Power system transient stability simulation under uncertainty based on Taylor model arithmetic

Shouxiang WANG, Zhijie ZHENG, Chengshan WANG

PDF(188 KB)
PDF(188 KB)
Front. Electr. Electron. Eng. ›› 2009, Vol. 4 ›› Issue (2) : 220-226. DOI: 10.1007/s11460-009-0039-5
RESEARCH ARTICLE
RESEARCH ARTICLE

Power system transient stability simulation under uncertainty based on Taylor model arithmetic

Author information +
History +

Abstract

The Taylor model arithmetic is introduced to deal with uncertainty. The uncertainty of model parameters is described by Taylor models and each variable in functions is replaced with the Taylor model (TM). Thus, time domain simulation under uncertainty is transformed to the integration of TM-based differential equations. In this paper, the Taylor series method is employed to compute differential equations; moreover, power system time domain simulation under uncertainty based on Taylor model method is presented. This method allows a rigorous estimation of the influence of either form of uncertainty and only needs one simulation. It is computationally fast compared with the Monte Carlo method, which is another technique for uncertainty analysis. The proposed method has been tested on the 39-bus New England system. The test results illustrate the effectiveness and practical value of the approach by comparing with the results of Monte Carlo simulation and traditional time domain simulation.

Keywords

interval arithmetic / power systems / Taylor series expansion / Taylor model / time domain simulation / transient stability / uncertainty

Cite this article

Download citation ▾
Shouxiang WANG, Zhijie ZHENG, Chengshan WANG. Power system transient stability simulation under uncertainty based on Taylor model arithmetic. Front Elect Electr Eng Chin, 2009, 4(2): 220‒226 https://doi.org/10.1007/s11460-009-0039-5

References

[1]
Kosterev D N, Tylor C W, Mittelstadt W A. Model validation for the August 10, 1996 WSCC system outage. IEEE Transactions on Power Systems, 1999, 14(3): 967–979
CrossRef Google scholar
[2]
Dopazo J F, Klitin O A, Sasson A M. Stochastic load flows. IEEE Transactions on Power Apparatus and Systems, 1975, 94(2): 299–309
CrossRef Google scholar
[3]
Allan R N, Billinton R, Breipohl A M, Grigg C H. Bibliography on the application of probability methods in power system reliability evaluation 1992-1996. IEEE Transactions on Power Systems, 1999, 14(1): 51–57
CrossRef Google scholar
[4]
Timko K J, Bose A, Anderson P M. Monte Carlo simulation of power system stability. IEEE Transactions on Power Apparatus and Systems, 1983, PAS-102(10): 3453–3459
CrossRef Google scholar
[5]
Anderson P M, Bose A. A probabilistic approach to power system stability analysis. IEEE Transactions on Power Apparatus and Systems, 1983, PAS-102(8): 2430–2439
CrossRef Google scholar
[6]
Wu F F, Tsai Y K. Probabilistic dynamic security assessment of power systems I: basic model. IEEE Transactions on Circuits and Systems, 1983, 30(3): 148–159
CrossRef Google scholar
[7]
Hiskens I A, Pai M A, Nguyen T B. Bounding uncertainty in power system dynamic simulations. In: Proceedings of IEEE Power Engineering Society Winter Meeting, Singapore. 2000, 2: 1533–1537
[8]
Hockenberry J R, Lesieutre B C. Evaluation of uncertainty in dynamic simulations of power system models: the probabilistic collocation method. IEEE Transactions on Power Systems, 2004, 19(3): 1483–1491
CrossRef Google scholar
[9]
Moore R E. Interval arithmetic and automatic error analysis in digital computing. Dissertation for the Doctoral Degree. Stanford: Stanford University, 1962
[10]
Alefeld G, Mayer G. Interval analysis: theory and applications. Journal of Computational and Applied Mathematics, 2000, 121(1-2): 421–464
CrossRef Google scholar
[11]
Hickey T, Ju Q, Van Emden M H. Interval arithmetic: from principles to implementation. Journal of the ACM, 2001, 48(5): 1038–1068
CrossRef Google scholar
[12]
Wang Z, Alvarado F L. Interval arithmetic in power flow analysis. IEEE Transactions on Power Systems, 1992, 7(3): 1341–1349
CrossRef Google scholar
[13]
Das B. Radial distribution system power flow using interval arithmetic. International Journal of Electrical Power & Energy Systems, 2002, 24(10): 827–836
CrossRef Google scholar
[14]
Sarić A T, Stanković A M. An application of interval analysis and optimization to electric energy markets. IEEE Transactions on Power Systems, 2006, 21(2): 515–523
CrossRef Google scholar
[15]
Chaturvedi A, Prasad K, Ranjan R. Use of interval arithmetic to incorporate the uncertainty of load demand for radial distribution system analysis. IEEE Transactions on Power Delivery, 2006, 21(2): 1019–1021
CrossRef Google scholar
[16]
Berz M, Hoffstatter G. Computation and application of Taylor polynomials with interval remainder bounds. Reliable Computing, 1998, 4(1): 83–97
CrossRef Google scholar
[17]
Makino K. Rigorous Analysis of nonlinear motion in particle accelerators. Dissertation for the Doctoral Degree. East Lansing: Michigan State University, 1998
[18]
Neumaier A. Taylor forms–use and limits. Reliable Computing, 2003, 9(1): 43–79
CrossRef Google scholar
[19]
Revol N, Makino K, Berz M. Taylor models and floating-point arithmetic: proof that arithmetic operations are validated in COSY. Journal of Logic and Algebraic Programming, 2005, 64(1): 135–154
CrossRef Google scholar
[20]
Makino K, Berz M. Taylor models and other validated functional inclusion methods. International Journal of Pure and Applied Mathematics, 2003, 4(4): 379–456
[21]
Barrio R. Performance of the Taylor series method for ODEs/DAEs. Applied Mathematics and Computation, 2005, 163(2): 525–545
CrossRef Google scholar
[22]
Pai M A. Energy Function Analysis for Power System Stability. Norwell, MA: Kluwer Academic Publishers, 1989

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 50477035).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
PDF(188 KB)

Accesses

Citations

Detail

Sections
Recommended

/