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Power system state estimation using dispersed particle filter

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Języki publikacji
EN
Abstrakty
EN
The paper presents a new approach to particle filtering, i.e. Dispersed Particle Filter. This algorithm has been applied to the power system, but it can also be used in other transmission networks. In this approach, the whole network must be divided into smaller parts. As it has been shown, use of Dispersed Particle Filter improves the quality of the state estimation, compared to a simple particle filter. It has been also checked that communication between subsystems improves the obtained results. It has been checked by means of simulation based on model, which has been created on the basis of knowledge about practically functioning power systems.
Twórcy
autor
  • Poznan University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3a, 60-965 Poznan, Poland
autor
  • Poznan University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3a, 60-965 Poznan, Poland
autor
  • Poznan University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3a, 60-965 Poznan, Poland
Bibliografia
  • [1] Abur A., Exposito A.G., “Power System State Estimation: Theory and Implementation”, Marcel Dekker, Inc., 2004, pp. 17–49. DOI:10.1201/9780203913673.
  • [2] 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, vol. 50, no. 2,2002, pp. 174–188.
  • [3] Candy J.V., “Bayesian Signal Processing”,WILEY, New Jersey, 2009, pp. 36–44. DOI:10.1002/9780470430583.
  • [4] Chen H., Liu X., She C., Yao C., “Power System Dynamic State Estimation Based on a New Particle Filter”, Procedia Environmental Sciences, vol. 11,Part B, 2011, pp. 655–661.
  • [5] Djuric P. M., Lu T., Bugallo M. F., “Multiple particle fiiltering”, In: 32nd IEEE ICASSP, April 2007, III pp. 1181–1184. DOI:10.1109/ICASSP.2007.367053.
  • [6] Douc R., Cappe O., Moulines E., “Comparison of Resampling Schemes for Particle Filtering”, In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, September 2005, pp. 64–69. DOI:10.1109/ISPA.2005.195385.
  • [7] Doucet A., Freitas N., Gordon N., “Sequential Monte Carlo Methods in Practice”, Springer-Verlag, New York, pp. 225–246 (2001). DOI:10.1007/978-1-4757-3437-9.
  • [8] Doucet A., Freitas N., Murphy K., Russell S., “Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks”, In: Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence, 2000, pp. 176–183.
  • [9] Gordon N.J., Salmond D.J., Smith A.F.M., “Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation”, IEE Proceedings-F, vol. 140,no. 2, 1993, pp. 107–113. DOI: 10.1049/ip-f-2.1993.0015.
  • [10] Horowitz S., Phadke A., Renz B., “The Future of Power Transmission”, IEEE Power and Energy Magazine, vol. 8, no. 2, 2010, pp. 34–40. DOI:10.1109/MPE.2009.935554.
  • [11] Huang Z., Schneider K., Nieplocha J., “Feasibility Studies of Applying Kalman Filter Techniques to Power System Dynamic State Estimation”, In:Power Engineering Conference, IPEC 2007, December 2007, pp. 376–382.
  • 39[12] Imtiaz S.A., Roy K., Huang B., Shah S.L., Jampana P., “Estimation of States of Nonlinear Systems using a Particle Filter”, In: IEEE International Conference on Industrial Technology, ICIT 2006, December 2006, pp. 2432–2437. DOI:10.1109/ICIT.2006.372687.
  • [13] Kotecha J.H., Djuri? P.M., “Gaussian Particle Filtering”, IEEE Trans Signal Processing,vol. 51, no. 10, 2003, pp. 2592–2601. DOI:10.1109/TSP.2003.816758.
  • [14] Kozierski P., Lis M., “Auxiliary and Rao-Blackwellised Particle Filters Comparison”, Poznan University of Technology Academic Journals:Electrical Engineering, Issue 76, 2013, pp. 79–88.
  • [15] Kozierski P., Lis M., “Filtr Czasteczkowy w Problemie Sledzenia – Wprowadzenie”, Studia z Automatyki i Informatyki, vol. 37, 2012, pp. 79–94.
  • [16] Kozierski P., Lis M., Krolikowski A., Gulczynski A.,“Resampling – Essence of Particle Filter”, CREATIVETIME,Krakow, vol. 1, 2013, pp. 174–185.
  • [17] Kozierski P., Lis M., Zietkiewicz A., “Resampling in Particle Filtering – Comparison”, Studia z Automatyki i Informatyki, vol. 38, 2013, pp. 35–64.
  • [18] Kremens Z., Sobierajski M., “Analiza Systemow Elektroenergetycznych”, Wydawnictwa Naukowo-Techniczne, Warszawa, 1996,pp. 39–191.
  • [19] Murray L., Lee A., Jacob P., “Rethinking Resampling in the Particle Filter on Graphics Processing Units”, arXiv preprint, arXiv:1301.4019, 2013.
  • [20] Pitt M., Shephard N., “Filtering via Simulation: Auxiliary Particle Filters”, Journal of the American Statistical association,vol. 94, no. 446, 1999, pp. 590–599. DOI:10.1080/01621459.1999.10474153.
  • [21] Schweppe F.C., Rom D.B., “Power System Static-State Estimation, Part II: Approximate Model”, IEEE Transactions on Power Apparatus and Systems, vol. 89, no. 1, January 1970, pp. 125–130. DOI: 10.1109/TPAS.1970.292679.
  • [22] Simon D., “Optimal State Estimation”, WILEY–INTERSCIENCE, New Jersey, 2006, pp. 461–484. DOI: 10.1002/0470045345.
  • [23] Singh R., Pal B.C., Jabr R.A., “Choice of Estimator for Distribution System State Estimation”, IET Generation, Transmission & Distribution, vol. 3, Iss. 7, 2009, pp. 666–678. DOI: 10.1049/ietgtd.2008.0485.
  • [24] Valverde G., Terzija V., “Unscented Kalman Filter for Power System Dynamic State Estimation”, IET Generation, Transmission & Distribution, vol. 5, Iss. 1, 2011, pp. 29–37. DOI:10.1049/iet-gtd.2010.0210.
  • [25] Wood, A.J., Wollenberg B., “Power Generation, Operation and Control”, John Wiley & Sons Inc., 1996, pp. 91–130, 453–513.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-23403e27-2fe0-4697-a864-c6b95101761e
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