Online estimation of remaining useful life of stator insulation

Abstract

Wide bandgap semiconductor devices in machine drive topologies are becoming more prevalent. These devices improve efficiency and can operate at higher switching frequencies. However, higher switching frequencies will increase the electrical stress applied to the insulation of the machine. Electrical stress, excessive heating, mechanical vibrations, and environmental contamination are leading factors that contribute to insulation degradation. Breakdown of the insulation will lead to a short circuit fault between two conductors. Short circuit faults can quickly propagate and lead to catastrophic failure. This project proposes an online method to accurately predict the remaining useful life (RUL) of the stator insulation by monitoring a trend in the leakage current. As the insulation degrades, the magnitude of the overshoot in the transient response of the leakage current exponentially decreases. An analog peak detection circuit is used to acquire this magnitude using low frequency sampling. An Extended Kalman Filter is applied to predict the RUL of the insulation. The proposed strategy will improve machine reliability, especially when using wide bandgap devices, while not requiring expensive or special equipment for assessing the health of the insulation.