Three-Layer Framework Integrating Optimal Placement of SCADA Measurements with Clustering-Based Electric Substations Selection for State Estimation of Medium-Voltage Distribution Networks

The continuous monitoring of modern electric distribution networks (EDNs) is essential for accurate situational awareness and state estimation. This paper proposed a robust and resilient three-layer methodology for state estimate of the EDNs based on an optimal placement algorithm of the remote terminal units integrated into the supervisory, control, and acquisition system (SCADA) at the level of the electric distribution substations (EDSs) to perform on-site measurements. The first layer allows the determination of the classes of the EDSs with similar features of the load profiles identified through a correlation matrix using the K-means clustering algorithm. The second layer identifies the “candidate” classes and decides the pilot EDSs with on-site SCADA measurements. The optimal placement corresponds to the minimization of the load estimation errors obtained using the multiple linear regression models between the EDSs from the classes not included in the set of the “candidate” classes and the pilot EDSs. The third layer allows the state estimation of the EDN based on the load values measured in the pilot EDEs and the other EDSs obtained through the regression models. The base testing and validating of the proposed framework was a real urban medium voltage electric distribution network. The results obtained highlighted that the accuracy had been ensured for on-site measurements in 12 of 39 EDSs (representing 30% approximately of EDSs integrated into the SCADA system), leading to a mean average percentage error of 2.6% for the load estimation and below 1% for the state variables at the level of the EDN.