This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Dynamic Surrogate Model Technique for Power Systems Modeling and Simulation
ISSN: 0148-7191, e-ISSN: 2688-3627
Published November 11, 2008 by SAE International in United States
Annotation ability available
Event: Power Systems Conference
Heterogeneous physical systems can often be considered as highly complex, consisting of a large number of subsystems and components, along with the associated interactions and hierarchies amongst them. The simulation of a large-scale, complex system can be computationally expensive and the dynamic interactions may be highly nonlinear. One approach to address these challenges is to increase the computing power or resort to a distributed computing environment. An alternative to improve the simulation computational performance and efficiency is to reduce CPU required time through the application of surrogate models. Surrogate modeling techniques for dynamic simulation models can be developed based on Recurrent Neural Networks (RNN).This study will present a method to improve the overall speed of a multi-physics time-domain simulation of a complex naval system using a surrogate modeling technique. For the purpose of demonstration, a small scale dynamic model of a power system has been developed as a monolithic implementation in Simulink®. The surrogate modeling technique will be evaluated by comparing time dependent responses of the surrogate against the original monolithic with respect to the approximation accuracy and computational performance.
|Technical Paper||The Computational Cost and Accuracy of Spray Droplet Collision Models|
|Technical Paper||CAE Model Validation in Vehicle Safety Design|
|Journal Article||A Parametric Optimization Study of a Hydraulic Valve Actuation System|
CitationBalchanos, M., Moon, K., Weston, N., and Mavris, D., "A Dynamic Surrogate Model Technique for Power Systems Modeling and Simulation," SAE Technical Paper 2008-01-2887, 2008, https://doi.org/10.4271/2008-01-2887.
- Suh N.P., Complexity: Theory and Applications, Oxford University Press, 2005.
- Simulation-Based Engineering Science Panel, Revolutionizing Engineering Science through Simulation, report, available at: http://www.nsf.gov/pubs/reports/sbes_final_report.pdf, 2006.
- Dunnington L., Garter G., and Stevens H., “Integrated Engineering Plant for Future Naval Combatants - Technology Assessment and Demonstration Roadmap,” Systems Engineering Group, Engineering Technology Center, Marine Technology Division, Anteon Corporation, 2003.
- Lively K.A., Scheidt D.H. and Drew K.F. “Mission Based Engineering Plant Control,” Reconfiguration and Survivability Symposium, 2005.
- Integrated Engineering Plant. URL: www.navy.mil [Date accessed June 2008].
- Mavris D.N., DeLaurentis D.A., Bandte O., Hale M.A., “A Stochastic Approach to Multi-disciplinary Aircraft Analysis and Design,” 36th Aerospace Sciences Meeting & Exhibit, Reno, NV, January 12-15, 1998.
- Hughes R., Balestrini S., Kelly K., Weston N.R., and Mavris D.N., “Modeling of an Integrated Reconfigurable Intelligent System (IRIS) for Ship Design,” Ships & Ship Systems (S3) Technology Symposium Change, Challenges & Constants, West Bethesda, MD, November 13-14, 2006.
- Weston N.R., Balchanos M.G., Koepp M.R., and Mavris D.N., “Strategies for Integrating Models of Interdependent Subsystems of Complex System-of-Systems Products,” IEEE Proceeding of the Thirty-Eighth Southeastern Symposium on Systems Theory, pp. 310-314, March 5, 2006.
- Balchanos M., Balestrini S., Weston N.R., and Mavris D.N., “Multi-Physics Time-Variant First-Order Model Integration of Complex Systems,” ASNE Automation and Control Conference, Biloxi, MS, 2007.
- Moon K., Weston N.R., and Mavris D.N., “A Method for Speeding up the Time-Domain Simulation of a Complex System Using Surrogate Modeling Technique,” ASNE Automation and Control Conference, Biloxi, MS, 2007.
- Myers R.H., Montgomery D.C., “Response Surface Methodology: Process and Product Optimization Using Designed Experiments,” 2nd ed., John Wiley & Sons, Inc., NY, February 2002.
- Karnopp D.C., Margolis D.L., and Rosenberg R.C., “System Dynamics: Modeling and Simulation of Mechatronic Systems,” 4th ed., John Wiley & Sons, Inc., Hoboken, NJ, 2006.
- Brown F.T., “Engineering System Dynamics: Control Engineering,” 1st ed., Marcel Dekker, Inc., New York, NY, 2001.
- Gawthrop P.J., and Bevan G.P. “Bond-Graph Modeling: A Tutorial Introduction for Control Engineers,” IEEE Controls Systems Magazine, vol. 27(2), pp. 24-45, April 2007.
- Kundur P., “Power System Stability and Control,” McGraw-Hill, Inc., NY, 1994.
- Sauer P.W., and Pai M.A. “Power System Dynamics and Stability,” Prentice Hall, NJ, 1998.
- Zivi E.L., and Youngs R. “Fluid-cooled power component heat exchanger model,” whitepaper, June 2005.
- Vasquez H., and Parker J.K. “A new simplified mathematical model for a switched reluctance motor in a variable speed pumping application,” Mechatronics, vol. 14(9), pp. 1055-1068, November 2004.
- Yoo K.; Simpson K.; Bell M., and Majkowski S., “An Engine Coolant Temperature Model and Application for Cooling System Diagnosis,” SAE 2000 World Congress, Detroit, MI, March 2000.
- Bellman R.E., Adaptive Control Processes: A Guided Tour, Princeton University Press, New York, 1961.
- Mandic D.P. and Chambers J.A., Recurrent neural networks for prediction: learning algorithms, architectures, and stability, Wiley, New York, 2001.
- NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/, September 2007.
- Owens A.J., “Empirical Modeling of Very Large Data Sets Using Neural Networks,” International Joint Conference on Neural Networks, 2000.
- Samarasinghe S., Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition, Auerbach Publications, Boca Raton, FL, 2007.
- SAS Institute Inc., JMP Design of Experiments Guide, SAS Institute Inc., Cary, NC, 2007.
- Seiffert U., “Training of Large-Scale Feed-Forward Neural Networks,” International Joint Conference on Neural Networks, 2006.
- Sola J. and Sevilla J., “Importance of Input Data Normalization for the Application of Neural Networks to Complex Industrial Problems,” IEEE Transaction on Nuclear Science, June 1997.
- Hecht-Nielson R., “Kolmogorov's Mapping Neural Network Existence Theorem,” IEEE International Conference on Neural Networks, vol. 3, pp. 11-13, 1987.
- Tsung F., “Modeling Dynamical Systems with Recurrent Neural Networks,” PhD Thesis, University of California, San Diego, 1994.
- Takens F., “Detecting Strange Attractors in Turbulence,” Dynamical Systems and Turbulence, vol. 898, pp. 366-381, 1981.