An Agreement Based Dynamic Routing Method for Fault Diagnosis in Power Network with Enhanced Noise Immunity

The stable operation of a power system often depends on inscribing the faults that may arise when transmitting and distributing electrical power. Characterizing these faults is necessary to analyze the post-fault oscillography of the power lines. The power lines are prone to be affected by noises. The noises are responsible to introduce uncertainty in operating conditions. The variation in operating conditions leads to an unbalanced system. The diagnosis of faults is essential to ensure the secured operation of a power network. This paper introduces a unified unsupervised learning framework for short circuit fault analysis of a power transmission line. The proposed approach works with a small number of data set and reduces the computational cost. It uses a capsule network that investigates the low-level fault-oriented features. To guarantee the robustness of the proposed framework against noises a stacked denoising-autoencoder is integrated and modeled. The performance of the proposed model is measured and compared with some of the techniques available in the literature in terms of noise. The test with field data for three types of fault classification results in an accuracy of 9 ms for fault triggering.