A Resilient Deep Learning Approach for State Estimation in Distribution Grids With Distributed Generation

State estimation is a challenging problem, particularly in distribution grids that have unique characteristics compared with transmission grids. Conventional methods that solve the state estimation problem at the transmission level require the grid to be observable, which does not apply to distribution grids. To make the distribution grid observable, researchers resort to pseudomeasurements, which are inaccurate. Also, the high integration of renewable energy introduces uncertainty, making the Distribution System State Estimation (DSSE) problem even more complex. This work proposes a deep neural network approach that solves the DSSE problem in unobservable distribution grids without employing erroneous pseudomeasurements. We create a dataset that emulates real-life scenarios of diverse operating conditions with distributed generation. We then subject the neural network to multiple test scenarios featuring noisier measurements and bad data to evaluate the robustness of our algorithm. We test our approach on three networks. Results demonstrate that our method efficiently solves the DSSE problem-which cannot be solved using conventional methods-and detects and mitigates bad data, further enhancing the reliability of the state estimation results.