Deep Learning Based Circuit Breaker Non - Full - Phase Operation State Monitoring Method

To solve the problem of low accuracy of the traditional monitoring method, a deep learning based monitoring method is designed. A sensor is installed at the part that may cause the circuit breaker to run in non-full phase and the state data of the circuit breaker is collected through the sensor. Filter and digital-to-analog converter are used to reduce noise and digital-to-analog conversion of the signal transmitted by the sensor. After the data are standardized and normalized, input into the convolutional neural network. The parameters and weights of convolutional neural network are determined by training the sample data. After the training of convolutional neural network, the input neural network data are processed according to the determined parameters and weights. Output the processed results to the database, in the database classification algorithm after the data classification, sent to the circuit breaker operation control module, to achieve the circuit breaker operation status monitoring. Through the comparison with the traditional monitoring methods, it is verified that the designed deep learning-based state monitoring method has higher accuracy and can make up for the shortcomings of the traditional methods.