Subspace System Identification (SSI) methods. Classical small signal analysis methods for power systems are based on mathematical modeling and linearized model of power system in an especial operating point. There are some difficulties when such a classical method is applied, specially, in the case of large power systems. In this paper, such difficulties and their bases are investigated and in order to avoid them, it is suggested to use SSI algorithms for small signal analysis of power systems. The paper discusses extracting of small signal properties of power systems and presents some new suggestions for application of subspace system identification methods. Different types of subspace system identification algorithms were applied to different power system case studies using the presented propositions. The benefits and drawbacks of subspace system identification methods and the presented suggestions are studied for small signal analysis of power systems and power system monitoring. Several comparisons were investigated using computer simulations. The results express the usefulness and easiness of proposed methods.
P. van Overschee and B. de Moor, A unifying theorem for three subspace system identification lgorithms, Automatica, vol. 31, pp. 1853-1864, 1995 http://dx.doi.org/10.1016/0005-1098(95)00072-0
T. Katayama, Subspace Methods for System Identification: Springer, 2005 http://dx.doi.org/10.1007/1-84628-158-X
S. J. Qin, An Overview of Subspace Identification, Elsevier, Computer & Chemical Engineering, ol. 30, pp. 1502-1513, 2006
] P. Van Overschee and B. De Moor, "N4SID: Subspace algorithms for the identification of ombined deterministic-stochastic systems, Automatica, vol. 30, pp. 75-93, 1994 http://dx.doi.org/10.1016/0005-1098(94)90230-5
M. Verhaegen and P. Dewilde, Subspace model identification Part 1. The output-error statespace odel identification class of algorithms, International Journal of Control, vol. 56, pp. 187-1210, 1992/10/01 1992
M. Verhaegen and P. Dewilde, Subspace model identification Part 2. Analysis of the elementary utput-error state-space model identification algorithm, International Journal of ontrol, vol. 56, pp. 1211-1241, 1992/10/01 1992
M. Viberg, Subspace-based methods for the identification of linear time-invariant systems, utomatica, vol. 31, pp. 1835-1851, 1995
M. Verhaegen, Identification of the deterministic part of MIMO state space models given in nnovations form from input-output data, Automatica, vol. 30, pp. 61-74, 1994 http://dx.doi.org/10.1016/0005-1098(94)90229-1
W. E. Larimore, "Canonical variate analysis in identification, filtering, and adaptive control, n Decision and Control, Proceedings of the 29th IEEE Conference on, 1990, pp. 596-604
S. J. Qin, An overview of subspace identification, Computers & Chemical Engineering, vol. 0, pp. 1502-1513, 2006 http://dx.doi.org/10.1016/j.compchemeng.2006.05.045
G. T. I. Kamwa, L. Gerin-Lajoe, Low Order Black-Box Models for Control System Design arge Power Systems, IEEE Transaction on Power Systems, Vol. 11, No. 1, February 1996 http://dx.doi.org/10.1109/59.486110
S. M. I. H. Akcay, B. Ninness, Subspace-Based Identification of Power Transformer Models rom Frequency Responce Data, IEEE Transaction on Instrumentation and Measurement, ol. 48, No. 3, June 1999
H. A. T. McKelvey, Lennart Ljung, Subspace-Based Multivariable System Identification rom Frequency Responce Data, IEEE Transaction on Automatic Control, Vol. 41, No. 7, uly 1996
S. M. I. H. Akcay, B. Ninness, Identification of Power Transformer Models from Frequency esponce data: A Case Study. Elsevier, Signal Processing, 68, pp 307-315, 1998 http://dx.doi.org/10.1016/S0165-1684(98)00080-2
O. P. M. M. Karrari, Identification of Heffron-Phillips Model Parameters for Synchronous emerators Using Online Measurment, IEEE Proceeding of Generator, Transmition, Distribution, ol. 151, No. 3, May 2004.
D. W. O. P. M. M. Soliman, Identification of Heffron-Phillips Model Parameters for Synchronous enerators Operating in Closed Loop, IEEE Generation, Transmition and Distribution, (4):530-541, 2008
O. P. M. Bin Wu. Multivariable Adaptive Control of Synchronous Machines in a Multimachine ower System. IEEE Transaction on Power Systems, Vol. 21, No. 4, November 2006
J. W. P. Ning Zhou, J. F. Hauer, Initial Results in Power System identification From Injected robing Signals Using a Subspace Method, IEEE Transaction on Power Systems, Vol. 21, o. 3, August 2006
C. C. H. Ghasemi, A. Moshref, Oscillatory Stability Limit Prediction Using Stochastic ubspace Identification, IEEE Transaction on Power Systems, 21(2):736-745, 2006 http://dx.doi.org/10.1109/TPWRS.2006.873100
L. G.-L. I. Kamwa, G. Trudel, Multi-Loop Power System Stablizers Using Wide-Area Synchronous hasor Measurements, Proceedings of the American Control Conference, Philadelphia, ennsylvania, June 1998
D. Y. G Cai, Y. Jiao, Ch. Shao, Power System Oscillation Mode Analysis and Parameter etermination of PSS Based on stochastic Subspace Identification, Asia-Pacific Power and nergy Engineering Conference (APPEEC), pp. 1-6, March 2009
P. Overschee and B. L. R. Moor, Subspace identification for linear systems: theory, implementation, pplications: Kluwer Academic Publishers, 1996 http://dx.doi.org/10.1007/978-1-4613-0465-4
PP. Kundur and N. J. Balu, Power System Stability and Control: IEEE, 1998
M. A. H.Wael A. Hashlamoun, and Eyad H. Abed, New Results on Modal Participation Factors: evealing a Previously Unknown Dichotomy. IEEE Transactions of Automatic Control, ol. 54, No. 7, July 2009
J. J. Sanchez-Gasca, V. Vittal, M. J. Gibbard, A. R. Messina, D. J. Vowles, S. Liu, and U. . Annakkage, Inclusion of higher order terms for small-signal (modal) analysis: committee eport-task force on assessing the need to include higher order terms for small-signal (modal) nalysis, Power Systems, IEEE Transactions on, 20(4):1886 - 1904, 2005
R. Klein, Moorty and Kundur. Analytical investigation of factors influencing PSS performance, EEE Trans. on EC, 7(3):382-390, 1992
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