摘要
为了实现大型变压器故障的精准诊断,提出了一种基于改进粒子群算法(Improved Particle Swarm Optimization,IPSO)优化深度信念网络(Deep Belief Network,DBN)的变压器故障诊断方法。该方法利用IPSO算法对DBN网络的参数进行优化,以油中溶解气体含量为输入量,变压器故障状态为输出量,构建了IPSO-DBN模型。利用IPSO-DBN模型对变压器进行故障诊断,结果表明IPSO-DBN模型诊断精度高达98.57%,诊断精度高于其他三种对比模型,验证了该模型能显著提升变压器故障诊断精度。
In order to achieve accurate diagnosis of large transformer faults,this paper proposes a transformer fault diagnosis method based on improved particle swarm optimization(IPSO)to optimize the deep belief network(DBN).This method utilizes the IPSO algorithm to optimize the network parameters of the DBN network,with the dissolved gas content in oil as the input and the transformer fault state as the output,to construct an IPSO-DBN model.The IPSO-DBN model was used for fault diagnosis of transformers,and the results showed that the diagnostic accuracy of the IPSO-DBN model was as high as 98.57%,which was higher than the other three comparative models,verifying that the model can significantly improve the accuracy of transformer fault diagnosis.
作者
李超
LI Chao(Shenzhen Shenpengda Power Grid Technology Co.,Ltd.,Shenzhen 518000,China)
出处
《电工技术》
2025年第13期55-58,共4页
Electric Engineering
关键词
变压器
故障诊断
深度信念网络
改进粒子群算法
适应度值
transformer
fault diagnosis
deep belief network
improved particle swarm optimization algorithm
fitness value
