Management system and optimal control for three-dimensional visualization and maintenance of thermal power plant

Abstract

With the evolution of energy pattern and the advancement of science and technology, the operation and maintenance management of thermal power plants has encountered bottlenecks. The traditional model is difficult to meet the current demand. The purpose of this study is to build an advanced three-dimensional (3D) visualization and maintenance system suitable for thermal power plants, and to optimize it with the technology of convolutional neural network (CNN). Firstly, literature research is carried out, and the achievements and existing shortcomings in related fields are deeply excavated. Then, this study systematically analyzes the operation and maintenance ecology of thermal power plants, focuses on equipment operation data trajectory and process flow context, and accurately anchors key pain points. Based on this, a basic 3D visualization and maintenance system is constructed. Its data acquisition and processing module is customized for thermal power generation conditions. It can accurately capture multiple data from core equipment such as boilers and steam turbines and integrate them efficiently. According to the actual situation and equipment details of the power plant, the 3D modeling module designs a highly realistic digital model. The visual interface module is user-experience-oriented, presenting an intuitive and convenient interactive window. It is convenient for operation and maintenance personnel to monitor and make efficient decisions in real time. Then, CNN technology is introduced to deeply analyze the data content and find out the operation and maintenance value. The experimental data shows the effectiveness, and the basic system performs well in the dimensions of accuracy, completeness and accuracy, with the numerical value exceeding 85%, which is more prominent than the traditional system. After optimization by CNN technology, the response time of the system is increased by 5%. The calculation cost is reduced by 15%, and the data throughput is increased by 13%. However, there is still room for improvement in the system. For example, the stability of data acquisition in complex electromagnetic and high-temperature environment needs to be strengthened. The calculation accuracy of the model for extreme working conditions and microscopic changes of equipment needs to be improved. The dimension of personalized customization of visual interface needs to meet the demands of multiple users. The system scalability needs to meet the requirements of technical iteration and equipment update, and the technical application process needs to be simplified for promotion. This study injects innovative vitality into the operation and maintenance management of thermal power plants, and significantly improves the quality and efficiency of operation and maintenance. Looking forward to the future, it is still necessary to test and analyze in many aspects and optimize in many dimensions to drive the operation and maintenance management of thermal power generation into a new intelligent field with science and technology engines.