【摘要】 为进一步提高有载分接开关（on-load tap changer,OLTC）机械状态监测的准确性，文中基于优化品质因数可调小波变换（tunable quality wavelet transform,TQWT）对OLTC切换过程中的振动信号进行了分析。即使用人工鱼群算法（artificial fish swarm algorithm,AFSA）基于分解余量与整体正交系数研究了TQWT的优化分解方法，计算得到了OLTC振动信号的多个子序列，构建了基于优化孪生支持向量机（twin support vector machine,TWSVM）的OLTC机械故障诊断模型。对某CM型OLTC正常与典型机械故障下振动信号的分析结果表明，所提优化TQWT分解方法有效提高了OLTC振动信号分解结果的准确性。相对于其他诊断模型，所构建AFSA-TWSVM的OLTC机械故障诊断模型分类效果好且收敛速度更快。更多还原

【Abstract】 For improving the accuracy of mechanical condition monitoring of on-load tap-changer(OLTC)further,the vibration signal of the OLTC during the switching period is analyzed on the basis of the optimized tunable Q-factor wavelet transform(TQWT)method. The optimization decomposition method of the TQWT is studied by using the artificial fish swarm algorithm and AFAS(based on decomposition residue and global orthogonal coefficients),and the mechanical fault diagnosis model of OLTC is constructed based on the optimized twin support vector machine(TWSVM)and in accordance with multiple multiple subsequences of the OLTC vibration signals which is obtained by calculation. The analysis results from vibration signals of a CM-type OLTC under normal and typical mechanical faults show that the proposed optimized TQWT decomposition method can effectively improve the accuracy of decomposing vibration signals of OLTC. The mechanical fault diagnosis mode of OLTC with AFSA-TWSVM,compared to other diagnosis models,has better classification performance and fast convergence speed.更多还原