Integrating Machine Learning with Variable Frequency Drive for Enhanced Speed Control in Induction Motors

This study delves into the integration of Machine learning (ML) methods with variable frequency drives (VFDs) to enhance speed control in single-phase and three-phase in-duction motors. Our innovative technology utilizes an Arduino microcontroller to govern the VFD circuit, integrating wireless communication with a Python-based ML face detector. By identi-fying specific faces, the system transmits pertinent speed control signals to the microcontroller for motor operation. This novel integration represents a pioneering approach to industrial op-timization, fostering increased energy efficiency through precise speed regulation and resultant cost savings. Furthermore, our automated methodology significantly extends the motor's lifespan by optimizing the power factor and alleviating mechanical stress. By seamlessly blending ML with VFD technology, our approach not only revolutionizes industrial motor control but also contributes to sustainable and efficient industrial practices. More-over, our study provides insights into the results obtained from the facial detection system. Each identified face corresponds to specific motor frequencies, enabling tailored speed adjustments. The mapped frequencies include 27 hertz, 120 hertz, and 160 hertz, each representing distinct motor speeds as illustrated by the associated waveform. This detailed analysis underscores the efficacy of our integrated approach in achieving dynamic and precise speed control based on facial recognition cues.