A Hybrid Deep Learning-Based State Forecasting Method for Smart Power Grids

TL;DR

This paper introduces a hybrid deep learning approach combining CNN and RNN models to improve power system state forecasting accuracy in smart grids, outperforming existing methods on benchmark data.

Contribution

It presents a novel CNN-RNN hybrid model specifically designed for voltage and phase angle forecasting in smart power grids, enhancing prediction accuracy.

Findings

Achieves 10% lower normalized RMSE than existing methods.

Reduces average voltage magnitude error by 65%.

Reduces maximum voltage error by 35%.

Abstract

Smart power grids are one of the most complex cyber-physical systems, delivering electricity from power generation stations to consumers. It is critically important to know exactly the current state of the system as well as its state variation tendency; consequently, state estimation and state forecasting are widely used in smart power grids. Given that state forecasting predicts the system state ahead of time, it can enhance state estimation because state estimation is highly sensitive to measurement corruption due to the bad data or communication failures. In this paper, a hybrid deep learningbased method is proposed for power system state forecasting. The proposed method leverages Convolutional Neural Network (CNN) for predicting voltage magnitudes and a Deep Recurrent Neural Network (RNN) for predicting phase angels. The proposed CNN-RNN model is evaluated on the IEEE 118-bus benchmark. The results demonstrate that the proposed CNNRNN model achieves better results than the existing techniques in the literature by reducing the normalized Root Mean Squared Error (RMSE) of predicted voltages by 10%. The results also show a 65% and 35% decrease in the average and maximum absolute error of voltage magnitude forecasting.