Application Of Data Mining In Fault Prediction Of Distribution Network

As one of China's economic pillars,the development of the power industry is related to China's social and people's livelihood.Whether or not it has advanced power technology has become a key factor affecting China's economic development.The prediction and diagnosis of distribution network failure has become a research in the power industry.The main research content of the field,among which the research on the diagnosis of distribution network faults is more favored by researchers,but the fault prediction of distribution network is also significant,and the prediction of distribution network failures can be predicted in a timely manner.It reduces the possibility of failure of the distribution network and reduces the waste of resources.Therefore,this paper proposes to apply data mining to fault prediction of distribution network,which has certain practical significance.Data mining uses valuable or semi-automatic methods to obtain valuable information in a large number of data with different attributes and different quality.Data mining technology can mine valuable information and rules in a large amount of data.The new data is predicted based on the information obtained.In this paper,the data mining technology is applied to the prediction of distribution network faults,and the fault prediction model of distribution network is constructed.Taking into account the arrival time of fault repair,etc.,the objective function is further optimized,and the simulated vegetative growth algorithm based on the fault prediction result is obtained.The parameter configuration and solution process based on the optimization algorithm,and the optimization result and analysis are obtained.It can be seen that applying data mining to the prediction of distribution network faults can greatly improve the prediction efficiency of distribution network faults,thereby reducing the possibility of faults in the distribution network and directly reducing the national economic losses.The paper has 26 pictures,19 tables,and 50 references.