Automatic Fault Prediction Method of EV Charging Pile Based on Deep Learning
The conventional automatic prediction node form of EV charging pile fault is generally fixed-point orientation,and the prediction efficiency is limited,resulting in the final average fault interval prediction times are reduced.The design and verification analysis of the automatic fault prediction method for EV charging pile based on deep learning are proposed.Firstly,based on the current demand for automatic prediction,cross testing is adopted to break the limitations of prediction efficiency and achieve automatic prediction node cross deployment and fault feature extraction.Then,based on this,the LSTM neural network in deep learning is used to construct an automatic prediction model for EV charging pile faults,and adaptive verification processing is adopted to achieve automatic prediction.Finally,for the selected 5 charging pile groups,3 sets of virtual fault instructions were imported in order,and the average number of predicted fault intervals reached 15 or more.The results indicate that this automatic prediction method can achieve automatic prediction and recognition of EV charging pile faults in complex environments,and has practical application significance.
deep learningEVcharging pilefault monitoringautomatic prediction method
NETL
NSTL
万方数据
