A Cubature Kalman Filter Based Power System Dynamic State Estimator

This paper proposes an application of the cubature Kalman filter (CKF) to the power system dynamic state estimation (PSDSE) utilizing the measurements from the remote terminal units as well as the phasor measurement units. The CKF process utilizes the spherical cubature and Gaussian quadrature rules to estimate the probability density functions of the state space and the measurement space. This helps in linearization of the nonlinear measurement function without loss of accuracy. The CKF does not require formation of the Jacobian and Hessian matrices to execute the PSDSE, which saves the execution time. A state forecasting technique is utilized to forecast the states during the interval between two time instants of receiving the measurement sets from the field. This helps in estimating the states of the power system during the period when the field measurements are not available. The effectiveness of the application of the CKF to the PSDSE has been demonstrated on IEEE 30 bus system and 246 bus Northern Regional Power Grid Indian system.

[1]  J. K. Mandal,et al.  Incorporating nonlinearities of measurement function in power system dynamic state estimation , 1995 .

[2]  Miodrag D. Kusljevic,et al.  Simultaneous Reactive-Power and Frequency Estimations Using Simple Recursive WLS Algorithm and Adaptive Filtering , 2011, IEEE Transactions on Instrumentation and Measurement.

[3]  A. Monti,et al.  A Software-Only PTP Synchronization for Power System State Estimation With PMUs , 2012, IEEE Transactions on Instrumentation and Measurement.

[4]  Junqi Liu,et al.  State Estimation and Branch Current Learning Using Independent Local Kalman Filter With Virtual Disturbance Model , 2011, IEEE Transactions on Instrumentation and Measurement.

[5]  I. Kamwa,et al.  Dynamic State Estimation in Power System by Applying the Extended Kalman Filter With Unknown Inputs to Phasor Measurements , 2011, IEEE Transactions on Power Systems.

[6]  Farrokh Aminifar,et al.  Power System Dynamic State Estimation With Synchronized Phasor Measurements , 2014, IEEE Transactions on Instrumentation and Measurement.

[7]  Innocent Kamwa,et al.  Online State Estimation of a Synchronous Generator Using Unscented Kalman Filter From Phasor Measurements Units , 2011, IEEE Transactions on Energy Conversion.

[8]  A. Stroud.  Approximate calculation of multiple integrals , 1973 .

[9]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part II: Approximate Model , 1970 .

[10]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part I: Exact Model , 1970 .

[11]  C. K. Michael Tse,et al.  Analysis of the Characteristic of the Kalman Gain for 1-D Chaotic Maps in Cubature Kalman Filter , 2013, IEEE Signal Processing Letters.

[12]  Paolo Attilio Pegoraro,et al.  Efficient Branch-Current-Based Distribution System State Estimation Including Synchronized Measurements , 2013, IEEE Transactions on Instrumentation and Measurement.

[13]  Seema Singh,et al.  Phasor-assisted Hybrid State Estimator , 2010 .

[14]  Ming Xin,et al.  High-degree cubature Kalman filter , 2013, Autom..

[15]  S. S. Thakur,et al.  Robust dynamic state estimation of power systems based on M-Estimation and realistic modeling of system dynamics , 1998 .

[16]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

[17]  Shaobu Wang,et al.  An Alternative Method for Power System Dynamic State Estimation Based on Unscented Transform , 2012, IEEE Transactions on Power Systems.

[18]  Elias Kyriakides,et al.  The Effect of Variable Weights in a WLS State Estimator Considering Instrument Transformer Uncertainties , 2014, IEEE Transactions on Instrumentation and Measurement.

[19]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part III: Implementation , 1970 .

[20]  Elias Kyriakides,et al.  Uncertainty in Power System State Variables Obtained Through Synchronized Measurements , 2009, IEEE Transactions on Instrumentation and Measurement.

[21]  E. Kyriakides,et al.  Optimal Placement of Phasor Measurement Units for Power System Observability , 2008, IEEE Transactions on Power Systems.

[22]  Gustavo Valverde,et al.  Unscented kalman filter for power system dynamic state estimation , 2011 .

[23]  A. P. A. D. Silva,et al.  State forecasting in electric power systems , 1983 .

[24]  A. Sharma,et al.  An Iterative Multiarea State Estimation Approach Using Area Slack Bus Adjustment , 2016, IEEE Systems Journal.

[25]  Vladimir V. Terzija,et al.  Estimation of Frequency and Fundamental Power Components Using an Unscented Kalman Filter , 2012, IEEE Transactions on Instrumentation and Measurement.

[26]  Yingmin Jia,et al.  Location of Mobile Station With Maneuvers Using an IMM-Based Cubature Kalman Filter , 2012, IEEE Transactions on Industrial Electronics.

[27]  S. Chakrabarti,et al.  A Constrained Formulation for Hybrid State Estimation , 2011, IEEE Transactions on Power Systems.

[28]  W. E. Miller,et al.  Dynamic state estimation in power systems , 1971 .