Design of Equipment Monitoring System and Anomaly Detection Model for Thermal Power Plants Assisted by AR

As the core component of the Electric Power System (EPS), thermal power plant equipment bears the responsibility of converting fossil fuel energy into electrical energy. Its stable and efficient operation is crucial for ensuring power supply. Therefore, the anomaly detection task of thermal power plant equipment is not only related to the economic benefits of the power plant, but also directly affects the energy security and stable operation of society. With the advancement of technology, augmented reality (AR) technology is gradually emerging in the industrial field with its unique advantages. AR technology provides users with an immersive interactive experience by calculating the position and angle of camera images in real-time and overlaying corresponding image information. This article combines AR technology with equipment monitoring in thermal power plants and proposes an innovative AR assisted equipment monitoring system for thermal power plants. The system not only utilizes AR technology to visually display equipment status, but also combines an anomaly detection model based on deep learning (DL) to achieve accurate identification and early warning of equipment failures in thermal power plants. The experimental results show that the AR assisted equipment monitoring system for thermal power plants can significantly improve the accuracy and efficiency of anomaly detection.