Real Time State Estimation of Power Grids Using Convolutional Neural Networks and State Forecasting Via Recurrent Neural Networks

TL;DR

This paper introduces CNN-based real-time power grid state estimation and RNN-based forecasting to improve accuracy and resilience, addressing limitations of traditional methods.

Contribution

It presents a novel CNN approach for power system state estimation and combines it with RNN-based forecasting for enhanced grid monitoring.

Findings

CNN achieved RMSE of 2.57 x 10^-4, outperforming previous models.

RNN forecasted with RMSE of 2.53 x 10^-3, comparable to existing methods.

Proposed models improve accuracy and computational efficiency in power grid monitoring.

Abstract

Power grids play a very important role in delivering electrical energy to homes, industries and other places that require it. Because of this increased demand they are facing a great challenge of voltage variations. This happens due to varied use of energy-consuming devices and appliances like electric vehicles, industrial consumption, occasional peak in energy demands etc. For these fluctuations in demands, it becomes extremely important to monitor the conditions at which the power grid operates. Once these conditions are known, the energy production can be manipulated to meet the demand. It has been found that the existing Power System State Estimation (PSSE) techniques may not be good in producing optimal Performance. Moreover, they are also expensive in terms of computational processing. To address this problem, this research proposes a state estimation method for power grids using Convolutional Neural Networks (CNN). It was found that the model produced an RMSE of 2.57 x 10-4, which was comparatively accurate than one of previous studies involved in making the estimation using a Prox Linear Model (2.97 x 10-4). Furthermore, the research also proposes Power System State Forecasting for improving system awareness and resilience. The forecasting is carried out using a model of Recurrent Neural Network (RNN). This model helps in accounting for long-term nonlinear aspects present in data and based on that it does the forecasting. The proposed model forecasted with a RMSE of 2.53 x 10-3, which is comparatively equal to the previous study mentioned above (2.59 x 10-3).