A Data Driven Energy Efficient Control of Induction Motor Drive with Vector Control

This paper introduces a novel online energy optimization algorithm for field-oriented control of induction motor (IM) drives. The control strategy incorporates an artificial neural network in the outer loop of the control technique to determine the optimized flux component that meets load demands while maintaining the motor's desired speed. Under light-load conditions, this approach significantly reduces the drive losses, thereby decreasing the motor's input power consumption making it ideal for energy-efficient systems. The proposed scheme ensures maximum efficiency with high accuracy, low computational burden, and fast dynamic response. Moreover, the scheme incurs no additional costs, enhancing its practicality and performance in manufacturing, transportation, and energy management sectors. The proposed controller is rigorously tested in MATLAB/Simulink to validate its high dynamic response and optimal efficiency with a 2.2 kW IM drive.