Meter placement approaches for matrix completion-based distribution system state estimator

Traditional matrix and tensor completion approaches utilize latent structures in data to impute missing entries. Recent works on distribution system state estimators employing such data imputation techniques have identified the need to incorporate fundamental system equations as constraints to improve state estimation accuracy. As a result, these techniques provide superior state estimation performance compared to their model-free counterparts and conventional state estimators. In practice, the data required for these estimators are provided by sensors/meters deployed in the network. However, prior efforts do not explore the placement of sensors that optimize the performance of the estimators. Moreover, constraints on entries of these matrices and tensors result in specific combinations of known measurements to provide better imputation results than others. Therefore, this work proposes two-meter placement approaches that leverage network parameters and linearized power flow equations to identify sensor locations. These approaches achieve this by iteratively placing sensors with the highest contribution towards minimizing the voltage residual in the selected reference cases. The first approach identifies buses that provide the highest reduction in voltage residuals. In contrast, the second approach identifies locations of a heterogeneous set of sensors that provide the highest reduction in voltage residual. The proposed approaches can also extend existing sensor deployments such as distribution phasor measurement units (D-PMU) and supervisory control and data acquisition (SCADA) sensing and measurement devices (e.g., Bellwether meters) to improve state estimation performance. The approaches have been evaluated on the IEEE 33-node distribution system and an unbalanced 3-phase 559-node distribution system.