Real-Time Monitoring of Three-Phase Unbalanced Devices and Real-Time Adjustment Analysis of Action Characteristics

This paper designs and implements a three-phase unbalanced monitoring and adjustment system based on advanced algorithms and model simulation. The system uses a variety of sensors and signal processing algorithms to achieve high-precision real-time monitoring of three-phase unbalanced parameters. By establishing a dynamic adjustment algorithm for action characteristics based on machine learning, the system can intelligently adjust the action characteristics of the three-phase unbalanced device according to real-time monitoring data to maintain the stability and efficiency of the system. The simulation platform uses MATLAB Simulink to establish a system model, and verifies the effectiveness and real-time response capability of the algorithm through multiple sets of simulation experiments. The experimental results show that the proposed system shows good adaptability under different loads and power grid environments, can effectively reduce the three-phase imbalance, and improve the power supply quality of the system. The real-time adjustment algorithm performs well in stability and response speed, and realizes the dynamic optimization of the action characteristics of the three-phase unbalanced device. This study provides innovative ideas and technical support for solving the three-phase imbalance problem in the power system.