Reducing estimated parameters of a synchronous generator for microgrid applications

Synchronous generators are widely utilized in microgrids with high penetration of distributed renewable energy resources for small scale power generation. An accurate model of a synchronous generator is key to effective planning and operation of a grid-tied microgrid as well as stabilizing the frequency and regulating the voltage in an islanded microgrid. In this paper, a new strategy, based on the sensitivity trajectory analysis, for modeling a synchronous generator, which influences the transients of a microgrid greatly, is proposed. This method partitions the model parameters into significant and less significant sets. It is shown that in microgrid modeling, only the significant parameters need to be identified, and the remaining parameters can be replaced by typical values as they do not influence the model outputs critically. Reduction of the estimated parameters allows for modeling other components using on-line measurements, increases the reliability of the identified parameters and generalization capability of the characterizing model. The performance of the proposed approach is demonstrated by modeling a 5.3 MVA synchronous generator utilized in the San Diego State University microgrid.

[1]  Reza Sabzehgar A review of AC/DC microgrid-developments, technologies, and challenges , 2015, 2015 IEEE Green Energy and Systems Conference (IGESC).

[2]  S. Billings Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains , 2013 .

[3]  Lennart Ljung,et al.  Nonlinear black-box modeling in system identification: a unified overview , 1995, Autom..

[4]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[5]  I. Kamwa,et al.  Cross-Identification of Synchronous Generator Parameters From RTDR Test Time-Domain Analytical Responses , 2011, IEEE Transactions on Energy Conversion.

[6]  M. Mohamadian,et al.  Microgrid Dynamic Performance Improvement Using a Doubly Fed Induction Wind Generator , 2009, IEEE Transactions on Energy Conversion.

[7]  Mohsen Hamzeh,et al.  A new power management control strategy for a MV microgrid with both synchronous generator and inverter-interfaced distributed energy resources , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[8]  P.L. Dandeno,et al.  Development of Detailed Turbogenerator Equivalent Circuits from Standstill Frequency Response Measurements , 1981, IEEE Transactions on Power Apparatus and Systems.

[9]  P. Kundur,et al.  Power system stability and control , 1994 .

[10]  Scott D. Sudhoff,et al.  Analysis of Electric Machinery and Drive Systems , 1995 .

[11]  L. Ljung Approaches to identification of nonlinear systems , 2010, Proceedings of the 29th Chinese Control Conference.

[12]  J. A. Mallick,et al.  Modeling of Solid Rotor Turbogenerators Part II: Example of Model Derivation and Use in Digital Simulation , 1978, IEEE Transactions on Power Apparatus and Systems.

[13]  Chang-Ming Liaw,et al.  Position sensorless surface-mounted permanent-magnet synchronous generator and its application to power DC microgrid , 2015 .

[14]  Ian A. Hiskens,et al.  Trajectory Sensitivity Analysis of Hybrid Systems , 2000 .

[15]  M. A. Arjona,et al.  Parameter Estimation of a Synchronous-Generator Two-Axis Model Based on the Standstill Chirp Test , 2013, IEEE Transactions on Energy Conversion.

[16]  Kevin P. Schneider,et al.  Three-phase unbalanced transient dynamics and powerflow for modeling distribution systems with synchronous machines , 2016 .

[17]  Wei Deng,et al.  Investigation of the Dynamic Stability of Microgrid , 2014, IEEE Transactions on Power Systems.

[18]  M.E. Coultes,et al.  Synchronous Machine Models by Standstill Frequency Response Tests , 1981, IEEE Transactions on Power Apparatus and Systems.

[19]  Jianguo Jiang,et al.  On-line estimation of variable parameters of synchronous machines using a novel adaptive algorithm. Estimation and experimental verification , 1997 .