Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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01 Jan 1999
TL;DR: This contribution discusses the aspects of model validation in the light of model error models that are explicit descriptions of the model error, which allows a better visualization of the possible deficiencies of the nominal model.
Abstract: To validate an estimated model and to have a good understanding of its reliability is a central aspect of System Identification. This contribution discusses these aspects in the light of model error models that are explicit descriptions of the model error. A model error model is implicitly present in most model validation methods, so the concept is more of a representation form than a set of new techniques. Traditional model validation is essentially a test of whether the confidence region of the model error model contains the zero model. However, the model error model allows a better visualization of the possible deficiencies of the nominal model. Based on such information, the nominal model may very well be accepted even if the model error model does not contain the zero model. Conversely, it will be illustrated that the model error model may give good reason - because of if its more precise infomation - to reject a nominal model, that has passed a conventional model validation test.
126 citations
TL;DR: In this paper, a new method for estimating parameters of non-linear parametric models that uses internal feedback to account for nonlinearities is presented, and the method estimates the linear frequency response matrix and nonlinear system parameters at forced and unforced degrees of freedom of general multiple-degree-of-freedom nonlinear systems simultaneously.
126 citations
TL;DR: This work investigates implementation of algorithms for solving the hyper-parameter estimation problem that can deal with both large data sets and possibly ill-conditioned computations and proposes a QR factorization based matrix-inversion-free algorithm to evaluate the cost function in an efficient and accurate way.
126 citations
TL;DR: This paper reviews over hundred articles related to the application of BSS and their variants to output-only modal identification and concludes with possible future trends in this area.
126 citations
TL;DR: In this article, stochastic subspace identification is employed to extract the critical mode(s) from the measured ambient noise without requiring artificial disturbances (e.g., line outages, generator tripping, and adding/removing loads), so that the identified critical mode may be used as an online index to predict the closest oscillatory instability.
Abstract: Determining stability limits and maximum loading margins in a power system is important and can be of significant help for system operators for preventing stability problems In this paper, stochastic subspace identification is employed to extract the critical mode(s) from the measured ambient noise without requiring artificial disturbances (eg, line outages, generator tripping, and adding/removing loads), so that the identified critical mode may be used as an online index to predict the closest oscillatory instability The proposed index is not only independent of system models and truly represents the actual system, but it is also computationally efficient The application of the proposed index to several realistic test systems is examined using a transient stability program and PSCAD/EMTDC, which has detailed models that can capture the full dynamic response of the system The results show the feasibility of using the proposed identification technique and index for online detection of proximity to oscillatory stability problems
125 citations