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The electric power distribution grid directly serves the ordinary users, the stability and reliability of which are crucial to production and life. The traditional operation and maintenance of the distribution grid is based on experience and cannot scientifically predict the state of line faults. In this paper, stepwise regression is used to screen out 8 key influencing factors of line faults, and principal component analysis is performed to find the risk value of each line; so that establishes multiple linear and exponential regression equations for the line risk value. According to the existing data, the risk of the line is predicted, and the GABP neural network model is built so as to accurately predict the risk value of the line.

The electric power distribution grid is directly oriented to the majority of the ordinary users. Traditional operation and maintenance are performed mainly based on experience, which disable to rationally evaluate the status of the line and predict faults. Based on big data, the risk of the line is evaluated through principal component analysis in this paper, so that a machine learning algorithm is carried out to calculate the risk value of the distribution grid line unit. Finally, GA-BP neural network is used to build a line risk value prediction model for improvement.