Improved performance of detection and classification of 3-phase transmission line faults based on discrete wavelet transform and double-channel extreme learning machine

Power transmission lines are the key network that transmits energy from the generation side to load. The complexity and uncertainty in the power system increase continuously due to the evolution of the smart grid, which needs an effective and accurate protection system. The faults in transmission lines affect the whole power system and also the consumers’ side. Therefore, accurate and precise identification of faults in transmission lines minimizes the losses and maximizes the functionality and reliability of the power network. Due to the recent advances in digital technology, an online scheme is used to locate the fault in transmission lines. In this paper, machine learning-based discrete wavelet transform and double-channel extreme learning machine method are proposed to locate and classify the faults in transmission lines. Db4 wavelet is used as a mother wavelet in the discrete wavelet transform for feature extraction up to nine levels. The proposed method validated on real-time data which achieves higher classification accuracies and less fault detection time. Results show that high-impedance non-linear faults have no effect on the proposed technique.