DOI QR코드

DOI QR Code

Development of Composite Load Models of Power Systems using On-line Measurement Data

  • Choi Byoung-Kon (School of Electrical and Computer Engineering, Cornell University) ;
  • Chiang Hsiao Dong (School of Electrical and Computer Engineering, Cornell University) ;
  • Li Yinhong (School of Electrical and Computer Engineering, Cornell University) ;
  • Chen Yung Tien (System Planning Department, Taiwan Power Company) ;
  • Huang Der Hua (EPRI, Palo Alto) ;
  • Lauby Mark G. (EPRI, Palo Alto)
  • Published : 2006.06.01

Abstract

Load representation has a significant impact on power system analysis and control results. In this paper, composite load models are developed based on on-line measurement data from a practical power system. Three types of static-dynamic load models are derived: general ZIP-induction motor model, Exponential-induction motor model and Z-induction motor model. For the dynamic induction motor model, two different third-order induction motor models are studied. The performances in modeling real and reactive power behaviors by composite load models are compared with other dynamic load models in terms of relative mismatch error. In addition, numerical consideration of ill-conditioned parameters is addressed based on trajectory sensitivity. Numerical studies indicate that the developed composite load models can accurately capture the dynamic behaviors of loads during disturbance.

Keywords

composite load model;measurement-based approach;nonlinear least-squares;parameter estimation;stability analysis

References

  1. IEEE Recommended Practice for Excitation System Models for Power System Stability Studies, IEEE Standard 421. 5-1992
  2. IEEE Standard DeJinitions for Excitation Systems and Synchronwus Machines, IEEE Standard 421. 1-1986
  3. IEEE Task Force on Load Representation for Dynamic Performance, 'Bibliography on load models for power flow and dynamic performance simulation,' IEEE Trans. Power Syst., vol. 10, no. 1, pp. 523-538, Feb. 1995 https://doi.org/10.1109/59.373979
  4. Dingguo Chen, Ronald R. Mohler, 'Neural-networkbased load modeling and its use in voltage stability analysis,' IEEE Trans. Control Systems Technology, vol. 11, no.11, pp. 460-470, Jul. 2003 https://doi.org/10.1109/TCST.2003.813400
  5. H.-D. Chiang et al., 'Development of a dynamic ZIPmotor load model from on-line field measurements', International Journal of Electrical Power & Energy Systems, vol. 19, no. 7, pp. 459-468, 1997 https://doi.org/10.1016/S0142-0615(97)00016-1
  6. J.-C. Wang, H.-D. Chiang, C.-L. Chang, and A.-H. Liu, 'Development of a fi-equency-dependent composite load model using the measurement approach,' IEEE Trans. Power Syst., vol. 9, no. 3, pp. 1546-1 556, Aug. 1994 https://doi.org/10.1109/59.336105
  7. Y. Baghzouz, Craig Quist, 'Composite load model derivation fiom recorded field data,' in Proc. IEEE PES 1999 Winter Meeting, vol. 1 , pp. 731 - 718, 31 Jan.-4 Feb. 1999
  8. Wen-Shiow Kao, Chia-Tien Lin, Chiang-Tsang Huang, Yung-Tien Chen, Chiew-Yann Chiou, 'Comparison of simulated power system dynamics applying various load models with actual recorded data', IEEE Trans. Power Syst., vol. 9, no. 1, pp. 248 - 254, Feb. 1994 https://doi.org/10.1109/59.317604
  9. R. Fletcher, Practical Methods of Optimization, Second Edition, John Wiley & Sons, New York, 1987
  10. L.G. Dias, M.E. El-Hawary, 'Nonlinear parameter estimation experiments for static load modeling in electric power systems,' IEE Proc. vol. 136, Pt. C, no. 2, pp. 68-77, Mar. 1998
  11. M. Burth, G. C. Verghese, and M. Velez-Reyes, 'Subset selection for improved parameter estimation in on-line identification of a synchronous generator,' IEEE Trans. Power Syst., vol. 14, no. 1, pp. 218-225, Feb. 1999 https://doi.org/10.1109/59.744536
  12. P. M. Frank, Introduction to System Sensitivity Theory. New York: Academic Press, 1978
  13. P. Kundur, Power System Stability and Control, McGraw-Hill, Inc., 1994
  14. C.-Y. Chiou, C.-H. Huang, H.-D. Chiang, J.-C. Wang, 'Development of a microprocessor-based transient data recording system for load behavior analysis' IEEE Trans. Power Syst., vol. 8, no. 1, pp. 16-22, 1993 https://doi.org/10.1109/59.221243
  15. Ju, P., Handschin, E., Karlsson, D, 'Nonlinear dynamic load modeling: model and parameter estimation,' IEEE Trans. Power Syst., vol. 11, no. 4, pp. 1689 - 1697, NOV. 1996 https://doi.org/10.1109/59.544629
  16. C.-J. Lin, Y.-T. Chen, H.-D. Chiang, and J.-C. Wang, 'Dynamic load models in power systems using the measurement approach,' IEEE Trans. Power Syst., vol. 8, no. 1, pp. 309-3 15, Feb. 1993 https://doi.org/10.1109/59.221226
  17. CIGRE Task Force 38.02.05, 'Load Modeling and Dynamics', Electra, May 1990
  18. S. Ahmed-Zaid, M. Taleb, 'Structural Modeling of Small and Large Induction Machines Using Integral Manifolds', IEEE Transactions on Energy Conversion, vol. 6, no. 3, pp. 529 - 535, Sep. 1991 https://doi.org/10.1109/60.84331
  19. Keyhani, A.; Lu, W.; Heydt, G.T., 'Composite neural network load models for power system stability analysis', IEEE PES Power Systems Conference and Exposition, 2004, vol. 2, pp. 1159 - 1163, Oct. 2004

Cited by

  1. Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System vol.03, pp.04, 2015, https://doi.org/10.4236/wjet.2015.34020
  2. Kalman-Filter Based Static Load Modeling of Real Power System Using K-EMS Data vol.7, pp.3, 2012, https://doi.org/10.5370/JEET.2012.7.3.304
  3. Estimation of composite load model with aggregate induction motor dynamic load for an isolated hybrid power system vol.9, pp.4, 2015, https://doi.org/10.1007/s11708-015-0373-7
  4. Role of Articular Cartilage and Chondrocyte Changes and Mononuclear Apoptosis in the Pathogenesis and Strategy of Therapy in Polyarticular Juvenile Rheumatoid Arthritis vol.3, pp.2, 2003, https://doi.org/10.3923/jms.2003.137.156
  5. Measurement-based Static Load Modeling Using the PMU data Installed on the University Load vol.7, pp.5, 2012, https://doi.org/10.5370/JEET.2012.7.5.653
  6. A novel tool for transient stability analysis of large-scale power systems: Its application to the KEPCO system vol.15, pp.7, 2007, https://doi.org/10.1016/j.simpat.2007.04.007
  7. Fast and Reliable Estimation of Composite Load Model Parameters Using Analytical Similarity of Parameter Sensitivity vol.31, pp.1, 2016, https://doi.org/10.1109/TPWRS.2015.2409116
  8. The Volume Measurement of Air Flowing through a Cross-section with PLC Using Trapezoidal Rule Method vol.8, pp.4, 2013, https://doi.org/10.5370/JEET.2013.8.4.872
  9. Development of the automatic load modelling system using PQM data on industry site vol.7, pp.1, 2017, https://doi.org/10.1080/22348972.2016.1158350
  10. Analytical approach to estimate mechanical parameters in induction machine using transient response parameters pp.20507038, 2018, https://doi.org/10.1002/etep.2751
  11. Automatic Identification of Power System Load Models Based on Field Measurements vol.33, pp.3, 2018, https://doi.org/10.1109/TPWRS.2017.2763752