Alternative Title
物理建模和数据驱动的配电系统态势感知
Abstract
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.
Degree Date
Summer 8-4-2020
Document Type
Dissertation
Degree Name
Ph.D.
Department
Electrical and Computer Engineering
Advisor
Jianhui Wang
Subject Area
Computer Engineering
Number of Pages
158
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Zhang, Ying, "Model-Based and Data-driven Situational Awareness for Distribution System Monitoring and Control" (2020). Electrical Engineering Theses and Dissertations. 38.
https://scholar.smu.edu/engineering_electrical_etds/38
Included in
Electrical and Electronics Commons, Power and Energy Commons, Systems and Communications Commons
Notes
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.