Defect Identification of Substation Secondary Equipment Based on Multi Strategy Particle Swarm Optimization Algorithm
The long-term operation of substation secondary equipment may lead that the protection degree of primary equipment gradually weakens and defects are exposed.Aiming at this problem,a defect identification method of substation secondary e-quipment based on multi strategy particle swarm optimization algorithm is proposed.The vibration acceleration sensor is used to collect the vibration signal when the secondary equipment is running.Seven characteristic parameters are extracted from the vibration signal,including extreme value difference,signal static change degree,signal vibration intensity,signal distribution form deviation degree,signal deviation from normal distribution degree,signal time domain waveform factor and signal pulse factor.A fitness function is constructed,and the fitness value of each particle is calculated by multi strategy particle swarm op-timization algorithm.According to the maximum fitness value,the defect factors of substation secondary equipment are corre-sponding one by one to complete the defect type identification of substation secondary equipment.Testing results show when the the method is applied,the defect type identified by the relay is contact looseness and cracking,the defect type identified by the fuse is fuse fusing,and the defect type identified by the control switch is refusal defect,which is consistent with the actual situation,and the identification accuracy is high.
multi strategy particle swarm optimizationsubstation secondary equipmentfeature extractiondefect identifica-tion method
万方数据
维普
