A novel fault identification method for HVDC transmission line based on Stransform multi-scale area

Abstract

Aiming at the problem that traditional traveling wave protection is difficult to take into account both quick-action and selectivity, an intelligent fault identification method for HVDC transmission lines based on S-transform multi-scale area is proposed. This method combines Radial Basis Function Network (RBFN) can accurately distinguish between internal and external faults, and at the same time achieve fault pole selection. First, the discrete S-transform is performed on the transient current signal, and multiple frequency scale signals are selected to calculate the area of the frequency signal after the fault. The S-transform multi-scale area is used to characterize the internal and external fault features and fault pole characteristics. The S-transform multi-scale area is used to form a feature vector, and the feature vector set is divided into a training set and a test set. The training set is trained to obtain an RBFN model, and the test set is used for testing. The prediction result obtained is the classification of different fault types. A large number of simulation results show that the method can effectively realize the internal and external fault identification and fault pole selection under different fault distances and different transition resistances, and has a strong ability to withstand transition resistances.