David J. Gritter

Neural network based on-line stator winding turn fault detection for induction motors

A novel on-line neural network based diagnostic scheme, for induction machine stator winding turn fault detection, is presented. The scheme consists of a feed-forward neural network combined with a self-organizing feature map (SOFM) to visually display the operating condition of the machine on a two-dimensional grid. The operating point moves to a specific region on the map as a fault starts developing and can be used to alert the motor protection system to an incipient fault. This is a useful tool for commercial condition monitoring systems. Experimental results are provided, with data obtained from a specially wound test motor, to illustrate the robustness of the proposed turn fault detection scheme. The new method is not sensitive to unbalanced supply voltages or asymmetries in the machine and instrumentation.

Soft starter inside delta motor modeling and its control

The paper presents a simulation of a thyristor based reduced voltage starter operating inside a delta connected induction motor. Behavior of the reduced voltage starter is shown to be different when operated inside the delta than when operated in the AC lines. Simulations of conventional control strategies based on delay of firing from voltage zero crossings (alpha control) and delay of firing from current zero crossings (gamma control) are presented and control problems are described. A control strategy based on combining these two methods is presented, along with a simplified current limit control algorithm.