Parameter estimation and dynamic modelling of power systems and their components are basis of design, planning and stability or security assessment in power systems. This study considers the estimation of power system model parameters by a global identification framework based on the maximum-likelihood principle. The proposed framework is formulated as a non-linear optimisation problem, which is solved by a hybrid method based on the bat-inspired algorithm and differential evolution method. The combination of these algorithms makes the hybrid method faster and it obtains closer to the global minimum than a pure global method. Since noise and model uncertainties are inherent parts of system identification, the effect of these factors on the performance of the proposed identification framework are studied. Results based on synthetic data in frequency domain show that the estimated parameters are close to the correct values even in the presence of significant measurement noise and considerable uncertainties.

References

1. 1)
  - 23. Saavedra-Montes, A.J., Ramirez-Scarpetta, J.M., Malik, O.P.: ‘Methodology to estimate parameters of an excitation system based on experimental conditions’, Electr. Power Syst. Res., 2011, 81, (1), pp. 170–176 (doi: 10.1016/j.epsr.2010.08.004).
2. 2)
  - 51. Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: ‘A novel particle swarm optimization algorithm with adaptive inertia weight’, Appl. Soft Comput., 2011, 11, pp. 3658–3670 (doi: 10.1016/j.asoc.2011.01.037).
3. 3)
  - 46. Teo, J.: ‘Exploring dynamic self-adaptive populations in differential evolution’, Soft Comput., 2006, 10, (8), pp. 673–686 (doi: 10.1007/s00500-005-0537-1).
4. 4)
  - 27. Abiri, E., Rashidi, F., Niknam, T.: ‘An optimal PMU placement method for power system observability under various contingencies’, Int. Trans. Electr. Energy Syst., 2014, doi: 10.1002/etep.1848.
5. 5)
  - 38. Das, S., Suganthan, P.N.: ‘Differential evolution: a survey of the state-of-the-art’, IEEE Trans. Evol. Comput., 2011, 15, (1), pp. 4–31 (doi: 10.1109/TEVC.2010.2059031).
6. 6)
  - 19. Pillutla, S., Keyhani, A.: ‘Neural network observers for on-line tracking of synchronous generator parameters’, IEEE Trans. Energy Convers., 1999, 14, pp. 23–31 (doi: 10.1109/60.749143).
7. 7)
  - 5. Fan, L., Miao, Z., Wehbe, Y.: ‘Application of dynamic state and parameter estimation techniques on real-world data’, IEEE Trans. Smart Grids, 2013, 4, (2), pp. 1133–1141 (doi: 10.1109/TSG.2012.2230031).
8. 8)
  - 24. Tsai, C.-C., Lee, W.-J., Nashawati, E., Wu, C.-C., Lan, H. -W.: ‘PMU based generator parameter identification to improve the system planning and operation’. IEEE Int. Conf. on Power and Energy, 2012, pp. 1–8.
9. 9)
  - 27. Niknam, T., Azizipanah-Abarghooee, R., Zare, M., Bahmani-Firouzi, B.: ‘Reserve constrained dynamic environmental/economic dispatch: a new multiobjective self-adaptive learning bat algorithm’, IEEE Syst. J., 2013, 7, (4), pp. 763–776 (doi: 10.1109/JSYST.2012.2225732).
10. 10)
  - A. Aminifar , A. Khodaei , M.F. Firuzabad , M. Shahidehpour . Contingency-constrained PMU placement in power networks. IEEE Trans. Power Syst. , 1 , 516 - 523
11. 11)
  - 41. Baziar, A., Kavoosi-Fard, A., Zare, J.: ‘A novel self-adaptive modification approach based on bat algorithm for optimal management of renewable MG’, J. Intell. Learn. Syst. Appl., 2013, 5, pp. 11–18.
12. 12)
  - 34. Manousakis, N.M., Korres, G.N., Georgilakis, P.S.: ‘Taxonomy of PMU placement methodologies’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 1070–1077 (doi: 10.1109/TPWRS.2011.2179816).
13. 13)
  - 24. Yang, X.S.: ‘A new metaheuristic bat-inspired algorithm’, Nat. Inspired Cooperative Strateg. Optim., 2010, 284, (1), pp. 65–74 (doi: 10.1007/978-3-642-12538-6_6).
14. 14)
  - 35. Gerstoft, P., Mecklenbrauker, C.F.: ‘Ocean acoustic inversion with estimation of a posteriori probability distributions’, J. Comput. Acoust., 1998, 104, pp. 808–819 (doi: 10.1121/1.423355).
15. 15)
  - 31. Xu, B., Abur, A.: ‘Observability analysis and measurement placement for systems with PMUs’. IEEE/PES Power Systems Conf., 2004, pp. 943–946.
16. 16)
  - 20. Tsai, H., Keyhani, A., Demcko, J.A., Selin, D.A.: ‘Development of a neural network based saturation model for synchronous generator analysis’, IEEE Trans. Energy Convers., 1995, 10, (4), pp. 617–624 (doi: 10.1109/60.475831).
17. 17)
  - 18. Puma, J.Q., Colome, D.G.: ‘Parameters identification of excitation system models using genetic algorithms’, IET. Gener. Transm. Distrib., 2008, 2, (3), pp. 456–467 (doi: 10.1049/iet-gtd:20070170).
18. 18)
  - 26. Abiri, E., Rashidi, F., Niknam, T., Salehi, M.R.: ‘Optimal PMU placement method for complete topological observability of power system under various contingencies’, Int. J. Electr. Power Energy Syst., 2014, 61, pp. 585–593 (doi: 10.1016/j.ijepes.2014.03.068).
19. 19)
  - 28. Enshaee, A., Hooshmand, R.A., Fesharaki, F.H.: ‘A new method for optimal placement of phasor measurement units to maintain full network observability under various contingencies’, Electr. Power Syst. Res., 2012, 89, (1), pp. 1–10 (doi: 10.1016/j.epsr.2012.01.020).
20. 20)
  - 32. Gomez, O., Rios, M.A., Anders, G.: ‘Reliability-based phasor measurement unit placement in power systems considering transmission line outages and channel limits’, IET Gener. Transm. Distrib., 2014, 8, (1), pp. 121–130 (doi: 10.1049/iet-gtd.2013.0251).
21. 21)
  - 2. Dehghani, M., Nikravesh, S.K.Y.: ‘Nonlinear state space model identification of synchronous generators’, Electr. Power Syst. Res., 2008, 78, pp. 926–940 (doi: 10.1016/j.epsr.2007.07.001).
22. 22)
  - 8. Shen, M., Venkatasubramanian, V., Abi-Samra, N., Sobajic, D.: ‘A new framework for estimation of generator dynamic parameters’, IEEE Trans. Power Syst., 2000, 15, (2), pp. 756–763 (doi: 10.1109/59.867170).
23. 23)
  - 11. Fard, R.D., Karrari, M., Malik, O.P.: ‘Synchronous generator model identification for control application using volterra series’, IEEE Trans. Energy Convers., 2005, 20, (4), pp. 852–858 (doi: 10.1109/TEC.2005.847997).
24. 24)
  - 44. Brest, J., Boškovć, B., Greiner, S., Žumer, V., Maučec, M.S.: ‘Performance comparison of self-adaptive and adaptive differential evolution algorithms’, Soft Comput., 2007, 11, (7), pp. 617–629 (doi: 10.1007/s00500-006-0124-0).
25. 25)
  - J.J.R. Melgoza , G.T. Heydt , A. Keyhani , B.L. Agrawal , D. Selin . Synchronous machine parameter estimation using Hartley series. IEEE Trans. Energy Convers. , 1 , 49 - 54
26. 26)
  - 12. Park, J.-W., Venayagamoorthy, G.K., Harley, R.G.: ‘MLP/RBF neural-networks based online global model identification of synchronous generator’, IEEE Trans. Ind. Electron., 2005, 52, (6), pp. 1685–1695 (doi: 10.1109/TIE.2005.858703).
27. 27)
  - 12. Ning, Z., Trudnowski, D.J., Pierre, J.W., Mittelstadt, W.A.: ‘Electromechanical mode online estimation using regularized robust RLS methods’, IEEE Trans. Power Syst., 2008, 23, pp. 1670–1680 (doi: 10.1109/TPWRS.2008.2002173).
28. 28)
  - 45. Ahandani, M.A., Shirjoposh, N.P., Banimahd, R.: ‘Three modified versions of differential evolution algorithm for continuous optimization’, Soft Comput., 2011, 15, pp. 803–830 (doi: 10.1007/s00500-010-0636-5).
29. 29)
  - 36. Marple, L.: ‘Digital spectral analysis’ (Prentice-Hall, Englewood Cliffs, NJ, 1987).
30. 30)
  - 17. Ghahremani, E., Kamwa, I.: ‘Dynamic state estimation in power system by applying the extended Kalman filter with unknown inputs to phasor measurements’, IEEE Trans. Power Syst., 2011, 26, pp. 2556–2566 (doi: 10.1109/TPWRS.2011.2145396).
31. 31)
  - 52. Tsai, C.C.: ‘PMU-based parameter identification for the synchronous generator dynamic model’. PhD thesis, The University of Texas at Aarlington, 2011.
32. 32)
  - 22. Rashidi, F., Abiri, E., Niknam, T., Salehi, M.R.: ‘Parameter identification of power plant characteristics based on PMU data using differential evolution based improved shuffled frog leaping algorithm’, Int. J. Bio-Inspired Comput., 2014 (in press).
33. 33)
  - 47. Arafa, M., Sallam, E.A., Fahmy, M.M.: ‘An enhanced differential evolution optimization algorithm’. IEEE Int. Conf. on DICTAP, 2014, pp. 216–225.
34. 34)
  - 49. Arasomwan, A.M., Adewumi, A.O.: ‘An investigation into the performance of particle swarm optimization with various chaotic maps’, Math. Problems Eng., 2014, 2014, pp. 1–17 (doi: 10.1155/2014/178959).
35. 35)
  - R. Wamkeue , I. Kamwa , X. Dai-Do , A. Keyhani . Iteratively reweighted least square for maximum likelihood identification of synchronous machine parameters from on-line tests. IEEE Trans. Energy Convers. , 2 , 159 - 166
36. 36)
  - 25. Panteli, M., Crossly, P.A., Kirschen, D.S., Sobajic, D.J.: ‘Assessing the impact of insufficient situation awareness on power system operation’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 2967–2977 (doi: 10.1109/TPWRS.2013.2240705).
37. 37)
  - 14. Welch, G., Bishop, G.: ‘An introduction to the Kalman filter’. TR95-041, Department of Computer Science, University of North Carolina at Chapel Hill, April2004.
38. 38)
  - 12. Chow, J.H., Chakrabortty, A., Vanfretti, L., Arcak, M.: ‘Estimation of radial power system transfer path dynamic parameters using synchronized phasor data’, IEEE Trans. Power Syst., 2008, 23, (2), pp. 564–571 (doi: 10.1109/TPWRS.2008.919315).
39. 39)
  - 13. Al-Hamadi, H.M., EL-Naggar, K.M.: ‘Measurement of synchronous machine parameters using Kalman filter based fuzzy logic estimator’, Meas. J., 2010, 43, (10), pp. 1327–1335 (doi: 10.1016/j.measurement.2010.07.012).
40. 40)
  - 43. Wang, X., Zhao, S.: ‘Differential evolution algorithm with self-adaptive population resizing mechanism’, Math. Problems Eng., 2013, 2013, pp. 1–14.
41. 41)
  - 37. Jensen, F.B., Kuperman, W.A., Porter, M.B., Schmidt, H.: ‘Computational ocean acoustics’ (Springer, 2011, 2nd edn.).
42. 42)
  - 42. Yang, X.S.: ‘Bat algorithm for multi-objective optimization’, Int. J. Bio-Inspired Comput., 2011, 3, (5), pp. 267–274.
43. 43)
  - 16. Cheng, Y., Lee, W.-J., Huang, S.-H., Adams, J.: ‘Dynamic parameter identification of generators for smart grid development’. Power Engineering Society IEEE General Meeting – PES, 2011, pp. 1–7.
44. 44)
  - 7. Huang, M., Li, W., Yan, W.: ‘Estimating parameters of synchronous generators using square-root unscented Kalman filter’, Electr. Power Syst. Res., 2010, 80, (9), pp. 1137–1144 (doi: 10.1016/j.epsr.2010.03.007).
45. 45)
  - R. Escarela-Perez , T. Niewierowicz , E. Campero-Littlewood . Synchronous machine parameters from frequency-response finite-element simulations and genetic algorithms. IEEE Trans. Energy Convers. , 2 , 198 - 203
46. 46)
  - 21. Saavedra-Montes, A.J., Ramirez-Scarpetta, J.M., Ramos-Paja, C.A., Malik, O.P.: ‘Identification of excitation systems with the generator online’, Electr. Power Syst. Res., 2012, 87, pp. 1–9 (doi: 10.1016/j.epsr.2012.01.005).
47. 47)
  - 1. Kyriakides, E., Heydt, G., Vittal, V.: ‘On-line estimation of synchronous generator parameters using a damper current observer and a graphic user interface’, IEEE Trans. Energy Convers., 2004, 19, (3), pp. 499–507 (doi: 10.1109/TEC.2004.832057).
48. 48)
  - 39. Tsai, P.W., Pan, J.S., Liao, B.Y., Tsai, M.J., Istanda, V.: ‘Bat algorithm inspired algorithm for solving numerical optimization problems’, Appl. Mech. Mater., 2011, 148–149, pp. 134–137 (doi: 10.4028/www.scientific.net/AMM.148-149.134).
49. 49)
  - 29. Emami, R., Abur, A.: ‘Robust measurement design by placing synchronized phasor measurements on network branches’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 38–43 (doi: 10.1109/TPWRS.2009.2036474).
50. 50)
  - 33. Miljaníc, Z., Djurović, I., Vujošević, I.: ‘Multiple channel PMU placement considering communication constraints’, Energy Syst., 2013, 4, (2), pp. 125–135 (doi: 10.1007/s12667-012-0069-6).
51. 51)
  - 9. Hajforoosh, S., Nabavi, S.M.H., Masoum, M.A.S.: ‘Coordinated aggregated-based particle swarm optimisation algorithm for congestion management in restructured power market by placement and sizing of unified power flow controller’, IET Sci. Meas. Technol., 2012, 6, (4), pp. 267–278 (doi: 10.1049/iet-smt.2011.0143).

On-line parameter identification of power plant characteristics based on phasor measurement unit recorded data using differential evolution and bat inspired algorithm

References

Related content