Capacitor voltage transformers(CVT)are important primary voltage monitoring components,but due to the influence of ambient temperature,humidity,aging of the components and other factors caused by capacitor upper and lower arm breakdown in primary side of capacitor voltage transformer and short circuit in secondary side of the transformer and other faults,a light-weight AlexNet-based fault diagnosis method for capacitor voltage transformer is proposed.This method uses Matlab to build a CVT circuit model and simulates three typical faults,namely,capacitance breakdown of high-voltage arm,capacitance breakdown of low-voltage arm and short circuit in secondary side of the transformer.The voltage data in secondary side of CVT are collected and transformed into feature matrices using Markov transition fields.Finally the voltage feature matrices are classified into faults using a light-weight AlexNet neural network.The simulation experiments prove that the proposed method can accurately detect the fault type of CVT without removing the CVT.
capacitor voltage transformerfeature extractionAlexNet neural networkfault diagnosis
NETL
NSTL
维普
万方数据
