Research on Integration and Optimization of Intelligent Algorithm in Digital Twin Power Plant Control System

This paper aims to discuss the integration and optimization strategy of intelligent algorithms in digital twin power plant control system, with special attention to the application of transfer learning algorithm in improving system performance. Firstly, the paper introduces the basic framework of the digital twin power station control system, which realizes the real-time monitoring and predictive maintenance of power station equipment by simulating the running state of real power station. Then, it elaborates how transfer learning algorithm can be applied to this system to accelerate the learning and adaptation process under new environment by using existing data and models, so as to improve the response speed and accuracy of the system. In addition, the paper also discusses the design and implementation of digital twin substation framework, and emphasizes the importance of system simulation in verifying and optimizing control strategies. Through comparative experiments, it is proved that the digital twin power plant control system with integrated transfer learning algorithm has obvious advantages in energy efficiency management, fault diagnosis and operation optimization.