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Transmission line outage detection plays an important role in maintaining the reliability of electric power systems. Most existing methods rely on optimization models to estimate the outage of transmission lines, and the process is computationally burdensome. In this study, we propose a transmission line outage detection method using machine learning. Using this method, we could monitor the power flow of one line and estimate whether another line is in service or not, despite the load fluctuations in the system. The study also investigates the principles for observation point selection and the effectiveness of this method in detecting the outage of transmission lines with different levels of power flows. The method was implemented on an IEEE 118-bus system, and results show that the method is effective for transmission lines with all levels of power flows, and line outage distribution factors (LODF) are good indicators in observation point selection. 

Natural disasters has been causing an increasing amount of economic losses in the past two decades. Natural disasters, such as hurricanes, winter storms, and wildfires, can cause severe damages to power systems, significantly impacting industrial, commercial, and residential activities, leading to not only economic losses but also inconveniences to people’s day-today life. Improving the resilience of power systems can lead to a reduced number of power outages during extreme events and is a critical goal in today’s power system operations. This paper presents a model for decentralized decision-making in power systems based on distributed optimization and implemented it on a modified RTS-96 test system, discusses the convergence of the problem, and compares the impact of decision-making mechanisms on power system resilience. Results show that a decentralized decision-making algorithm can significantly reduce power outages when part of the system is islanded during severe transmission contingencies. 

Transmission switching is widely used in the electric power industry for both preventive and corrective purposes. Optimal transmission switching (OTS) problems are usually formulated based on optimal power flow (OPF) problems. OTS problems are originally nonlinear optimization problems with binary integer variables indicating whether a transmission line is in or out of service, however, they can be linearized into mixed-integer linear programs (MILP) through the big-M method. In such big-M-based MILP problems, the value of M can significantly affect their computational efficiency. This paper proposes a method to find the optimal big-M values for OTS problems and studies the impact of big-M values on the computational efficiency of OTS problems. The model was implemented on a modified RTS-96 test system, and the results show that the proposed model can effectively reduce the computational time by finding an optimal big-M value which ensures optimal switching solutions while maintaining numerical stability.