ConvLSTM-based real-time power flow estimation of smart grid with high penetration of uncertain PV considering measurement noise

A modern smart grid tends to have increasingly various uncertain renewable generations. Due to different geographical areas and network topology constraints, operations of a smart grid become complicated and challenging. Moreover, using existing methods, power flow estimation in real-time could be time-consuming and computationally expensive. This paper proposes an efficient deep learning approach to estimate real-time power flow solutions of the smart grid with high penetration of uncertain PV generations using synchrophasor data considering measurement noise. The performance and effectiveness of the proposed convolutional long short-term memory (ConvLSTM) with time-series cross-validation technique are examined using synchrophasor data with Gaussian noise in the IEEE 39 bus test system. The proposed ConvLSTM approach shows better robust performance than weighted least square (WLS) state estimation and long short-term memory (LSTM) approaches. In addition, state measurements and confidence intervals are employed to confirm the accuracy of estimated real-time power flow results. The accurate real-time power flow estimation is crucial to determining dynamic available transfer capability (ATC) results and efficient operations of smart grids.