Power Grid Fault Diagnosis Method Based on Stacked Sparse Denoising Auto-Encoder and GRU Network

Abstract

This research aims to solve the problems of feature extraction, data dimension reduction, gradient dissipation and low training efficiency in power grid fault classification. Aiming at PMU measurement data with high dimensionality and strong real-time characteristics, a fault diagnosis method based on Stacked Sparse Denoising Auto-Encoder and GRU network is proposed. The Stacked Sparse Denoising Auto-Encoder is used to reduce the sequence dimension to obtain the sparse feature expression of the data, and then the time-dependent features of the data extracted by the GRU are used to obtain the fault type. Simulations and experiments show that compared with the traditional neural network algorithm, the proposed method can effectively extract high-dimensional data features, reduce data dimensions, improve the efficiency of GRU network classification, accelerate the convergence speed and reduce the training time, and has better stability and higher accuracy.