Three-Layer Framework Integrating Optimal Placement of Supervisory, Control, and Acquisition System Measurements with Clustering-Based Electric Substations Selection for State Estimation of Medium-Voltage Distribution Networks
Source abstract
Abstract
One of the biggest challenges, both from a technical and economic point of view, of the Distribution Network Operators refers to identifying the locations (electric distribution substations) integrated into a supervisory, control, and acquisition (SCADA) system to perform on-site measurements used in the state estimation of the electric distribution networks (EDNs). In response to this challenge, a robust and resilient three-layer methodology has been proposed to solve the state estimate issue of the EDNs based on an optimal placement algorithm of the remote terminal units integrated into the SCADA system at the level of the EDSs. The first layer allows a clustering algorithm-based determination of the classes of the EDSs with similar features of the load profiles. The second layer identifies the “candidate” classes and decides the pilot EDSs with on-site SCADA measurements. The third layer allows the state estimation of the EDN based on the load values measured in the pilot EDEs. The framework was tested and validated using a medium voltage EDN of a Romanian DNO supplying an urban area. The results obtained highlighted that the accuracy had been ensured for on-site measurements in 12 of 39 EDSs (representing approximately 30% 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 determined by a power flow calculation at the level of the EDN. Consequently, the framework can significantly decrease the investments associated with integrating the SCADA system by the DNOs, with great benefits regarding the state estimation of the EDNs.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power System Optimization and Stability · Optimal Power Flow Distribution · Power System Reliability and Maintenance
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Power Engineering, “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania
References (37)
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