Physics-Informed Deep Neural Network Method for Limited Observability State Estimation
This addresses the challenge of reliable grid operation with increasing renewable energy fluctuations, though it is an incremental improvement by modifying existing DNN training for a specific domain.
The paper tackles the problem of state estimation in partially observable power distribution grids by introducing a deep neural network method that incorporates physical grid information, achieving significantly better accuracy compared to standard approaches like weighted least squares with pseudo-measurements.
The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, increasing the fluctuations of the injected power. In this paper, we consider the case when the distribution grid becomes partially observable, and the state estimation problem is under-determined. We present a new methodology that leverages a deep neural network (DNN) to estimate the grid state. The standard DNN training method is modified to explicitly incorporate the physical information of the grid topology and line/shunt admittance. We show that our method leads to a superior accuracy of the estimation when compared to the case when no physical information is provided. Finally, we compare the performance of our method to the standard state estimation approach, which is based on the weighted least squares with pseudo-measurements, and show that our method performs significantly better with respect to the estimation accuracy.