Influence of Magnetic Measurement Modeling on the Solution of Magnetostatic Inverse Problems Applied to Current Distribution Reconstruction in Switching Air Arcs

This paper presents a systematic analysis of the influence of magnetic measurement modeling on the regularized solution of magnetostatic inverse problems. The focus is on the application of the inverse problem technique to current distribution reconstruction of arc plasmas inside low-voltage switching devices. The inverse problems modeled with both the ideal sensor assumption and non-ideal sensor modeling are studied, considering the geometry and active area of sensors. The Tikhonov regularization is adopted to address the ill posedness of the inverse problem. Sensor arrays with random variations in sensor directions and positions are compared with arrays using perfectly aligned setup. The numerical results reveal that for mono-axial planar sensor arrays the ideal sensor assumption could be used to simplify the modeling process with serious environmental noises. Better reconstruction can be obtained using the non-ideal sensor model when the sensor variations are large.