A Bayesian Approach for Spherical Harmonic Expansion Identification: Application to Magnetostatic Field Created by a Power Circuitry

• Published in: IEEE transactions on electromagnetic compatibility Vol. 57; no. 6; pp. 1501 - 1509
• Main Authors: Pinaud, Olivier, Chadebec, Olivier, Rouve, Laure-Line, Coulomb, Jean-Louis, Guichon, Jean-Michel, Vassilev, Andrea
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Bayes theorem; Bayesian analysis; Computational modeling; Covariance matrices; Electric circuits; Electric power; Electric power generation; Electromagnetic compatibility; Engineering Sciences; Harmonic analysis; Inverse; inverse problem theory; Inverse problems; Magnetostatic fields; magnetostatics; Mathematical models; Numerical models; Random variables; random variables propagation; Sensors; Spherical harmonics; inverse problem theory; spherical harmonics; random variables propagation; magnetostatics; Bayes theorem
• ISSN: 0018-9375; 1558-187X
• DOI: 10.1109/TEMC.2015.2458353

This paper deals with the use of the Bayesian approach to inverse an underdetermined magnetostatic problem based on spherical harmonic expansion. Identification of the spherical harmonic coefficients is helped thanks to some a priori information. This information comes from a numerical model statistically studied to define an average-state vector and a covariance matrix. The whole approach is applied for the study of the magnetostatic field inside an electric vehicle, created by its power circuitry. It demonstrates the strength of merging a priori information and measured information in order to obtain an efficient identification of magnetic sources created by a complex set of conductors.