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Power grids play a very important role in delivering electrical energy tohomes, industries and other places that require it. Because of this increaseddemand they are facing a great challenge of voltage variations. This happensdue to varied use of energy-consuming devices and appliances like electricvehicles, industrial consumption, occasional peak in energy demands etc. Forthese fluctuations in demands, it becomes extremely important to monitor theconditions at which the power grid operates. Once these conditions are known,the energy production can be manipulated to meet the demand. It has been foundthat the existing Power System State Estimation (PSSE) techniques may not begood in producing optimal Performance. Moreover, they are also expensive interms of computational processing. To address this problem, this researchproposes a state estimation method for power grids using Convolutional NeuralNetworks (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 inmaking the estimation using a Prox Linear Model (2.97 x 10-4).  Furthermore, the research also proposes Power System State Forecasting forimproving system awareness and resilience. The forecasting is carried out usinga model of Recurrent Neural Network (RNN). This model helps in accounting forlong-term nonlinear aspects present in data and based on that it does theforecasting. The proposed model forecasted with a RMSE of 2.53 x 10-3, which iscomparatively equal to the previous study mentioned above (2.59 x 10-3).

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