The safe and stable operation of the distribution network is crucial to the power system

and traditional fault detection methods have problems of lagging response and low prediction accuracy. This paper proposes fault prediction and prevention strategies based on artificial intelligence

builds machine learning and deep learning models for typical failures of power distribution networks

and improves prediction accuracy. Through feature engineering

data training and optimization

efficient fault warning is realized. Combined with intelligent scheduling and optimized prevention strategies

the reliability of the system has been enhanced. Research shows that this method can effectively improve the accuracy of fault prediction

reduce the occurrence of accidents

improve the discontinuities of power supply

and provide technical support for the construction of smart grids.