Research on the Application of Artificial Intelligence in Fault Diagnosis of Power Systems
With the large-scale integration of new energy into power systems, the intermittency and volatility caused by the high penetration of renewable energy generation (such as wind power and photovoltaic power) require diagnostic systems to possess strong uncertainty-handling capabilities. Consequently, the complexity and uncertainty of power grid operation have increased significantly, posing unprecedented challenges to the safe, stable and economic operation of the power system. The traditional fault diagnosis and handling methods, which are based on fixed models and manual experience, can no longer adapt to the dynamic and complex operating characteristics of the new energy power grid, making it urgent to explore intelligent technical solutions. Traditional power grid fault diagnosis approaches rely mainly on expert experience and physical models. Model-based methods locate faults through state estimation and power flow calculation, whose accuracy heavily depends on model precision and parameter identification. However, under complex operating conditions such as high new energy penetration, grid topology changes, and frequent fluctuations in power supply and demand, establishing an accurate mathematical model that can cover all operating scenarios is extremely challenging—model mismatches often occur, leading to reduced fault diagnosis accuracy. Expert systems, on the other hand, integrate the operational experience of power grid engineers into rule bases, offering transparent reasoning processes that are easy to understand and verify. Yet, they suffer from inherent limitations: knowledge acquisition is time-consuming and labor-intensive, it is difficult to update rules in a timely manner with the iteration of grid technology, and they lack self-learning ability, making it impossible to adapt to new fault types and complex operating environments brought by new energy integration. When dealing with massive real-time data generated by the power grid (including new energy output data, load data, equipment monitoring data, and environmental data) and complex system environments, the following prominent problems frequently arise, which further restrict the efficiency and reliability of power grid operation and fault handling.