Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter

Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage–Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.

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

[2]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[3]  Kai Sun,et al.  Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability , 2015, IEEE Transactions on Smart Grid.

[4]  Yi Wang,et al.  Robust dynamic state estimation of power systems with model uncertainties based on adaptive unscented H ∞ filter , 2019 .

[5]  J. Whitaker,et al.  Ensemble Square Root Filters , 2003, Statistical Methods for Climate Scientists.

[6]  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.

[7]  Renke Huang,et al.  Calibrating Parameters of Power System Stability Models Using Advanced Ensemble Kalman Filter , 2018, IEEE Transactions on Power Systems.

[8]  Lamine Mili,et al.  Robust state estimation of electric power systems , 1994 .

[9]  Weijen Lee,et al.  Development of a real time power system dynamic performance monitoring system , 1996, Proceedings of 1996 IAS Industrial and Commercial Power Systems Technical Conference.

[10]  Renke Huang,et al.  Gaussian Mixture Model-Based Ensemble Kalman Filter for Machine Parameter Calibration , 2018, IEEE Transactions on Energy Conversion.

[11]  Yi Wang,et al.  Adaptive Robust Cubature Kalman Filter for Power System Dynamic State Estimation Against Outliers , 2019, IEEE Access.

[12]  G. R. Gopinath,et al.  Sensorless control of Permanent Magnet Synchronous Motor using Square-root Cubature Kalman Filter , 2016, 2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia).

[13]  Yi Wang,et al.  Robust Forecasting-Aided State Estimation for Power System Against Uncertainties , 2020, IEEE Transactions on Power Systems.

[14]  Liang Chen,et al.  Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics , 2019, IEEE Access.

[15]  Xinxin Lv,et al.  Dynamic state estimation for synchronous machines based on interpolation H∞ extended Kalman filter , 2018, 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[16]  Petar M. Djuric,et al.  Gaussian particle filtering , 2003, IEEE Trans. Signal Process..

[17]  Renke Huang,et al.  Precise Fault Location on Transmission Lines Using Ensemble Kalman Filter , 2018, IEEE Transactions on Power Delivery.

[18]  C. Chen,et al.  Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects , 2013 .

[19]  D. Bernstein,et al.  What is the ensemble Kalman filter and how well does it work? , 2006, 2006 American Control Conference.

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

[21]  Liu Shirong,et al.  An improved Sage-Husa adaptive filtering algorithm , 2012, Proceedings of the 31st Chinese Control Conference.

[22]  F. Alvarado,et al.  Constrained LAV state estimation using penalty functions , 1997 .

[23]  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.

[24]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[25]  J. Whitaker,et al.  Ensemble Data Assimilation without Perturbed Observations , 2002 .

[26]  Simon Haykin,et al.  Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations , 2010, IEEE Transactions on Signal Processing.

[27]  James S. Thorp,et al.  Synchronized Phasor Measurement Applications in Power Systems , 2010, IEEE Transactions on Smart Grid.