Fault identification in T-connection transmission lines based on general regression neural network and traveling wave power angle

Abstract

In order to improve the accuracy of internal and external fault identification of T-connected transmission lines, a new method for identifying internal and external faults of T-connected transmission lines based on general regression neural network and traveling wave power angle was studied. The initial voltage and current traveling wave measured by each traveling wave protection unit of T-connected transmission line are transformed by S-transform, and the single frequency power angle after fault is calculated to form the sample set of fault eigenvector of T-connected transmission line. The established general regression neural network intelligent fault recognition model is used to train and test the sample data to identify internal and external faults. The simulation results show that the algorithm can accurately identify the internal and external faults of the T-connected transmission line under various operating conditions.

Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm

“…The SVM evaluation method is difficult to obtain ideal prediction accuracy when dealing with large-scale training samples [23]. Generalized regression neural network (GRNN) is a radial basis function neural network proposed by Specht, which has a strong ability of nonlinear mapping [24]. Compared with BPNN and SVM, GRNN has fewer adjustment parameters, is not easy to fall into a local minimum, and is good at dealing with large-scale training samples [25].…”