基于IABC优化SVM的变压器故障诊断

Transformer fault diagnosis based on an artificial bee colony-support vector machine optimization algorithm

针对故障信息较少时无法准确诊断变压器故障的问题,提出一种改进的人工蜂群算法优化支持向量机的故障诊断方法。首先采用主成分分析(PCA)对输入变量进行特征提取,降低特征向量的维数,避免了变量信息之间的相互重叠。其次,通过基于二维均匀的种群初始化和基于欧氏距离的食物源更新来对传统的人工蜂群算法(ABC)进行改进,并将改进蜂群算法(IABC)与ABC和粒子群算法(PSO)进行性能测试,证明了搜索速率和收敛性都有显著提高。最后用IABC优化支持向量机(SVM)的参数,将PCA提取的新特征值分别输入IABC-SVM、GA-SVM、PSO-SVM模型并对比诊断效果。最终表明所提方法具有诊断准确率高、模型简单、泛化能力强的特点。

The fault of a transformer cannot be accurately diagnosed when the fault information is small. An improved artificial bee colony algorithm is proposed to optimize the fault diagnosis method of the support vector machine. First, Principal Component Analysis(PCA) is used to extract the features of the input variables. This reduces the dimension of the feature vector and avoids the overlap of the variable information. Secondly, through two-dimensional uniform based population initialization and an Euclidean distance-based food source update, this paper improves the traditional Artificial Bee Colony(ABC) algorithm, and then tests the performance of the Improved Bee Colony Algorithm(IABC) and ABC and Particle Swarm Optimization(PSO). Search rate and convergence are improved significantly. By using IABC optimization Support Vector Machine(SVM) parameters, the new eigenvalues extracted by PCA are input into IABC-SVM, GA-SVM, PSO-SVM models and the diagnostic results are compared. Finally, the method has high diagnostic accuracy, uses a simple model, and has strong generalization ability.