High-Frequency Impedance Parametric Modeling and Partial Discharge Validation for RUL Estimation of Thermally Aged Stator Insulation

International Conference on Electrical Machines (ICEM), pp. 1–7

Abstract

The degradation of stator winding insulation is a primary contributor to failures in inverter-fed machines. This study proposed a comprehensive condition-monitoring framework for stator winding subjected to a controlled, accelerated thermal ageing cycle. A high-frequency (HF) frequency response analysis (FRA) based on the proposed machine model was mapped with partial discharge (PD) to evaluate multiple degradation levels. The proposed machine model enabled accurate tracking of turn-to-turn and phase-to-ground capacitance variations, which are critical dielectric markers. Furthermore, PD analysis using inception voltage (PDIV) and PD charge was evaluated as a complementary discharge-based indicator. An Arrhenius–Miner thermal ageing model quantified cumulative insulation damage, with each ageing cycle contributing approximately 2.4 % to the total life consumption. The remaining useful life (RUL) decreased monotonically from 100% (healthy state) to 74 %, validating the enhanced early-stage sensitivity of FRA compared with conventional indicators. The validated model-based framework exhibited superior sensitivity to early-stage insulation degradation, thereby facilitating predictive maintenance strategies and providing a robust physics-based foundation for machine learning–assisted, model-driven prognostics.

Authors 7

1. Muhammad Usman Sardar

   Tallinn University of Technology

   Affiliation as printed

   Tallinn University of Technology

2. Toomas Vaimann

   Tallinn University of Technology

   Affiliation as printed

   Tallinn University of Technology

3. Lauri Kütt

   Tallinn University of Technology

   Affiliation as printed

   Tallinn University of Technology

4. Bilal Asad

   Tallinn University of Technology

   Affiliation as printed

   Tallinn University of Technology

5. Ants Kallaste

   Tallinn University of Technology

   Affiliation as printed

   Tallinn University of Technology

6. Hujun Peng

   Tallinn University of Technology

   Affiliation as printed

   Tallinn University of Technology

7. Simon Steentjes Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University