Machine learning-based single-phase ground fault identification strategy for AC-DC transmission lines

In this paper, an improved single-phase grounding fault (SPGF) nature identification strategy based on machine learning is proposed. To solve the problem of conventional single-phase automatic reclosure cannot distinguishing the nature of SPGF in AC-DC hybrid lines, the fault phase voltage characteristics of AC lines with DC feed are analyzed, and the voltage harmonic energy feature vector is extracted. The whale optimization algorithm (WOA) designed by orthogonal experiment design (OED) is used to select the model parameters of the least square support vector machine (LSSVM), and a discriminative model for the fault properties of the hybrid transmission line is obtained. The simulation experiments based on PSCAD/EMTDC 4.5.0 platform are conducted for model training and fault identification performance testing, and the results verifies the effectiveness and feasibility of the proposed method. Compared with LSSVM, the fault identification strategy based on OED-WOALSSVM has a higher accuracy in fault identification. In addition, the accuracy of the model identification strategy under different fault locations, parallel compensation degree, transition resistance, and power system noise effects is also verified.