Quantitative Prediction Method for Distribution Power Grid Risk

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