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粒子群优化三相桥式整流电路故障诊断

更新时间：2020-10-27 05:24:07 大小：1M 上传用户：zhengdai查看TA发布的资源 标签：整流电路故障诊断 下载积分：1分 评价赚积分 （如何评价?） 打赏 收藏 评论(0) 举报

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针对三相桥式整流电路中晶闸管出现断路故障的问题,提出采用粒子群优化BP网络的故障诊断方法和蚁群粒子群混合算法优化BP网络的故障诊断方法。首先采用Matlab软件对电路建立仿真模型,通过仿真得到电路故障信息;然后将故障信息输入到BP网络中,对比网络输出与期望输出,得出故障诊断率;最后分别采用粒子群算法和蚁群粒子群混合算法优化BP网络,采用相同的诊断方法,分别得出两种优化网络的故障诊断率。通过对比三种网络的输出信息可以看出,未优化的、PSO优化的和混合算法优化的BP网络,收敛时迭代次数分别为6000、2750和715,网络故障诊断率分别为68.2、81.8%和95.5%。采用神经网络对整流电路中的晶闸管进行断路故障诊断时,通过对网络的优化,能减少诊断时间和提高诊断率。

Aiming at the problem of thyristor breaking fault in three-phase bridge rectifier circuit, a fault diagnosis method using BP network optimized by Particle Swarm Optimization (PSO) and a fault diagnosis method using BP network optimized by the hybrid algorithm of Ant Colony Optimization (ACO) and PSO were proposed. Firstly, Matlab software was used to establish a simulation model for the circuit, and the circuit fault information was obtained through the simulation. Then, the fault information was input into BP network, and the fault diagnosis rate was obtained by comparing the network output with the expected output. Finally, PSO algorithm and the hybrid algorithm of ACO and PSO were used to optimize BP network respectively. By using the same diagnosis method, the fault diagnosis rates of the two optimized networks were obtained. By comparing the output information of the three networks, it can be seen that the iteration times of the non-optimized BP network, the optimized BP netowrk by PSO, and the optimized BP network by the hybrid algorithm is 6 000, 2 750 and 715 respectively, and the network fault diagnosis rate is 68.2, 81.8% and 95.5%. When the neural network is used to diagnose the thyristor breaking fault in the rectifier circuit. By optimizing the network, the diagnosis time is reduced and the diagnosis rate is improved.

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