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Course# EE-607: Advanced Methods for Model Identification

Summary

This course introduces the principles of model identification for non-linear dynamic systems, and provides a set of possible solution methods that are thoroughly characterized in terms of modelling assumptions and uncertainty levels.

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Instructors (2)

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Mario Paolone

Mario Paolone received the M.Sc. (with honors) and the Ph.D. degree in electrical engineering from the University of Bologna, Italy, in 1998 and 2002, respectively. In 2005, he was appointed assistant professor in power systems at the University of Bologna where he was with the Power Systems laboratory until 2011. In 2010, he received the Associate Professor eligibility from the Politecnico di Milano, Italy. Since 2011 he joined the Swiss Federal Institute of Technology, Lausanne, Switzerland, where he is now Full Professor, Chair of the Distributed Electrical Systems laboratory and Head of the Swiss Competence Center for Energy Research (SCCER) FURIES (Future Swiss Electrical infrastructure). He was co-chairperson of the technical programme committees of the 9th edition of the International Conference of Power Systems Transients (IPST 2009) and of the 2016 Power Systems Computation Conference (PSCC 2016). He was chair of the technical programme committee of the 2018 Power Systems Computation Conference (PSCC 2018). In 2013, he was the recipient of the IEEE EMC Society Technical Achievement Award. He was co-author of several papers that received the following awards: best IEEE Transactions on EMC paper award for the year 2017, in 2014 best paper award at the 13th International Conference on Probabilistic Methods Applied to Power Systems, Durham, UK, in 2013 Basil Papadias best paper award at the 2013 IEEE PowerTech, Grenoble, France, in 2008 best paper award at the International Universities Power Engineering Conference (UPEC). He was the founder Editor-in-Chief of the Elsevier journal Sustainable Energy, Grids and Networks and was Associate Editor of the IEEE Transactions on Industrial Informatics. His research interests are in power systems with particular reference to real-time monitoring and operation, power system protections, power systems dynamics and power system transients. Mario Paolone is author or coauthor of over 300 scientific papers published in reviewed journals and international conferences.

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Nonlinear system

In mathematics and science, a nonlinear system (or a non-linear system) is a system in which the change of the output is not proportional to the change of the input. Nonlinear problems are of interest to engineers, biologists, physicists, mathematicians, and many other scientists since most systems are inherently nonlinear in nature. Nonlinear dynamical systems, describing changes in variables over time, may appear chaotic, unpredictable, or counterintuitive, contrasting with much simpler linear systems.

Dynamical system

In mathematics, a dynamical system is a system in which a function describes the time dependence of a point in an ambient space, such as in a parametric curve. Examples include the mathematical models that describe the swinging of a clock pendulum, the flow of water in a pipe, the random motion of particles in the air, and the number of fish each springtime in a lake. The most general definition unifies several concepts in mathematics such as ordinary differential equations and ergodic theory by allowing different choices of the space and how time is measured.

Linear system

In systems theory, a linear system is a mathematical model of a system based on the use of a linear operator. Linear systems typically exhibit features and properties that are much simpler than the nonlinear case. As a mathematical abstraction or idealization, linear systems find important applications in automatic control theory, signal processing, and telecommunications. For example, the propagation medium for wireless communication systems can often be modeled by linear systems.

Linear least squares

Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least squares include inverting the matrix of the normal equations and orthogonal decomposition methods. The three main linear least squares formulations are: Ordinary least squares (OLS) is the most common estimator.

Measurement uncertainty

In metrology, measurement uncertainty is the expression of the statistical dispersion of the values attributed to a measured quantity. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty, such as the standard deviation. By international agreement, this uncertainty has a probabilistic basis and reflects incomplete knowledge of the quantity value. It is a non-negative parameter.