Fault Detection, Classification, and Location Based on Empirical Wavelet Transform-Teager Energy Operator and ANN for Hybrid Transmission Lines in VSC-HVDC Systems

Traditional protection methods are not suitable for hybrid (cable and overhead) transmission lines in voltage source converter based high-voltage direct current (VSC- HVDC) systems. Accordingly, this paper presents the robust fault detection, classification, and location based on the empirical wavelet transform-Teager energy operator (EWT-TEO) and artificial neural network (ANN) for hybrid transmission lines in the VSC-HVDC systems. The operational scheme of the proposed protection method consists of two loops: ➀ an EWT-TEO based feature extraction loop, ➁ and an ANN-based fault detection, classification, and location loop. Under the proposed protection method, the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform (EWT) method. The energy content extracted by the EWT is fed into the ANN for fault detection, classification, and location. Various fault cases, including the high-impedance fault (HIF) and noises, are performed to train the ANN with two hidden layers. The test system and signal decomposition are conducted by PSCAD/EMT-DC and MATLAB, respectively. The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave (TW) based protection method. The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems, where a mean percentage error of approximately 0.1% is achieved.