An Efficient and Reliable Electric Power Transmission Network Topology Processing

Abstract

The modern bulk power system operation is complex and dynamic, with rapidly increasing inverter-based resources and active distribution systems. Therefore, high-speed monitoring is required to operate the power system reliably and efficiently. Transmission network topology processing (TNTP) is vital in power system control. Today’s TNTP is based on supervisory control and data acquisition (SCADA) system monitoring of relay signals (SMRS). Due to the slow data communication rate, SMRS cannot efficiently support the modern bulk power system’s energy management system (EMS) functions. In this study, a physics-based hierarchical TNTP (H-TNTP) approach based solely on node voltages and branch currents measurements is proposed utilizing artificial intelligence algorithms. H-TNTP includes the identification of substation configuration. The reliability of the H-TNTP is guaranteed by the design with inherent verification. If required, H-TNTP is capable of operating concurrently with the TNTP-SMRS. A power system with solar photovoltaic (PV) plants is utilized as a test system to illustrate the proposed H-TNTP approach. Results indicate that H-TNTP is fast with synchrophasor measurements. Furthermore, to demonstrate the application of the reliable and fast TNTP approach in EMSs, fast automatic generation control (AGC) during contingencies is studied. Typical results show that fast reconfiguration of AGC modes and dispatch factors leads to better frequency regulation.

Keywords

• Artificial intelligence
• reconfigurable automatic generation control
• substation configuration identification
• transmission network topology processor