Transient Stability Evaluation Model for Power System Based on Kernel Support Vector Machine
With the development of computer technology,several fields begin to transform towards intelligence,and the application of spatial data intelligence technology is increasingly concerned.The power system is an important part of the whole power grid operation,and it is the working space of electronic components and electronic equipment as well.How to evaluate the stability of power systems is the key point to ensure its stable operation.To handle the transient evaluation problem of power systems,data samples are collected and a feature extraction model is constructed,through integrating spatial data intelligence technology into it.Support Vector Machine(SVM)algorithm is used to improve the performance of power systems,and kernel function and Mahalanobis distance are used to optimize the SVM algorithm.A transient evaluation model of power systems based on Kernel SVM(KSVM)is established.Experiments are conducted in the power system datasets,and the results show that the accuracy of the proposed model KSVM is 95.62％,which is 11.36％higher than that of the convolutional neural network algorithm.
power systemtransient stateSVMkernel functionMahalanobis distance
万方数据
维普
