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Performance Analysis of Static and Dynamic State Estimation Incorporating Synchro Phasor Measurements


  • EEE, K L University, Vaddeswaram, Guntur District – 520002, Andhra Pradesh, India


Objective: State estimation is used as a necessary tool for online monitoring, analysis and control of power systems. The main objective of this paper is to deal with static state estimation using the snap shot data and dynamic state estimation which accounts for the time varying behaviour of power system states in to consideration. Methods/Analysis: Effects of inclusion of PMU measurements along with the available metered data have been explored using the weighted least square state estimation technique in this paper. A comparative analysis of static state estimation with and without bad data has been carried out and the bad data has been identified and eliminated by using largest normalized residue test. Findings: To investigate the time varying nature of the system states linear state estimator with second order approximation and kalman filter techniques has been proposed in this paper. Case studies are conducted on IEEE 14 bus test system and the test results obtained from non linear, linear first order, second order and kalman filter techniques have been compared. Application/Improvements: Correction of state variables are obtained using linear state estimation with first order and second order approximation and kalman filter techniques has been compared to get the better state estimation algorithm.


Bad Data, Kalman Filter, PMU, State Estimation, Static, Dynamic, Weighted Least Squares.

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  • Vaishnavi C, Sheikh IA. A review of power system state estimation by weighted least square technique. International Journal of Advance Engineering and Research Development (IJAERD); 2015. p. 1–4.
  • Kamireddy S, Schulz NN, Srivastava AK. Comparison of state estimation algorithms for extreme contingencies. IEEE 40th North American Power Symposium; Calgary, AB. 2008 Sep 28-30. p. 1–5.
  • Brandao RFM, Carvalho JAB, Ferreira IM. State estimation in transmission line systems. 39th International Universities Power Engineering Conference (UPEC); 2004. p. 1200–3.
  • Sengupta A, Sinha AK. A comparative study on state estimation algorithms. Second International Conference on Industrial and Information Systems, ICIIS; Sri Lanka. 2007 Aug 8-11.
  • Mallick S, Ghoshal SP, Acharjee P, Thakur SS. Optimal static state estimation using hybrid particle swarm-differential evolution based optimization. Energy and Power Engineering. 2013; 5:670–6.
  • Babu DS, Jamuna K, Aryanandiny B. Power system state estimation - A review. ACEEE International Journal on Electrical and Power Engineering. 2014 Feb; 5(1):10–8.
  • Mahaei SM, Navayi MR. Power system state estimation with Weighted Linear Least Square. IJECE. 2014 Apr; 4(2):169–78.
  • Rice M, Heydt GT. Phasor measurement unit data in power system state estimation. Power Systems Engineering Research Centre; 2005 Jan.
  • Chen F, Han X, Pan Z, Han L. State estimation model and algorithm including PMU. DRPT 2008; Nanjing China. 2008 Apr 6-9; p. 1097–102.
  • Manousakis NM, Korres GN, Aliprantis JN, Vavourakis GP, George-Constantine J. A two-stage state estimator for power systems with PMU and SCADA measurements. Makrinas School of Electrical and Computer Engineering, National Technical University of Athens (NTUA); 2013. p. 1–6.
  • Merrill HM, Schweppe FC. Bad data suppression in power system static state estimation. IEEE Transactions on Power Apparatus and Systems. 1971 Nov; 90(6):2718–25.
  • Kun Z. Impact of input uncertainties on power system state estimation robustness. [Master Thesis]. Stockholm, Sweden; 2008.
  • Tarali A, Abur A. Bad data detection in two-stage state estimation using phasor measurements. 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe); Berlin. 2012.
  • Carvalho B, Bretas N. Analysis of the largest normalised residual test robustness for measurements gross errors processing in the WLS state estimator. Sao Carlos, Sao Paulo, Brazil: University of Sao Paulo; 2012. p. 1–6.
  • Shivakumar NR, Jain A. A review of power system dynamic state estimation techniques. Joint International Conference on Power Systems Technology and IEEE Power India Conference; New Delhi. 2008. p. 1–6.
  • Shivakumar NR, Jain A. Including phasor measurements in dynamic state estimation of power systems. Proceedings of International Conference on Power System Analysis Control and Optimization; Visakhapatnam. 2008 Mar 13-15. p. 958–63.
  • Jain A, Shivakumar NR. Phasor measurements in dynamic state estimation of power systems. TENCON 2008 IEEE Region 10 Conference; Hyderabad. 2008. p. 1–6.
  • Welch G, Bishop G. An introduction to the kalman filter. Chapel Hill, NC; University of North Carolina at Chapel Hill; 2006 Jul.
  • Reid I. Estimation II. Hilary Term; 2001.
  • Edgar TF. State estimation using the kalman filter. Austin: University of Texas; 2006.
  • Madhumita M, Aich SR. Study of kalman, Extended Kalman and Unscented Kalman Filter. National Institute of Technology; Rourkela. 2010 May.
  • Tebianian H, Jeyasurya B. Dynamic state estimation in power systems using kalman filters. IEEE Electrical Power and Energy Conference (EPEC); 2013. p. 1–5.
  • Safarinejadian B, Mozaffari M. A new kalman filter based state estimation method for multi-input multi-output unit time-delay systems. Indian Journal of Science and Technology. 2013 Mar; 6(3).


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