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Kalman-Filter Based Static Load Modeling of Real Power System Using K-EMS Data 원문보기

Journal of electrical engineering & technology, v.7 no.3, 2012년, pp.304 - 311  

Lee, Soo-Hyoung (School of Electrical and Electronic Eng., Yonsei University) ,  Son, Seo-Eun (School of Electrical and Electronic Eng., Yonsei University) ,  Lee, Sung-Moo (System Operation & Control Department, Korea Power Exchange) ,  Cho, Jong-Man (System Operation & Control Department, Korea Power Exchange) ,  Song, Kyung-Bin (Department of Electrical Engineering, Soongsil University) ,  Park, Jung-Wook (School of Electrical and Electronic Engineering, Yonsei University)

Abstract ▼ AI-Helper 아이콘AI-Helper

So far, the importance for an accurate load model has been constantly raised and its necessity would be further more emphasized. Currently used load model for analysis of power system in Korea was developed 10 years ago, which is aggregated by applying the statistically estimated load compositions t...

주제어

#Kalman-filter   #K-EMS   #Measurement base   #Parameter estimation   #ZIP model  

AI 본문요약
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제안 방법

  • In addition, the proposed ZIP modeling algorithm based on the Kalman-filter algorithm removes the noise of the independent load variation. Therefore, the proposed algorithm accurately composes the load model. Of course, the load model describes the load characteristics more accurately if there is high sampling measuring devices.
  • 6, the proposed load modeling algorithm has excellent noise rejection capability. Therefore, the proposed algorithm can improve modeling accuracy by removing measurement error and noise of load variation.

이론/모형

  • In the same manner, the reactive power characteristic of the load is modeled as (11) by applying the Kalman-filter algorithm to the measurement z in (10).
  • To determine the parameters, the data are measured on every substation in Korea at interval of four seconds by the Korean Energy Management System (K-EMS). The parameters are estimated without iterations by a Kalman-filter based load modeling algorithm.
  • Therefore, an estimation algorithm which requires iterative computation, such as the gradient decent method, the Newton method, and the genetic algorithm, is not suitable considering the characteristics of the K-EMS data and the ZIP model [13]. This study develops a non-iterative parameter estimation algorithm based on the Kalman-filter algorithm.
  • In this study, the static load model is chosen because of the realistic constraint that a high performance measuring device, such as digital fault recorder (DFR), has not been installed on every bus. To determine the parameters, the data are measured on every substation in Korea at interval of four seconds by the Korean Energy Management System (K-EMS). The parameters are estimated without iterations by a Kalman-filter based load modeling algorithm.
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참고문헌 (15)

  1. C. S. Chen, T. -H. Wu, C. -C. Lee, and Y. -M. TzengJoseph, "The Application of Load Models of Electric Appliances to Distribution System Analysis," IEEE Trans. on Power Systems, vol. 10, No. 3, pp. 1376-1382, August 1995. 

    상세보기 crossref

  2. W. W. Price, K. A. Wirgau, A, Murdoch, J. V. Mitsche, E. Vaahedi, and M. El-Kady, "Load Modeling for Power Flow and Transient Stability Computer Studies," IEEE Trans. on Power Systems, vol. 3, No. 1, pp. 180-187, February 1988. 

    상세보기 crossref

  3. J. Kim, K. -B. Shim, and J. -H. Kim, "Load Modeling of Electric Locomotive Using Parameter Identification," Journal of Electrical Engineering & Technology, Vol. 2. No. 2, pp. 145-151, June 2007. 

    원문보기 상세보기 crossref

  4. B. K. Choi, H. -D. Chiang, Y. Li, Y. -T. Chen, D. -H. Huang, and M. G. Lauby, "Development of Composite Load Models of Power Systems Using On-Line Measurement Data," Journal of Electrical Engineering & Technology, Vol. 1, No. 2, pp. 161- 169, June 2006. 

    원문보기 상세보기 crossref

  5. J. Ma, D. Han, R. -M. He, Z. -Y. Dong, and D. J. Hill, "Reducing Identified Parameters of Measurement- Based Composite Load Model," IEEE Trans. on Power Systems, Vol. 23, No. 1, pp. 76-83, February 2008. 

    상세보기 crossref

  6. I. A. Hiskens, "Nonlinear Dynamic Model Evaluation from Disturbance Measurements," IEEE Trans. on Power Systems, Vol. 16, No. 4, pp. 702-710, November 2001. 

    상세보기 crossref

  7. L. Pereira, D. Kosterev, P. Mackin, D. Davies, J. Undrill, and W. Zhu, "An Interim Dynamic Induction Motor Model for Stability Studies in the WSCC," IEEE Trans. on Power Systems, Vol. 17, No. 4, pp. 1108-1115, November 2002. 

    상세보기 crossref

  8. H. Renmu, M. Jin, and D. J. Hill, "Composite Load Modeling via Measurement Approach," IEEE Trans. on Power Systems, Vol. 21, No. 2, pp. 663-672, May 2006. 

    상세보기 crossref

  9. B. K. Choi, H. -D. Chiang, Y. Li, H. Li, Y. -T. Chen, D. -H. Huang, and M. G. Lauby, "Measurement- Based Dynamic Load Models: Derivation, Comparison, and Validation," IEEE Trans. on Power Systems, Vol. 21, No. 3, pp. 1276-1283, August 2006. 

    상세보기 crossref

  10. V. Knyazkin, C. A. Canizares, and L. H. Soder, "On the Parameter Estimation and Modeling of Aggregate Power System Loads," IEEE Trans. on Power Systems, Vol. 19, No. 2, pp. 1023-1031, May 2004. 

    상세보기 crossref

  11. B. -K. Choi, and H. -D. Chiang, "Multiple Solutions and Plateau Phenomenon in Measurement-Based Load Model Development: Issues and Suggestions," IEEE Trans. on Power Systems, Vol. 24, No. 2, pp. 824-831, May 2009. 

    상세보기 crossref

  12. H. Bai, P. Zhang, and V. Ajjarapu, "A Novel Parameter Identification Approach via Hybrid Learning for Aggregate Load Modeling," IEEE Trans. on Power Systems, Vol. 24, No. 3, pp. 1145-1154, August 2009. 

    상세보기 crossref

  13. J. Ma, Z. -Y. Dong, R. -M. He, D. J. Hill, "Measurement-based Load Modeling Using Genetic Algorithms," in proc. of IEEE Congress on Evolutionary Computation, pp. 2909-2916, September 2007. 

  14. S. H. Lee and J. W. Park, "Selection of Optimal Location and Size of Multiple Distributed Generations by Using Kalman-Filter Algorithm," IEEE Trans. on Power Systems, Vol. 24, No. 3, pp. 1393-1400, August 2009. 

    상세보기 crossref

  15. Wiltshire, R.A., Ledwich, G., and O'Shea, P., "A Kalman Filtering Approach to Rapidly Detecting Modal Changes in Power Systems," IEEE Trans. on Power Systems, Vol. 22, No. 4, pp. 1698-1706, Nov. 2007. 

    상세보기 crossref

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