Monitoring the Operational Status of Hydropower Station Equipment Based on Digital Twin and IoT Technology

Due to the complex operating environment and high long-term workload of hydropower station equipment, there is a certain probability of failure. If the operation status of hydropower station equipment cannot be monitored in a timely manner, it will increase the risk of sudden failures of hydropower station equipment, which may lead to equipment shutdown, accidents, and even casualties. Therefore, a monitoring method for the operational status of hydropower station equipment based on digital twin and Internet of Things technology has been proposed. Building a monitoring architecture for the operation status of hydropower station equipment based on digital twins, using Internet of Things technology to collect the operation status signals of hydropower station equipment, and using local wave decomposition method to eliminate noise in the operation status signals of hydropower station equipment. On this basis, probabilistic neural networks are used to monitor the operational status of hydropower station equipment. The experimental results show that the proposed method has high accuracy and efficiency in monitoring the operational status of hydropower station equipment.