A method of classified HV circuit breaker fault signal based on EEMD and BP neural network

During the process of high voltage circuit breaker run, the change of the vibration signal reflects the mechanical state of the circuit breaker, an efficient method of extracting vibration signal features is directly related to the accuracy and practicability of fault diagnosis. In the paper, a status feature extraction based on overall empirical mode decomposition (ensemble empirical mode decomposition EEMD) and correlation dimension has been presented. Firstly, the original non-stationary vibration signals are broken down to a plurality of stationary intrinsic mode function (IMF); Secondly, using GP algorithm to calculate the correlated dimensions of first four IMF as a high voltage circuit breaker vibration signal's feature vectors. Finally, constructing BP (back propagation) neural network to classify the feature vectors. Through testing different fault vibration signals of circuit breaker, it showed that the method can accurately diagnose all kinds of circuit breaker fault state and provide a new thinking way about fault diagnosis.