Online monitoring of marine turbine insulation condition based on high frequency models: Methodology for finding the "best" identification protocol

Abstract

This paper investigates the online monitoring of electrical machine winding insulation systems based on the parametric modeling and identification. The proposed method consists in monitoring the drift of diagnostic indicators built from in-situ estimation of high-frequency electrical model parameters. The involved model structures are derived from the RLC network modeling of the winding insulation. Because they often present an important modeling noise, we propose to use the output error method not only to estimate the model parameter values but also to evaluate their uncertainty. This approach is based on the numerical integration of the model sensitivity functions. The so-called global identification scheme is coupled with an optimization algorithm that brings the best combination of any diagnostic model structure and its excitation protocol usable in operating conditions. Experimental data recorded from an industrial wound machines are used to illustrate the methodology.