基于LSSA优化DBN的双有源桥变换器开路故障诊断

Open circuit fault diagnosis of dual active bridgeconverterbased on LSSA optimized DBN

针对双有源桥(dual active bridge,DAB)变换器中IGBT开路故障诊断精度较低的问题,提出基于莱维飞行麻雀搜索算法(Levy sparrow search algorithm,LSSA)优化深度信念网络(deep belief network,DBN)的故障诊断方法。首先,利用莱维飞行策略改进麻雀搜索算法的收敛速度和全局优化能力。然后将DBN的均方差作为适应度函数,利用LSSA寻找DBN的最优隐藏层单元数,根据得到的最优值建立DBN故障诊断模型。通过RT-LAB搭建DAB变换器半实物仿真系统,对变压器漏感电流信号进行故障诊断,在收敛速度、适应度值和诊断精度指标方面进行对比分析。实验结果表明诊断模型可以有效诊断DAB变换器开路故障,且诊断精度达到99%。

Aiming at the low fault diagnosis accuracy of IGBTs’open circuit fault in dual active bridge(DAB)converter,a fault diagnosis method based on the Levy sparrow search algorithm(LSSA)to optimize the deep belief network(DBN)is proposed.First,the Levy flight strategy improves the convergence speed and global optimization capability of the SSA.Then,the mean square error function of the DBN is taken as the fitness function.The LSSA finds the optimal number of hidden layer units of DBN.According to the optimal number of hidden layers,we construct a DBN open-circuit fault diagnosis model.Through building the hardware-in-the-loop simulation system of DAB converter in RT-LAB,the method uses the transformer leakage current as the diagnostic signal.The comparative analysis is conducted on the convergence speed,fitness value index and diagnosis accuracy.The experiment results show that the diagnosis model can diagnose the open-circuit fault of the DAB converter effectively,and the fault diagnosis accuracy achieves 99%.