Research on Predicting Overheating Faults in Ring Main Unit Based on Multidimensional Monitoring Data
With the development of modern distribution networks,as the core equipment of the ring main unit,accurate prediction of overheating faults is crucial for ensuring the stable operation of the power supply system.Therefore,this paper proposes a method for predicting overheating faults in ring main units based on multidimensional monitoring data,aiming to improve the accuracy and timeliness of fault warning.By introducing discrimination indicators such as temperature rise,temperature difference,and relative temperature difference,combined with deep learning techniques,especially temperature prediction models based on long short term memory(LSTM)networks,the limitations of traditional temperature threshold based diagnostic methods are overcome.Using MATLAB platform for model simulation,the constructed model integrates multiple relevant data dimensions such as historical temperature,current,and environmental temperature,improving the accuracy and stability of prediction.The results show that compared with traditional BP neural networks,LSTM network has lower error and stronger adaptability in the prediction of overheating fault trend,which improves the accuracy of fault prediction,providing scientific basis for power grid fault diagnosis and maintenance work,and further ensuring the reliability of the power supply system.
multidimensional monitoring dataring main unitoverheat fault prediction
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