Fault Analysis Of 10kV Rural Distribution Network Based On Grey Prediction

With the development of social economy,power users have higher and higher requirements for power supply reliability,taking into account the characteristics of the rural distribution network in Mengdong,especially when the power equipment is exposed to the natural environment of windy,thunder and rain in Inner Mongolia.In the rural distribution network,the overhead line is prone to fault trip,which will damage the power facilities and equipment.At this time,we urgently need a means to predict the important safety hazards in the distribution equipment in advance,and take preventive maintenance monitoring in advance to ensure The overall stability of the grid.This paper summarizes the causes and types of faults in distribution lines in recent years through the analysis of fault data in rural distribution networks.Secondly,it improves the quality of fault data by processing,mining and sorting the fault data of equipment,and then passes statistics.The data-driven method is used to derive the fault data for prediction,and introduces the concept of monthly correction smoothing coefficient.The processed data with time series features are processed to improve the accuracy of the gray algorithm,and then the gray algorithm is used for the equipment.The mechanism fault prediction,based on the prediction information,discusses the application scenarios of fault prediction methods,and finally summarizes and forecasts the fault prediction technology of distribution network equipment.According to the data analysis results,the simulated data meets the accuracy range of the gray prediction.The overall trend and short-term trend of the fault can be clearly determined through this prediction result,and the targeted maintenance plan is made for the maintenance plan.To strengthen the online monitoring and state estimation of key links and key equipment to achieve the purpose of early detection,early detection and early maintenance of distribution line equipment,and to optimize the human and material resources of rural distribution network.