Toward an Uncertainty-Based Model Level Selection for the Simulation of Complex Power Systems

Simulation has become an indispensable support in the design process. This is particularly evident in the design of complex, multidomain, and multiphysics systems. In these cases in particular, though, the setup of the scheme to be simulated is a challenge in itself. In this paper, we propose an approach to the selection of the right level of detail of the models given the design objectives. This method is based on stochastic analysis and the results are lumped in indices that allow for a ranking of available models of each component. In detail, the main contribution is the formalization of a method to describe difference between different models of the same component. A case study is proposed to demonstrate the concept via the design of the overcurrent protection of an induction machine.

[1]  P. Corning Complexity Is Just a Word , 1998 .

[2]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[3]  Pierre-Olivier Amblard,et al.  A transient detector based on Malvar wavelets , 1996, OCEANS 96 MTS/IEEE Conference Proceedings. The Coastal Ocean - Prospects for the 21st Century.

[4]  Amara Lynn Graps,et al.  An introduction to wavelets , 1995 .

[5]  S. Manoochehri,et al.  Optimal design of electronic system utilizing an integrated multidisciplinary process , 2000 .

[6]  John A. Miller,et al.  Ontologies for modeling and simulation: issues and approaches , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[7]  D. Xiu Fast numerical methods for stochastic computations: A review , 2009 .

[8]  Antonello Monti,et al.  A neuro-fuzzy approach for the detection of partial discharge , 2001, IEEE Trans. Instrum. Meas..

[9]  Alfons G. Hoekstra,et al.  Multi-scale Modeling with Cellular Automata: The Complex Automata Approach , 2008, ACRI.

[10]  Roger A. Dougal,et al.  Uncertainty-Based Self Configuring Simulation for Design Support , 2011 .

[11]  A. Monti,et al.  Methods for partitioning the system and performance evaluation in power-hardware-in-the-loop simulations - Part II , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[12]  Ivan Tomov Dimov,et al.  A Quasi-Monte Carlo Method for Integration with Improved Convergence , 2001, LSSC.

[13]  Henrique S. Malvar Lapped transforms for efficient transform/subband coding , 1990, IEEE Trans. Acoust. Speech Signal Process..

[14]  Dongbin Xiu,et al.  High-Order Collocation Methods for Differential Equations with Random Inputs , 2005, SIAM J. Sci. Comput..

[15]  T.S. Ericsen Model based specifications for design , 2006, 2006 IEEE Power Engineering Society General Meeting.

[16]  Torbjörn Thiringer,et al.  Comparison of reduced-order dynamic models of induction machines , 2001 .

[17]  Reuven Y. Rubinstein,et al.  Simulation and the Monte Carlo method , 1981, Wiley series in probability and mathematical statistics.

[18]  Antonello Monti,et al.  Towards an Uncertainty-Based Model Level Selection for the Simulation of Complex Power Systems , 2010, 2010 Complexity in Engineering.

[19]  Nilay Shah,et al.  Comparison of Monte Carlo and Quasi Monte Carlo Sampling Methods in High Dimensional Model Representation , 2009, 2009 First International Conference on Advances in System Simulation.

[20]  T. Suwa,et al.  Multidisciplinary Electronic Package Design and Optimization Methodology Based on Genetic Algorithm , 2007, IEEE Transactions on Advanced Packaging.

[21]  S. Mallat A wavelet tour of signal processing , 1998 .

[22]  Manfred Krafczyk,et al.  Introducing Complex Automata for Modelling Multi-Scale Complex Systems , 2006 .

[23]  Richard J. Mayer,et al.  Using Ontologies for Simulation Modeling , 2006, Proceedings of the 2006 Winter Simulation Conference.

[24]  Antonello Monti,et al.  Stochastic based sensitivity function for model level selection in system simulation , 2010 .

[25]  Joshua A. Taylor,et al.  Uncertainty Analysis of Power Systems Using Collocation , 2008 .