Transient stability assessment of power systems using support vector regressor and convolution neural network

Abstract

The electric power system is based on a large-scale non-linear system that creates various stability issues. One of the issues is the Transient Stability Assessment, due to multiple hindrances, the power system aims to maintain its synchronism. In the power system, ensuring reliability in planning, control, and monitoring is essential. With the development of new techniques like electric components, power electronics, renewable power generations, etc., to ensure safety and reliability, and improve economic conditions, human interference is complicated. Therefore, a computer-based assessment is needed. Transient stability assessment (TSA) ensures security and provides stable operation in the power system. This paper proposed support-vector machines(SVM)-based support-vector machines Convolutional Neural Networks (CNN) to assist the operation of the power system (SVM-CNN). The novelty of the proposed work is, it minimizes the workload of operational staff and improves efficiency and ability using SVM-based CNN. With the proposed accuracy rate for the training data set was 97.02 %, and the testing data set was 96.83 %. SVM got 96.44 %, and CNN got 96.68 % in the training data set. and the testing data set produces 95.24 % in SVM and 95.74 % in the CNN model.