Dynamic state estimation of a doubly fed induction generator based on a comprehensive nonlinear model

Abstract One important challenge in controller design for Doubly-Fed Induction Generator (DFIG) or dynamic analysis of networks with DFIGs, is its nonlinearity besides invisibility of some important state variables. In this paper, a state estimation algorithm based on Extended Kalman Filter (EKF) is proposed for grid connected DFIG. A complete 15th order nonlinear model of DFIG equipped with a nonlinear controller is utilized, and all state equations are derived in appropriate form to be used for EKF. The results of the proposed state estimation algorithm can be used for modeling and analysis of any disturbance such as wind speed variations or faults occurrence in the network. To obtain electrical measures required for state estimation, a Phasor Measurement Unit (PMU) is utilized and all measurement and process noise are modeled. Accuracy of the proposed algorithm is evaluated by five different case studies covering the effect of initial guess for state variables, the effect of process and measurement noises, variation in wind speed and occurrence of a solid short circuit close to the DFIG. The simulation results demonstrate robustness and accuracy of the proposed algorithm in estimating dynamic state variables.

[1]  Maarouf Saad,et al.  Dynamic state estimation of a permanent magnet synchronous generator-based wind turbine , 2016 .

[2]  Xingyu Wang,et al.  Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems , 2015 .

[3]  César A. Silva,et al.  Experimental sensorless vector control performance of a DFIG based on an extended Kalman filter , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

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

[5]  W. S. Mota,et al.  Matrix Method to Linearization and State Space Representation of Power Systems Containing Doubly Fed Induction Machines Operating as Wind Generators , 2006, 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America.

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

[7]  Xiao-Ping Zhang,et al.  Decentralized Nonlinear Control of Wind Turbine With Doubly Fed Induction Generator , 2008, IEEE Transactions on Power Systems.

[8]  B. Kalyan Kumar,et al.  Effect of Modeling of Induction Generator Based Wind Generating Systems on Determining CCT , 2013, IEEE Transactions on Power Systems.

[9]  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.

[10]  Atif Iqbal,et al.  Extended Kalman filter based speeds estimation of series-connected five-phase two-motor drive system , 2009, Simul. Model. Pract. Theory.

[11]  Roberto Cárdenas,et al.  Overview of control systems for the operation of DFIGs in wind energy applications , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[12]  Vedran Kirincic,et al.  A two-step hybrid power system state estimator , 2015 .

[13]  Jang-Mok Kim,et al.  Feedback linearization control of Doubly-fed induction generator under an unbalanced voltage , 2011, 8th International Conference on Power Electronics - ECCE Asia.

[14]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[15]  Shiyi Shao,et al.  Stator-Flux-Oriented Vector Control for Brushless Doubly Fed Induction Generator , 2009, IEEE Transactions on Industrial Electronics.

[16]  Mohsen Rahimi,et al.  Transient Performance Improvement of Wind Turbines With Doubly Fed Induction Generators Using Nonlinear Control Strategy , 2010, IEEE Transactions on Energy Conversion.

[17]  Luis Marroyo,et al.  Doubly Fed Induction Machine : Modeling and Control for Wind Energy Generation , 2011 .

[18]  R. Perini,et al.  An MRAS Observer for Sensorless DFIM Drives With Direct Estimation of the Torque and Flux Rotor Current Components , 2012, IEEE Transactions on Power Electronics.

[19]  Lassaâd Sbita,et al.  FDI based on an adaptive observer for current and speed sensors of PMSM drives , 2013, Simul. Model. Pract. Theory.

[20]  S. M. Muyeen,et al.  Stability Augmentation of a Grid-connected Wind Farm , 2008 .

[21]  Mohamed Jemli,et al.  High performance sensorless speed vector control of SPIM Drives with on-line stator resistance estimation , 2011, Simul. Model. Pract. Theory.

[22]  Mohieddine Jelali,et al.  Mathematical modelling and parameter identification of a stainless steel annealing furnace , 2016, Simul. Model. Pract. Theory.

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

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

[25]  Ali Keyhani,et al.  Smart Power Grids 2011 , 2012 .

[26]  K. Schneider,et al.  Feasibility studies of applying Kalman Filter techniques to power system dynamic state estimation , 2007, 2007 International Power Engineering Conference (IPEC 2007).

[27]  Wilfried Hofmann,et al.  Reactive Power Control Design in Doubly Fed Induction Generators for Wind Turbines , 2009, IEEE Transactions on Industrial Electronics.

[28]  Hesamoddin Marzooghi,et al.  Improving the performance of proton exchange membrane and solid oxide fuel cells under voltage flicker using Fuzzy-PI controller , 2012 .

[29]  Tadashi Koga,et al.  Real-time motion detection for high-assurance aircraft tracking system using Downlink Aircraft Parameters , 2016, Simul. Model. Pract. Theory.

[30]  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.

[31]  H. Madadi Kojabadi,et al.  Simulation and experimental studies of model reference adaptive system for sensorless induction motor drive , 2005, Simul. Model. Pract. Theory.

[32]  Arun G. Phadke,et al.  Synchronized Phasor Measurements and Their Applications , 2008 .

[33]  Mondher Farza,et al.  High gain observer based on-line rotor and stator resistances estimation for IMs , 2010, 2010 IEEE International Conference on Control Applications.

[34]  Greg Welch,et al.  A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation , 2014, IEEE Transactions on Sustainable Energy.

[35]  Srinivasan Krishnaswamy,et al.  State estimation of DFIG using an Extended Kalman Filter with an augmented state model , 2014, 2014 Eighteenth National Power Systems Conference (NPSC).