基于改进萤火虫算法和多分类支持向量机的变压器故障诊断

为了高效完成电力变压器故障诊断,引入萤火虫算法(FA),利用混沌优化理论和自适应变步长机制对算法进行改进,提高算法的收敛速度和精度,并将改进萤火虫算法(IFA)和支持向量机(SVM)理论相结合构造变压器故障诊断方法.该方法利用IFA选择合适的SVM参数,同时结合二叉树方法构造多分类SVM进行变压器故障类型识别.变压器故...

Power transformer fault diagnosis based on improved firefly algorithm and multi-classification support vector machine

Firefly algorithm, whose convergence speed and precision is improved by chaotic optimization theory and adaptive variable step size mechanism, is introduced to achieve fault diagnosis of power transformer efficiently. A power transformer fault diagnosis method based on improved firefly algorithm (IFA) and multi-classification support vector machine (SVM) is proposed. The method uses IFA to optimize the parameters of SVM. Multi-class SVM is constructed based on binary tree method to identify power transformer fault types in the method. The simulation results of power transformer fault diagnosis examples show that the convergence and optimization ability of IFA are better than those of FA and particle swarm optimization (PSO), and the optimized power transformer fault diagnosis model has higher accuracy.