基于KPCA和CPSO的故障检测方法

提出一种基于核主元分析(KPCA)和混沌粒子优化群(CPSO)算法的非线性故障检测方法。通过核函数完成非线性变换，将变量由非线性的输入空间转换到线性的特征空间来计算主元，构造平方预测误差统计量检测故障是否发生。为避免粒子群算法的早熟现象，利用混沌优化的搜索特性，将CPSO算法应用到KPCA核参数的优化中。变压器故障检测结果表明，与基于PCA、KPCA和 PSO-KPCA的故障检测方法相比，该方法的检测正确率较高。

Fault Detection Method Based on KPCA and CPSO

A nonlinear fault detection method based on Kernel Principal Component Analysis(KPCA) and Chaos Particle Swarm Optimization(CPSO) algorithm is presented. KPCA performs nonlinear transformation by kernel function to map the nonlinear input space into linear feature space, computes principal component and detects faults by utilizing SPE statistics. The kernel parameters of kernel principal component are optimized in order to enhance the fault detection performance. For the premature convergence problem of the Particle Swarm Optimization(PSO) algorithm, the CPSO algorithm is adopted to utilize the chaos optimization’s search properties. Experimental results of transformer show that the proposed method has better detection performance than PCA, KPCA and PSO-KPCA method.