Research on Power Cable Fault Diagnosis Method Integrating LSTM and DBM
In the normal and safe operation of the power system,power cables are prone to faults caused by natural weather and human factors,which seriously disrupt the normal operation of power lines,reduce the normal transmission of power signals,and affect the stability and reliability of the power system.Based on this,an improved method for power cable fault diagnosis based on the fusion of long short-term neural networks and deep Boltzmann algorithm was designed.The power signal on the power cable was used as the data source for the input layer of the long short-term neural network for real-time collection and monitoring.The hidden layer of the neural network was used to process and identify various types of power signals,accurately extracting abnormal data when a fault occurred,and acting on the output layer for expression,improving the accuracy of power cable fault recognition.Furthermore,the deep Boltzmann algorithm was introduced to adjust the fault data recognition factor of the hidden layer,which improved the convergence speed of the long short-term neural network and the convergence characteristics of the algorithm.Finally,the power cable fault diagnosis method was applied to the power signal monitoring platform and compared with traditional BP neural networks and other methods.The improved long short-term neural network achieved a fault signal recognition rate of 97.3％and a fault signal diagnosis accuracy of 96.61％,which improved the fault recognition ability of traditional methods and provided important guarantees for the safe operation of the power system.
long short-term neural networkspower cablesdeep boltzmann algorithmfault diagnosis
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