Application of Energy Spectrum Entropy Vector Method and RBF Neural Networks Optimized by the Particle Swarm in High-voltage Circuit Breaker Mechanical Fault Diagnosis

High voltage vacuum circuit breaker is an important high voltage appliance of power system switchgears,and 80% of failure of the high voltage circuit breaker is due to its poor mechanical properties.Thus,using the signal energy spectrum entropy as character input vectors,we established a fault identification system model of the high voltage circuit breaker,which was based on particle swarm optimization by radial basis function neural network,and analyzed vibration signals of high-voltage circuit breakers through the wavelet packet.Moreover,we obtained and analyzed the actual vibration signals of high-voltage circuit breaker.Experimental results show that each element of the spectrum entropy vector of normal signal of high-voltage circuit breakers is evenly distributed,while the elements of fault signal spectrum entropy vector remarkably regularly change.The accuracy and precision of RBF network model which is optimized by particle swarm are higher than those of the traditional neural network model.Experimental results also show that the method for high voltage circuit breaker fault diagnosis is feasible and effective,and can provide a better theoretical basis for fault diagnosis of circuit breakers.