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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. 

Network measurement and monitoring are instrumental to network operations, planning and troubleshooting. However, increasing line rates (100+Gbps), changing measurement targets and metrics, privacy concerns, and policy differences across multiple R&E network domains have introduced tremendous challenges in operating such high-speed heterogeneous networks, understanding the traffic patterns, providing for resource optimization, and locating and resolving network issues. There is strong demand for a flexible, high-performance measurement instrument that can empower network operators to achieve the versatile objectives of effective network management and resource provisioning. In this demonstration, we present AMIS: Advanced Measurement Instrument and Services to achieve programmable, flow-granularity and event-driven network measurement, sustain scalable line rates, to meet evolving measurement objectives and to derive knowledge for network advancement.