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.
Electrical and Computer Engineering
Number of Pages
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Zhang, Ying, "Model-Based and Data-driven Situational Awareness for Distribution System Monitoring and Control" (2020). Electrical Engineering Theses and Dissertations. 38.
Electrical and Electronics Commons, Power and Energy Commons, Systems and Communications Commons
Distribution system state estimation, machine learning, voltage control, phasor measurement units, situational awareness, deep reinforcement learning, power system monitoring and control, artificial intelligence, distributed generation, generative adversarial network.