Kory W. Hedman

Xingpeng Li, Pranavamoorthy Balasubramanian, Mostafa Sahraei-Ardakani et al.

Transmission switching (TS) has gained significant attention recently. However, barriers still remain and must be overcome before the technology can be adopted by the industry. The state of the art challenges include AC feasibility and performance, computational complexity, the ability to handle large-scale real power systems, and dynamic stability. This two-part paper investigates these challenges by developing an AC TS-based real-time contingency analysis (RTCA) tool that can handle large-scale systems within a reasonable time. The tool proposes multiple corrective switching actions, after detection of a contingency with potential violations. To reduce the computational complexity, three heuristic algorithms are proposed to generate a small set of candidates for switching. Parallel computing is implemented to further speed up the solution time. Furthermore, stability analysis is performed to check for dynamic stability of proposed TS solutions. Part I of the paper presents a comprehensive literature review and the methodology. The promising results, tested on the Tennessee Valley Authority (TVA) system and actual energy management system (EMS) snapshots from Pennsylvania New Jersey Maryland (PJM) and the Electric Reliability Council of Texas (ERCOT), are presented in Part II. It is concluded that RTCA with corrective TS significantly reduces potential post-contingency violations and is ripe for industry adoption.

Xingpeng Li, Kory W. Hedman

State estimation estimates the system condition in real-time and provides a base case for other energy management system (EMS) applications including real-time contingency analysis and security-constrained economic dispatch. Recent work in the literature shows malicious cyber-attack can inject false measurements that bypass traditional bad data detection in state estimation and cause actual overloads. Thus, it is very important to detect such cyber-attack. In this paper, multiple metrics are proposed to monitor abnormal load deviations and suspicious branch flow changes. A systematic two-stage approach is proposed to detect false data injection (FDI) cyber-attack. The first stage determines whether the system is under attack while the second stage identifies the target branch. Numerical simulations verify that FDI can cause severe system violations and demonstrate the effectiveness of the proposed two-stage FDI detection (FDID) method. It is concluded that the proposed FDID approach can efficiently detect FDI cyber-attack and identify the target branch, which will substantially improve operators situation awareness in real-time.