Reconstruction of Power System Measurements Based on Enhanced Denoising Autoencoder

TL;DR

This paper introduces an enhanced denoising autoencoder with LSTM integration for improved missing data reconstruction in power systems, effectively handling large datasets and noise.

Contribution

It proposes a novel LSTM-EDAE model that leverages neighbor correlation and deep learning for superior missing data reconstruction in power system measurements.

Findings

LSTM-EDAE outperforms traditional neural networks in reconstruction accuracy.

Utilizing neighbor correlation improves missing data recovery.

Effective in handling big data and noise in power system measurements.

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.