State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks
This work addresses a domain-specific problem for distribution network operators, offering an incremental improvement in estimation techniques.
The paper tackles the challenge of state and topology estimation in unobservable distribution systems by proposing a deep learning-based approach that improves accuracy with fewer synchrophasor measurement devices compared to conventional methods.
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach for topology identification (TI) and unbalanced three-phase distribution system state estimation (DSSE). Two deep neural networks (DNNs) are trained for time-synchronized DNN-based TI and DSSE, respectively, for systems that are incompletely observed by synchrophasor measurement devices (SMDs) in real-time. A data-driven approach for judicious SMD placement to facilitate reliable TI and DSSE is also provided. Robustness of the proposed methodology is demonstrated by considering non-Gaussian noise in the SMD measurements. A comparison of the DNN-based DSSE with more conventional approaches indicates that the DL-based approach gives better accuracy with smaller number of SMDs.