A polynomial chaos approach to measurement uncertainty

Measurement uncertainty is traditionally represented in the form of expanded uncertainty as defined through the Guide to the Expression of Uncertainty in Measurement (GUM). The International Organization for Standardization GUM represents uncertainty through confidence intervals based on the variances and means derived from probability density functions. A new approach to the evaluation of measurement uncertainty based on the polynomial chaos theory is presented and compared with the traditional GUM method.     

A virtual environment for remote testing of complex systems

Complex systems, realized by integration of several components or subsystems, pose specific problems to simulation environments. It is, in fact, desirable to simulate the complex system altogether, and not component by component, since the operation of the single part depends on the surrounding system and an early verification can prevent damages and save time for modifications. The availability of detailed and validated models of the single parts is therefore critical. This task may be difficult to achieve. In fact, in industrial applications, where a system can be a mix of different devices produced by different manufacturers, the physical device may not be accessible to the modeler for proprietary or safety concerns. Starting from this point, the idea of creating a virtual environment able to test the real single component remotely, employing simulators with remote signal processing capability, has been considered. A methodology for remote model validation is presented. The effectiveness of the approach is experimentally verified locally and remotely. For the remote testing, in particular, the physical device under test is located at the Politecnico di Milano, Italy, and the Virtual Test Bed model is located at the University of South Carolina.     

Automatic synthesis of uncertain models for linear circuit simulation: A polynomial chaos theory approach

A generalized and automated process for the evaluation of system uncertainty using computer simulation is presented. Wiener–Askey polynomial chaos and generalized polynomial chaos expansions along with Galerkin projections, are used to project a resistive companion system representation onto a stochastic space. Modifications to the resistive companion modeling method that allow for individual models to be produced independently from one another are presented. The results of the polynomial chaos system simulation are compared to Monte Carlo simulation results from PSPICE and C++. The comparison of the simulation results from the various methods demonstrates that polynomial chaos circuit simulation is accurate and advantageous. The algorithms and processes presented in this paper are the basis for the creation of a computer-aided design (CAD) simulator for linear networks containing uncertain parameters.     

A Decentralized Observer for Ship Power System Applications: Implementation and Experimental Validation

In the last few years, the growing complexity of the electrical power networks, mainly due to the increased use of electronic converters together with the requirements of a higher level of reliability and security, pushed the development of new techniques for the state estimation of the power systems. In this paper, the authors focus their attention on the implementation and experimental validation of a decentralized observer for the state estimation in an electric ship, whose power network is characterized by fast dynamics and by the presence of many electronic devices. The proposed solution implements a decentralized information filter (DIF).      

Indirect Measurements via a Polynomial Chaos Observer

This paper proposes an innovative approach to designing algorithms for indirect measurements based on a polynomial chaos observer (PCO). A PCO allows the introduction and management of uncertainty in a process. The structure of this algorithm is based on the standard closed-loop structure of the observer that is originally introduced by Luenberger. This structure is extended here to formally include uncertainty in the measurement and in the model parameters. Possible applications of this structure are discussed     

A neurofuzzy application for AC motor drives monitoring system

Currently industrial applications require suitable monitoring systems able to identify any decrease in efficiency resulting in economic losses. This paper shows that the information coming from a general purpose monitoring system can be usefully exploited to realize a sensorless instrument for the monitoring of an ac motor drive, and can be fed to a diagnostic tool for providing useful risk coefficients. The method is based on digital processing of the line signals acquired by means of a virtual instrument. The employed wavelet algorithms have been implemented within a Matlab environment, and risk coefficients are generated by means of suitable neurofuzzy algorithms.     

Maximum Entropy Multivariate Analysis of Uncertain Dynamical Systems Based on the Wiener–Askey Polynomial Chaos

Many measurement models are formalized in terms of a stochastic ordinary differential equation that relates its solution to some given observables. The expression of the measurement uncertainty for the solution that is evaluated at some time instants requires the determination of its (joint) probability density function. Recently, the polynomial chaos theory (PCT) has been widely recognized as a promising technique in order to address the problem. The uncertainty estimation via PCT requires the use of a Monte Carlo integration sampling strategy. In this paper, a novel approach will be presented in order to achieve PCT uncertainty estimation on the basis of an analytical methodology, requiring only optimization calculus