Reconstruction of Power System Measurements Based on Enhanced Denoising Autoencoder

Abstract: This paper presents a new solution for reconstructing missing data in power system measurements. An Enhanced Denoising Autoencoder (EDAE) is proposed to reconstruct the missing data through the input vector space reconstruction based on the neighbor values correlation and Long Short-Term Memory (LSTM) networks. The proposed LSTM-EDAE is able to remove the noise, extract principle features of the dataset, and reconstruct the missing information for new inputs. The paper shows that the utilization of neighbor correlation can perform better in missing data reconstruction. Trained with LSTM networks, the EDAE is more effective in coping with big data in power systems and obtains a better performance than the neural network in conventional Denoising Autoencoder. A random data sequence and the simulated Phasor Measurement Unit (PMU) data of power system are utilized to verify the effectiveness of the proposed LSTM-EDAE.