Kernel‐PCA‐based single‐phase earth fault detection model using multilayer perceptron in deep learning

Abstract

The rapid and accurate identification of single‐phase earth fault is of prime concern for many scholars from industry and academia, because its fault characteristics are extremely weak while the related potential hazard is extremely severe. In this context, this paper borrows from Deep Learning and in turn innovatively puts forward a multilayer perceptron (MLP)‐based single‐phase earth fault detection model augmented with kernel principal component analysis (KPCA). First, KPCA is applied to building fault feature extraction engineering via the transformation of fault features from low‐dimensional linear indivisible space to high‐dimensional one. Second, a MLP‐based model is used for basic model reference where many optimization strategies together with dropout technique are introduced and custom‐designed in tuning this model to achieve the best detection accuracy and convergence characteristic. Numerical studies demonstrate the superiority of the proposed KPCA‐MLP‐based single‐phase earth fault detection model.