Fault location method of Internet of Things power equipment based on BP neural network algorithm
The current IoT power equipment fault location models are mostly intelligent structures with relatively single positioning methods,resulting in uncontrollable deviations in the final positioning results.To address this issue,the article conducts research on fault location methods for IoT power equipment based on BP neural network algorithm.The article first performs data preprocessing and equipment fault feature extraction,then uses the BP neural network algorithm to improve the overall efficiency of power fault localization.Finally,a BP neural network is constructed to calculate the power equipment fault localization model,and an adaptive interval verification method is used to achieve fault localization processing.The test results show that compared with traditional low-voltage pulse power equipment fault positioning methods and traditional GRU power equipment fault positioning methods,the proposed power equipment fault positioning method has a lower misjudgment rate and higher positioning accuracy,which has certain application value.
BP neural networkInternet of Thingspower equipmentfault identificationremote abnormal sensing
万方数据
维普
