Showing papers by "Robert Fischl published in 1993"
TL;DR: An integrated decision support system is described based on sensor fusion techniques, used for assessing the security of power systems, and is illustrated for detecting static voltage collapse by fusing the security information from a set of existing security indices.
Abstract: An integrated decision support system is described based on sensor fusion techniques, used for assessing the security of power systems. The integrated decision support system fuses information from various approximated system performance (ASP) models to minimize the risk of making the wrong decision under changing operating conditions. It uses the classification decisions provided by different ASP models together with information about their statistical performance (e.g., probabilities of misclassifications) to synthesize the globally optimal decision in the Bayesian risk sense. This global decision is often superior (and in no case inferior) to the one obtained using any single ASP model. The design of the integrated decision support system is illustrated for detecting static voltage collapse by fusing the security information from a set of existing security indices. >
9 citations
01 Dec 1993
TL;DR: In this article, a procedure for obtaining an uncertainty model for real uncertain parameter problems in which the uncertain parameters can be represented in a multilinear form is presented, and the procedure is formulated such that the resulting uncertainty model is minimal (or near minimal) relative to a given state space realization of the system.
Abstract: Viewgraphs and a paper on parametric uncertainty modeling for application to robust control are included. Advanced robust control system analysis and design is based on the availability of an uncertainty description which separates the uncertain system elements from the nominal system. Although this modeling structure is relatively straightforward to obtain for multiple unstructured uncertainties modeled throughout the system, it is difficult to formulate for many problems involving real parameter variations. Furthermore, it is difficult to ensure that the uncertainty model is formulated such that the dimension of the resulting model is minimal. A procedure for obtaining an uncertainty model for real uncertain parameter problems in which the uncertain parameters can be represented in a multilinear form is presented. Furthermore, the procedure is formulated such that the resulting uncertainty model is minimal (or near minimal) relative to a given state space realization of the system. The approach is demonstrated for a multivariable third-order example problem having four uncertain parameters.
1 citations