Model-Based and Data-driven Situational Awareness for Distribution System Monitoring and Control

Electric power systems are undergoing a dramatic change. The penetration of distributed energy resources (DERs) such as wind turbine generators and photovoltaic panels is turning a traditional power system into the active distribution network. Power system situational awareness, which provides critical information for system monitoring and control, is being challenged by multiple sources of uncertainties such as random meter errors, stochastic power output of DERs, and imprecise network parameters. On the other hand, cyber-physical power system operation is vulnerable to cyberattacks against effective state estimation, such as false data injection attacks (FDIAs). To construct next-generation smart grids, this dissertation develops a comprehensive situational awareness framework for distribution system monitoring and control via optimization, machine learning, and artificial intelligence. Specifically, this dissertation explores advanced model-based and data-driven methodologies in this framework, including state estimation, cyberattack detection, fault location, and voltage control.