Performance Analysis of Induction Motor Control Based on Variable Frequency Drive (VFD and Neural Network Under Load Variations

Abstract

The use of induction motors in industrial applications continues to grow due to their robustness, low cost, and minimal maintenance requirements. However, maintaining optimal performance under varying load conditions remains a challenge. This research aims to analyze the performance of an induction motor control system that integrates a Variable Frequency Drive (VFD) and a Neural Network-based controller to improve efficiency and dynamic response under different load variations. The VFD serves as a tool to regulate the motor’s frequency and voltage, enabling precise speed control. Meanwhile, the neural network algorithm is designed to learn and adapt in real time, adjusting control parameters according to load conditions to maintain stability and efficiency. Experimental tests were conducted by applying different load scenarios to the induction motor and recording key parameters such as motor speed, torque, current, and efficiency. The results showed that the integration of the neural network with the VFD enhanced the system's adaptability and performance, especially during sudden changes in load. Compared to traditional PID control, the proposed system provided faster response times, better energy utilization, and improved motor protection. This study demonstrates that a hybrid control system combining VFD and neural networks offers a promising solution for modern motor control systems requiring intelligent and adaptive behavior.