Research on intelligent power grid operation and maintenance and fault prediction system based on artificial intelligence

The growing complexity of power systems and the heightened need for stable power supply have made the study and creation of intelligent grid operation, maintenance, and fault prediction systems a critical technical priority. This study seeks to devise a sophisticated AI technology framework that seamlessly merges convolutional neural networks (CNN) and recurrent neural networks (RNN) for the real-time monitoring, smart diagnosis, and advance warning of faults in power grid equipment. This system first focuses on the collection and processing of large-scale power grid operation data, including equipment key parameters, environmental conditions, operation history and other dimensions of information. Big data analysis technology is used for efficient data cleaning, integration and deep mining, and then a detailed and accurate power grid operating state model is built. The core is to use convolutional neural network to capture spatial and local features in equipment data, and recurrent neural network to process time series data. The organic combination of the two can realize real-time monitoring of the operating status of power grid equipment and accurate assessment of its health status, so as to achieve early identification and accurate positioning of potential faults. At the same time, the system relies on the machine learning prediction model, especially the combination of historical fault cases and real-time monitoring data, and uses the rich features generated by CNN and RNN to make probabilistic prediction of the future fault possibility, providing scientific data support and decision-making reference for preventive maintenance measures. The intelligent operation and fault prediction system, which leverages both convolutional neural network and recurrent neural network, will significantly enhance the efficiency of power grid operation and maintenance. By lowering operational costs and mitigating the risk of significant power outages at their origin, it ensures the power grid system operates safely and reliably.