Abstract
We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory. The proposed algorithm also successfully detects and identifies sudden load changes in power systems. The method uses three normalized vectors to process errors at each sampling time: normalized measurement residual, normalized Lagrange multiplier, and normalized innovation vector. An IEEE 14-bus test system was used to verify and demonstrate the effectiveness of the proposed method. Numerical results are presented and discussed to show the accuracy of the method.
Similar content being viewed by others
References
Abur, A., Exposito, A.G., 2004. Power System State Estimation. Marcel & Dekker Publishers, New York.
Bao, W., Guo, R.P., Han, Z.X., et al., 2015. A substation oriented approach to optimal phasor measurement units placement. J. Electr. Eng. Technol, 10(1):18–29.
Debs, A.S., Larson, R.E., 1970. A dynamic estimator for tracking the state of the power system. IEEE Trans. Power Appar. Syst., PAS-89(7):1670–1678. http://dx.doi.org/10.1109/TPAS.1970.292822
Falcao, D.M., Cooke, P.A., Brameller, A., 1982. Power system tracking state estimation and bad data processing. IEEE Trans. Power Syst., PAS-101(2):325–333. http://dx.doi.org/10.1109/TPAS.1982.317110
Filho, M.B.C., Souza, J.C.S., 2009. Forecasting aided state estimation: part I: panorama. IEEE Trans. Power Syst., 24(4):1667–1677. http://dx.doi.org/10.1109/TPWRS.2009.2030295
Filho, M.B.C., Silva, A.M.L., Cantera, J.M.C., et al., 1989. Information debugging for real-time power systems monitoring. IET Gener. Transm. Distr., 136(3):145–152. http://dx.doi.org/10.1049/ip-c.1989.0021
Filho, M.B.C., Souza, J.C.S., Freund, R.S., 2009. Forecasting aided state estimation: part II: implementation. IEEE Trans. Power Syst., 24(4):1678–1685. http://dx.doi.org/10.1109/TPWRS.2009.2030297
Glazunova, A.M., 2010. Forecasting power system state variables on the basis dynamic state estimation and artificial neural networks. IEEE Region 8 Int. Conf. on Computational Technologies in Electrical and Electronics Engineering, p.470–475. http://dx.doi.org/10.1109/SIBIRCON.2010.5555125
Gu, C., Jirutitijaroen, P., 2015. Dynamic state estimation under communication failure using Kriging based bus load forecasting. IEEE Trans. Power Syst., 30(6):2831–2840. http://dx.doi.org/10.1109/TPWRS.2014.2382102
Gui, Y., Kavasseri, R., 2015. A particle filter for dynamic state estimation in multi-machine systems with detailed models. IEEE Trans. Power Syst., 30(6):3377–3385. http://dx.doi.org/10.1109/TPWRS.2014.2387792
Hu, L., Wang, Z., Liu, X., 2015. Dynamic state estimation of power systems with quantization effects: a recursive filter approach. IEEE Trans. Neur. Netw. Learn. Syst., 27(8):1604–1614. http://dx.doi.org/10.1109/TNNLS.2014.2381853
Huang, S.J., Shih, K.R., 2002. Dynamic state estimation scheme including nonlinear measurement function considerations. IET Gener. Transm. Distr., 149(6):673–678. http://dx.doi.org/10.1049/ip-gtd:20020644
Karimipour, H., Dinavahi, V., 2015. Extended Kalman filter based parallel dynamic state estimation. IEEE Trans. Smart Grid, 6(3):1539–1549. http://dx.doi.org/10.1109/TSG.2014.2387169
Lin, J.M., Huang, S.J., Shih, K.R., 2003. Application of sliding surface enhances fuzzy control for dynamic state estimation of a power system. IEEE Trans. Power Syst., 18(2):570–577. http://dx.doi.org/10.1109/TPWRS.2003.810894
Prasad, G.D., Thakur, S.S., 1998. A new approach to dynamic state estimation of power systems. Electr. Power Syst. Res., 45(3):173–180. http://dx.doi.org/10.1016/S0378-7796(97)01219-4
Qing, X.Y., Karimi, H.R., Niu, N.G., et al., 2015. Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems. Int. J. Electr. Power Energy Syst., 65:26–33. http://dx.doi.org/10.1016/j.ijepes.2014.09.024
Qiu, J.B., Feng, G., Gao, H.J., 2013a. Static-output-feedback H ∞ control of continuous-time T-S fuzzy affine systems via piecewise Lyapunov functions. IEEE Trans. Fuzzy Syst., 21(2):245–261. http://dx.doi.org/10.1109/TFUZZ.2012.2210555
Qiu, J.B., Tian, H., Lu, Q.G., et al., 2013b. Nonsynchronized robust filtering design for continuous-time T-S fuzzy affine dynamic systems based on piecewise Lyapunov functions. IEEE Trans. Cybern., 43(6):1755–1766. http://dx.doi.org/10.1109/TSMCB.2012.2229389
Qiu, J.B., Wei, Y.L., Karimi, H.R., 2015. New approach to delay dependent H ∞ control for continuous time Markovian jump systems with time varying delay and deficient transition descriptions. J. Franklin Inst., 352(1):189–215. http://dx.doi.org/10.1016/j.jfranklin.2014.10.022
Risso, M., Rubiales, A.J., Lotito, A.P., 2015. Hybrid method for power system state estimation. IET Gener. Transm. Distr., 9(7):636–643. http://dx.doi.org/10.1049/iet-gtd.2014.0836
Sharma, A., Srivastava, S.C., Chakrabarti, S., 2015. A multiagent-based power system hybrid dynamic state estimator. IEEE Intell. Syst., 30(3):52–59. http://dx.doi.org/10.1109/MIS.2015.52
Shih, K.R., Huang, S.J., 2002. Application of a robust algorithm for dynamic state estimation of a power system. IEEE Trans. Power Syst., 17(1):141–147. http://dx.doi.org/10.1109/59.982205
Silva, A.M.L., Filho, M.B.C., 1983. State estimation in electric power systems. IET Gener. Transm. Distr., 130:237–244.
Silva, A.M.L., Filho, M.B.C., Cantera, J.M.C., 1987. An efficient dynamic state estimation algorithm including bad data processing. IEEE Trans. Power Syst., 2(4):1050–1058. http://dx.doi.org/10.1109/TPWRS.1987.4335300
Tebianian, H., Jeyasurya, B., 2015. Dynamic state estimation in power systems: modeling, and challenges. Electr. Power Syst. Res., 121:109–114. http://dx.doi.org/10.1016/j.epsr.2014.12.005
Valverde, G., Terzija, V., 2011. Unscented Kalman filter for power system dynamic state estimation. IET Gener. Transm. Distr., 5(1):29–37. http://dx.doi.org/10.1049/iet-gtd.2010.0210
Wang, S., Gao, W., Meliopoulos, A.P.S., 2012. An alternative method for power system dynamic state estimation based on unscented transform. IEEE Trans. Power Syst., 27(2):942–950. http://dx.doi.org/10.1109/TPWRS.2011.2175255
Zhu, J., Abur, A., 2010. Improvement in network parameter error identification via synchronized phasors. IEEE Trans. Power Syst., 25(1):44–50. http://dx.doi.org/10.1109/TPWRS.2009.2030274
Author information
Authors and Affiliations
Corresponding author
Additional information
ORCID: Mehdi AHMADI JIRDEHI, http://orcid.org/0000-0002-7836-9401
Rights and permissions
About this article
Cite this article
Ahmadi Jirdehi, M., Hemmati, R., Abbasi, V. et al. A multi-functional dynamic state estimator for error validation: measurement and parameter errors and sudden load changes. Frontiers Inf Technol Electronic Eng 17, 1218–1227 (2016). https://doi.org/10.1631/FITEE.1500301
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1500301