A Hybrid Physics-Based and Data-Driven Approach for Monitoring of Inverter-Fed Machine Stator Insulation Degradations Using Switching Oscillations

Inverter-fed machines are widely used in many important industrial applications. However, the machine stator groundwall and/or turn insulation are prone to failure suffering the inverter's high dv/dt. It is necessary to identify them as early as possible but challenging due to their coupled and weak symptoms. In this article, a hybrid physics-based and data-driven approach is proposed to monitor insulation degradations. First, the physical mechanism analysis is carried out to obtain high-quality data sensitive to stator insulation conditions, i.e., the high-frequency common-mode switching oscillations. Then, the continuous wavelet transform is used to extract the time-frequency features of switching oscillations. Finally, an improved convolutional neural network is designed for the groundwall and/or turn insulation faults diagnosis. The experimental results on a 3 kW permanent magnet synchronous motor drive system demonstrate the effectiveness of the proposed method for multiple faults diagnosis with excellent sensitivity, accuracy, and robustness.

I. Introduction

Inverter-fed machines are commonly employed in various safety-critical systems, including electric vehicles, offshore wind turbines, high-speed trains, and nuclear power plants. Statistical data indicates that approximately 30% of all failures are linked to stator winding insulation, which is a frequent issue encountered in electrical machines [1], [2], [3]. In actual inverter-fed systems, transient overvoltages induced by high rates of change in voltage (dv/dt) may expedite the insulation aging process [4], usually beginning at a vulnerable point in the winding groundwall or turn insulation [5]. In the event of an initial fault, it could rapidly evolve into a catastrophic failure within a short timeframe [6]. Therefore, the early detection of initial degradation in the ground wall or turn insulation of an inverter-fed machine is of paramount importance.