Particle filter in power system state estimation – bad measurement data and branch disconnection

• Autorzy: Kozierski P. , Lis M. , Owczarkowski A. , Horla D.
• Języki publikacji: EN

Abstrakty

EN

An approach to power system state estimation using a particle filter has been proposed in the paper. Two problems have been taken into account during research, namely bad measurements data and a network structure modification with rapid changes of the state variables. For each case the modification of the algorithm has been proposed. It has also been observed that anti-zero bias modification has a very positive influence on the obtained results (few orders of magnitude, in comparison to the standard particle filter), and additional calculations are quite symbolic. In the second problem, used modification also improved estimation quality of the state variables. The obtained results have been compared to the extended Kalman filter method.

Słowa kluczowe

EN

bad data; branch disconnection measurement errors; particle filter; power system; state estimation

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2015
• Tom: Vol. 64, nr 2
• Strony: 237--248
• Opis fizyczny: Bibliogr. 30 poz., wykr., wz.

Twórcy

autor

Kozierski P.

• Poznan University of Technology Faculty of Electrical Engineering, Institute of Control and Information Engineering Piotrowo 3a, 60-965 Poznań, Poland

autor

Lis M.

• Poznan University of Technology Faculty of Electrical Engineering, Institute of Electrical Engineering and Electronics Piotrowo 3a, 60-965 Poznań, Poland

autor

Owczarkowski A.

• Poznan University of Technology Faculty of Electrical Engineering, Institute of Control and Information Engineering Piotrowo 3a, 60-965 Poznań, Poland

autor

Horla D.

• Poznan University of Technology Faculty of Electrical Engineering, Institute of Control and Information Engineering Piotrowo 3a, 60-965 Poznań, Poland

Bibliografia

• [1] Kundur P., Power System Stability and Control. McGraw-Hill Company, New York (1994).
• [2] Wang H., On the Computation and Application of Multi-Period Security-Constrained Optimal Power Flow for Real-Time Electricity Market Operations. PhD Thesis, Cornell University (2007).
• [3] Weber J. D., Implementation of a Newton-Based Optimal Power Flow into a Power System Simulation Environment. PhD Thesis, University of Illinois (2007).
• [4] Schweppe F.C., Rom D.B., Power System Static-State Estimation, Part II: Approximate Model. IEEE Transactions on Power Apparatus and Systems 89(1): 125-130 (1970).
• [5] Chen J., Liao Y., State Estimation and Power Flow Analysis of Power Systems. Journal of Computers 7(3): 685-691 (2012).
• [6] Huang Z., Schneider K., Nieplocha J., Feasibility Studies of Applying Kalman Filter Techniques to Power System Dynamic State Estimation. Proc. In Power Engineering Conference, IPEC 2007, pp. 376-382 (2007).
• [7] Valverde G., Terzija V., Unscented Kalman Filter for Power System Dynamic State Estimation. IET Generation, Transmission & Distribution 5(1): 29-37 (2011).
• [8] Kozierski P., Lis M., Ziętkiewicz J., Particle Filter in State Vector Estimation Problem for Power System. Pomiary Automatyka Robotyka 18(1): 71-76 (2014).
• [9] Kozierski P., Lis M., Horla D., Power System State Estimation using Dispersed Particle Filter. Journal of Automation, Mobile Robotics & Intelligent Systems 8(3): 35-40 (2014). DOI: 10.14313/ JAMRIS_3-2014/25.
• [10] Kozierski P., Lis M., Owczarkowski A., Horla D., Dispersed Filters for Power System State Estimation. In Methods and Models in Automation and Robotics (MMAR). 19th International Conference On, IEEE, pp. 129-133 (2014).
• [11] Brzozowska-Rup K., Dawidowicz A.L., Particle Filter Method (in Polish), Matematyka Stosowana: matematyka dla społeczeństwa 10/51: 69-107 (2009).
• [12] Thrun S., Burgard W., Fox D., Probabilistic robotics. MIT Press, Cambridge, pp. 67-90 (2005).
• [13] Simon D., Optimal State Estimation. WILEY-INTERSCIENCE, New Jersey, pp. 461-484 (2006).
• [14] Kozierski P., Lis M., Particle Filter in Tracking Problem - Introduction (in Polish). Studia z Automatyki i Informatyki 37: 79-94 (2012).
• [15] Doucet A., Godsill S., Andrieu C., On sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing 10: 197-208 (2000).
• [16] Gordon N.J., Salmond D.J., Smith A.F.M., Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F 140(2): 107-113 (1993).
• [17] Pitt M., Shephard N., Filtering via Simulation: Auxiliary Particle Filters. Journal of the American Statistical association 94(446): 590-599 (1999).
• [18] Klaas M., De Freitas N., Doucet A., Toward Practical N2 Monte Carlo: The Marginal Particle Filter. http://arxiv.org/ftp/arxiv/papers/1207/1207.1396.pdf, accessed July (2014).
• [19] Kotecha J.H., Djurić P.M., Gaussian Particle Filtering. IEEE Trans Signal Processing 51(10): 2592-2601 (2003).
• [20] Konatowski S., Kaniewski P., Non-linear filtering algorithms used in positioning systems (in Polish). Elektronika 12: 100-103 (2012).
• [21] Merwe R., Doucet A., Freitas N., Wan E., The Unscented Particle Filter, Technical Report CUED/F-INFENG/TR 380. Cambridge University Engineering Department (2000).
• [22] Wang F., Lin Y., Zhang T., Liu J., Particle Filter with Hybrid Proposal Distribution for Nonlinear State Estimation. Journal of Computers 6(11): 2491-2501 (2011).
• [23] Liang-Qun L., Hong-Bing J., Jun-Hui L., The Iterated Extended Kalman Particle Filter. Proc. Int. Conf. Communications and Information Technology, ISCIT 2005, 2: 1213-1216 (2005).
• [24] Kozierski P., Lis M., Ziętkiewicz J., Resampling in Particle Filtering - Comparison. Studia z Automatyki i Informatyki 38: 35-64 (2013).
• [25] Murray L., GPU Acceleration of the Particle Filter: the Metropolis Resampler. http://arxiv.org/pdf/1202.6163v1.pdf, accessed July (2014).
• [26] Mountney J., Obeid I., Silage D., Modular Particle Filtering FPGA Hardware Architecture for Brain Machine Interfaces. Proc. Int. Conf. IEEE Eng Med Biol Soc., pp. 4617-4620 (2011).
• [27] Arulampalam S., Maskell S., Gordon N., Clapp T., A Tutorial on Particle Filters for On-line Nonlinear/ Non-Gaussian Bayesian Tracking. IEEE Proceedings on Signal Processing 50(2): 174-188 (2002).
• [28] Wood A.J., Wollenberg B., Power Generation, Operation and Control. John Wiley & Sons Inc., pp. 91-130 (1996).
• [29] Kremens Z., Sobierajski M., The Analysis of Power Systems (in Polish). Wydawnictwa Naukowo- Techniczne, Warsaw, pp. 39-191 (1996).
• [30] Abur A., Exposito A.G., Power System State Estimation: Theory and Implementation. Marcel Dekker, Inc., pp. 17-49 (2004).