基于粒子群优化的KPCA暂态稳定评估模型的特征提取

提出一种核主成分分析法(KPCA),用于电力系统暂态稳定评估(TSA)模型中的输入向量特征提取,并利用粒子群优化算法(PSO)对核函数参数进行优化设置.以EPRI36系统为例,对基于支持向量机(SVM)分类的暂态稳定评估模型进行仿真,结果表明该方法不仅得到了良好的预测精度,而且大大降低了输入空间的维数.

Feature extract based on optimized kernel principal component analysis in transient stability assessment

An application about kernel principal component analysis(KPCA) is proposed for feature abstract in electric
systems transient stability assessment(TSA). The kernel function’s parameter is optimized by using the algorithm of particle swarm optimization(PSO). Emluator of TSA model based on support vector machine(SVM) with power system EPRI36 is also given. The result shows that the method not only has good prediction accuracy, but also reduces the input dimension greatly.