Application and optimization of networked battery maintenance mode in power system

Traditional battery maintenance methods have some problems, such as low efficiency, difficult to find potential problems in time and accurately, which affect the stability and reliability of power system. This paper studies the application and optimization of networked battery maintenance mode in power system. By integrating sensor technology, network communication and data analysis, this mode realizes real-time monitoring and maintenance automation of battery state. Aiming at the challenges of lack of real-time and accuracy, optimization strategies such as deploying 5G base stations, adopting advanced algorithms and machine learning technologies are proposed. In addition, a big data analysis platform based on cloud computing is built, and machine learning and data mining are used to predict battery performance decline, and intelligent maintenance algorithms are developed. Experiments show that this mode improves voltage stability, capacity retention and internal resistance control, reduces maintenance cost and improves efficiency. These achievements provide theoretical and practical guidance for the stable operation of power system and efficient maintenance of battery.