Application of Intelligent Knowledge Graph with Emergency Fault Diagnosis for Power Maintenance

With the development of society and the growth of electricity demand, the complexity and scale of the power grid are also constantly expanding, which makes the diagnosis and handling of power grid faults more difficult. The traditional emergency fault diagnosis methods for power maintenance often rely on manual experience and fixed rules, making it difficult to cope with complex and ever-changing fault scenarios. In order to improve the stability and safety of the power grid, it is necessary to research more intelligent and automated fault diagnosis methods. This article analyzes the functional structure of the application of knowledge graph in the study and judgment of power supply emergency faults. Combining the causes of faults and the exploration of fault prediction and prevention, the following conclusion is drawn: the preventive maintenance of the power supply emergency fault study and judgment system can prevent the occurrence of faults in 75% of cases, and the average response time is reduced by about 5 minutes. The research innovatively integrates knowledge graph technology, real-time data processing, and intelligent decision-making algorithms, providing efficient and intelligent solutions for emergency fault diagnosis in the power system.