Uncertainty propagation of iron loss from characterization measurements to computation of electrical machines
TypeArticles dans des revues avec comité de lecture
The aim of the research is to find out how uncertainties in the characterization of magnetic materials propagate through identification and numerical simulation to the computation of iron losses in electrical machines. Design/methodology/approach The probabilistic uncertainties in the iron losses are modelled with the spectral approach using chaos polynomials. The Sobol indices are used for the global sensitivity analysis. The machine is modelled with a 2D finite element method and the iron losses are computed with a previously developed accurate method. Findings The uncertainties propagate in different ways to the different components of losses, i.e. eddy current, hysteresis, and excess losses. The propagation is also different depending on the investigated region of the machine, i.e. Stator or rotor teeth, yokes, tooth tips. Research limitations/implications The method does not account for uncertainties related to the manufacturing process, which might result in even larger variability. Practical implications A major implication of the findings is that the identification of iron loss parameters at low frequencies does not affect the loss variability. The identification with high frequency measurement is very important for the rotor tooth tips. The variability in the excess loss parameters is of low impact. Originality/value The presented results are of importance for the magnetic material manufacturers and the electrical machine designers. The manufacturers can plan the measurement and identification procedures as to minimize the output variability of the parameters. The designers of the machine can use the result and the presented procedures to estimate the variability of their design
Fichier(s) constituant cette publication
Cette publication figure dans le(s) laboratoire(s) suivant(s)
Visualiser des documents liés par titre, auteur, créateur et sujet.
Global Parameters Sensitivity Analysis and Development of a Two-Dimensional Real-Time Model of Proton-Exchange-Membrane Fuel Cells ZHOU, Daming; NGUYEN, Thu; BREAZ, Elena; ZHAO, Dongdong; CLENET, Stéphane; GAO, Fei (Elsevier, 2018)This paper presents a 2-D real-time modeling approach for a proton-exchange-membrane fuel cell (PEMFC). The proposed model covers multi-physical domains for both ﬂuidic and electrochemical features, which considers in ...
Uncertainty quantification and sensitivity analysis in electrical machines with stochastically varying machine parameters OFFERMANN, Peter; MAC, Hung; NGUYEN, Thu Trang; CLENET, Stéphane; DE GERSEM, Herbert; HAMEYER, Kay (IEEE, 2015)Electrical machines that are produced in mass production suffer from stochastic deviations introduced during the production process. These variations can cause undesired and unanticipated side-effects. Until now, only worst ...
Stochastic Metamodel for Probability of Detection Estimation of Eddy-Current Testing Problem in Random Geometric ABDELLI, Djamel Eddin; NGUYEN, Thanh Hung; CLENET, Stéphane; CHERIET, Ahmed (Institute of Electrical and Electronics Engineers (IEEE), 2019)The calculation of the Probability Of Detection (POD) in Non Destructive Eddy Current Testing requires the solution of a stochastic model requiring numerous calls of a numerical model leading to a huge computation time. ...
Benefits of Waveform Relaxation Method and Output Space Mapping for the Optimization of Multirate Systems PIERQUIN, Antoine; BRISSET, Stéphane; HENNERON, Thomas; CLENET, Stéphane (2014)We present an optimization problem that requires to model a multirate system, composed of subsystems with different time constants. We use waveform relaxation method in order to simulate such a system. But computation time ...
DENG, Siyang; BRISSET, Stéphane; CLENET, Stéphane (Emerald, 2018)This paper compares different probabilistic optimization methods dealing with uncertainties. Reliability-Based Design Optimization is presented as well as various approaches to calculate the probability of failure. They ...