Square-Root Sigma-Point Filtering Approach to State Estimation for Wind Turbine Generators in Interconnected Energy Systems

The internal states of generators obtained by dynamic states estimation (DSE) may provide additional information for the control performances. However, conventional sigma-point Kalman filter (SPKF)-based DSE may experience the loss of positive definiteness and symmetricalness of state noise covariances. This article proposes three numerically stable square-root SPKF (SR-SPKF) algorithms and proposes a novel derivative-free SR-SPKF-based DSE framework to estimate the dynamic states for doubly fed induction generator (DFIG) wind turbines in an interconnected power network. While this article investigates the dynamic behavior of the power grid at a system-level, the DSE of DFIG is achieved in a decentralized manner, which is made possible by the use of phasor measurement units (PMUs) to acquire and transmit voltage and current phasors at DFIG terminal. By utilizing the SR-SPKF-based DSE framework and PMUs data, a comparison study is conducted for square-root unscented Kalman filter, square-root Cubature Kalman filter, square-root central difference Kalman filter, and their conventional versions. The computational burden, estimation accuracy, and mathematical capability of each filtering algorithm are compared and analyzed through simulation studies.

[1]  Karl Berntorp,et al.  Tire-Stiffness and Vehicle-State Estimation Based on Noise-Adaptive Particle Filtering , 2019, IEEE Transactions on Control Systems Technology.

[2]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[3]  Rong Zheng,et al.  Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid , 2017, IEEE Systems Journal.

[4]  Bikash C. Pal,et al.  Decentralized Dynamic State Estimation in Power Systems Using Unscented Transformation , 2014, IEEE Transactions on Power Systems.

[5]  Carlo Muscas,et al.  Model Order Reduction for PMU-Based State Estimation in Distribution Grids , 2018, IEEE Systems Journal.

[6]  Zhenyu Huang,et al.  Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation , 2017, 2017 IEEE Power & Energy Society General Meeting.

[7]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

[8]  Elias Kyriakides,et al.  A Two-Stage State Estimator for Dynamic Monitoring of Power Systems , 2017, IEEE Systems Journal.

[9]  Ali Abur,et al.  Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work , 2019, IEEE Transactions on Power Systems.

[10]  Zidong Wang,et al.  A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements , 2013, IEEE Transactions on Control Systems Technology.

[11]  Hicham Chaoui,et al.  Adaptive State of Charge Estimation of Lithium-Ion Batteries With Parameter and Thermal Uncertainties , 2017, IEEE Transactions on Control Systems Technology.

[12]  Kianoush Emami,et al.  Dynamic State Estimation Based Control Strategy for DFIG Wind Turbine Connected to Complex Power Systems , 2017, IEEE Transactions on Power Systems.

[13]  Kit Po Wong,et al.  Oscillatory Stability and Eigenvalue Sensitivity Analysis of A DFIG Wind Turbine System , 2011, IEEE Transactions on Energy Conversion.

[14]  H. Sorenson,et al.  NONLINEAR FILTERING BY APPROXIMATION OF THE A POSTERIORI DENSITY , 1968 .

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

[16]  Guangjun Liu,et al.  An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots , 2015, IEEE Transactions on Control Systems Technology.

[17]  Kit Po Wong,et al.  State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems , 2016, IEEE Transactions on Power Systems.

[18]  Gexiang Zhang,et al.  Power System Real-Time Monitoring by Using PMU-Based Robust State Estimation Method , 2016, IEEE Transactions on Smart Grid.

[19]  Afef Fekih,et al.  A Fault-Tolerant Control Paradigm for Microgrid-Connected Wind Energy Systems , 2018, IEEE Systems Journal.

[20]  Niels Kjølstad Poulsen,et al.  New developments in state estimation for nonlinear systems , 2000, Autom..

[21]  Tyrone Fernando,et al.  An Adaptive-Phasor Approach to PMU Measurement Rectification for LFOD Enhancement , 2019, IEEE Transactions on Power Systems.

[22]  Kit Po Wong,et al.  Advanced Control Strategy of DFIG Wind Turbines for Power System Fault Ride Through , 2012, IEEE Transactions on Power Systems.

[23]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[24]  Pierluigi Pisu,et al.  Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model , 2015, IEEE Transactions on Control Systems Technology.

[25]  Vishal Kumar,et al.  Unbiased Minimum Variance Filter-Based Generator State Estimation Using PMU Measurements for Unknown Generator Input , 2019, IEEE Systems Journal.

[26]  Tyrone Fernando,et al.  A Comparison Study for the Estimation of SOFC Internal Dynamic States in Complex Power Systems Using Filtering Algorithms , 2017, IEEE Transactions on Industrial Informatics.

[27]  Le Yi Wang,et al.  Controllability, Observability, and Integrated State Estimation and Control of Networked Battery Systems , 2018, IEEE Transactions on Control Systems Technology.

[28]  Arno Solin,et al.  Optimal Filtering with Kalman Filters and Smoothers , 2011 .

[29]  N E Manos,et al.  Stochastic Models , 1960, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[30]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[31]  Florian Nadel,et al.  Stochastic Processes And Filtering Theory , 2016 .

[32]  Peter S. Maybeck,et al.  Stochastic Models, Estimation And Control , 2012 .

[33]  Jinna Qin,et al.  Real-Time Trajectory Compensation in Robotic Friction Stir Welding Using State Estimators , 2016, IEEE Transactions on Control Systems Technology.