An Iterative Approach for Selecting Poles on Complex Frequency Localizing Basis Function-Based Models
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Abstract
Modeling dynamic systems from frequency-domain data is a relevant issue for system analysis in many fields of engineering. Many techniques to approach such problems are based on models built by using a series of rational functions with fixed structure. As far as rational basis function models are concerned, the problem of selecting the function’s poles or dynamics is an issue and the Sanathanan–Koerner (SK) iterations represent a successful approach that can be applied to solve it. Frequency localizing basis function (FLBF) models are a rational function which can be used in such a context. However FLBF, as originally proposed, are not suitable for application with SK-iterations because they have not been designed to incorporate complex poles. To overcome this issue and to make the application of SK-iterations possible, this paper proposes a new set of basis functions that are based on the FLBF. In this paper, they will be referred to as complex frequency localizing basis functions. This new set of basis functions preserves the FLBF behavior but real and complex poles can be incorporated in the model and so it allows the use of SK-iterations. The proposed model is validated through simulation.
Keywords
Linear systems System identification Rational basis functions models Frequency-domain system identificationNotes
Acknowledgments
The first two authors would like to acknowledge CNPq and Fundação Araucária, Brazil, for their valuable support.
References
- Aguero, J. C., Yuz, J. I., Goodwin, G. C., & Delgado, R. A. (2010). On the equivalence of time and frequency domain maximum likelihood estimation. Automatica, 46(2), 260–270.MathSciNetCrossRefGoogle Scholar
- Ahna, H., Leeb, S., & Leec, S. (2003). Frequency domain control-relevant identification of MIMO AMB rigid rotor. Automatica, 39(2), 299–307.MathSciNetCrossRefGoogle Scholar
- Akay, H., & Ninness, B. (1999). Orthonormal basis functions for modelling continuous-time systems. Signal Processing, 77, 261–274.CrossRefGoogle Scholar
- Blom, R. S., & Van den Hof, P. M. J. (2010). Multivariable frequency domain identification using IV-based linear regression. In 49th IEEE conference on decision and control.Google Scholar
- Cigre WG A2/C4.39. (2014). Electrical transient interaction between transformer and the power system, part 1 expertise and part 2 case studies.Google Scholar
- Deschrijver, D., & Dhaene, T. (2006). Parametric identification of frequency domain systems using orthonormal rational bases. In IFAC symposium on system identification (pp. 837–842).Google Scholar
- Deschrijver, D., Gustavsen, B., & Dhaene, T. (2007a). Advancements in iterative methods for rational approximation in the frequency domain. IEEE Transactions on Power Delivery, 22(3), 1633–1642.Google Scholar
- Deschrijver, D., Haegeman, B., & Dhaene, T. (2007b). Orthonormal vector fitting: A robust macromodeling tool for rational approximation of frequency domain responses. IEEE Transactions on Advanced Packaging, 30(2), 216–225.Google Scholar
- Gao, S., Li, Y., & Zhang, M. (2010). An Efficient algebraic method for the passivity enforcement of macromodels. IEEE Transactions on Microwave Theory and Techniques, 58, 1830–1839.CrossRefGoogle Scholar
- Garnier, H., Wang, L., & Young, P. (2008). Direct identification of continuous-time models from sampled data. Berlin: Springer.CrossRefGoogle Scholar
- Gustavsen, B. (2004). Wide band modeling of power transformers. IEEE Transactions on Power Delivery, 19, 414–422.CrossRefGoogle Scholar
- Heuberger, P. S. C., Van den Hof, P. M. J., & Wahlberg, B. (2005). Modelling and identification with rational orthogonal basis functions. Berlin: Springer.CrossRefGoogle Scholar
- IEEE Std C57.149. (2012). Guide for the application and interpretation of frequency response analysis for oil immersed transformers. New York, NY: IEEE Power and Energy Society.Google Scholar
- Ljung, L. (1994). Some results on identifying linear systems using frequency domain. IEEE Transactions on Automatic Control, 39, 2245–2260.CrossRefGoogle Scholar
- Ljung, L. (1999). System identification: Theory for the user (2nd ed.). Englewood Cliffs, NJ: Prentice Hall Inc.Google Scholar
- Mi, W., Qian, T., & Wan, F. (2012). A fast adaptive model reduction method based on Takenaka–Malmquist systems. Systems & Control Letters, 61(1), 223230.MathSciNetCrossRefGoogle Scholar
- Oliveira, G. H. C., & Mitchell, S. D. (2013). Comparison of black-box modeling approaches for transient analysis: A GIS case study. In Proceeding of the international conference on power system transients.Google Scholar
- Oliveira, G. H. C., Maestrelli, R., & Rocha, A. C. O. (2009). An application of orthonormal basis functions in power transformers wide band modeling. In IEEE international conference on control and automation (pp. 831–836).Google Scholar
- Perez, T., & Fossen, T. (2011). Practical aspects of frequency-domain identification of dynamic models of marine structures from hydrodynamic data. Ocean Engineering, 38(2–3), 426–436.CrossRefGoogle Scholar
- Pintelon, R., & Schoukens, J. (2012). System identification a frequency domain approach (2nd ed.). New York: IEEE Press.CrossRefGoogle Scholar
- Pordanjani, I. R., Chung, C. Y., Mazin, H. E., & Xu, W. (2011). A method to construct equivalent circuit model from frequency responses with guaranteed passivity. IEEE Transactions on Power Delivery, 26(1), 400–409.CrossRefGoogle Scholar
- Reginato, B. C., & Oliveira, G. H. C. (2007). On selecting the MIMO generalized orthonormal basis functions poles by using particle swarm optimization. In Proceedings of the European control conference.Google Scholar
- Sanathanan, C. K., & Koerner, J. (1963). Transfer function synthesis as a ratio of two complex polynomials. IEEE Transactions on Automatic Control, 9(1), 56–58.CrossRefGoogle Scholar
- Saraswat, D., Achar, R., & Nakhla, M. S. (2004). A fast algorithm and practical considerations for passive macromodeling of measured/simulated data. IEEE Transactions on Advanced Packaging, 27(1), 57–70.CrossRefGoogle Scholar
- Silva, T. O. (2000). Optimal pole conditions for Laguerre and two-parameters Kautz models: A survey of known results. In Proceedings of the 12th IFAC SYSID (Vol. 2, pp. 457–462).Google Scholar
- Tufa, L. D., Ramasamy, M., & Shuhaimi, M. (2011). Improved method for development of parsimonious orthonormal basis filter models. Journal of Process Control, 21(1), 3645.CrossRefGoogle Scholar
- Ubolli, A., & Grivet-Talocia, S. (2007). Weighting strategies for passivity enforcement schemes. In 16th IEEE topical meeting electrical performance electronic package (pp. 55–58).Google Scholar
- Van den Bosch, P. P. J., & Van der Klauw, A. C. (1994). Modeling, identification and simulation of dynamical systems. Boca Raton, FL: CRC Press.Google Scholar
- Welsh, J. S., & Goodwin, G. C. (2003). Frequency localising basis functions for wide-band identification. In European control conference.Google Scholar
- Welsh, J. S., & Rojas, C. R. (2007). Frequency localising basis functions for wide-band system identification: A condition number bound for output error systems. In European control conference.Google Scholar
- Welsh, J. S., Rojas, C. R., & Mitchell, S. D. (2007). Wideband parametric identification of a power transformer. In Australasian universities power engineering conference (pp. 1–6).Google Scholar