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

Bobby.B Lyle School of Engineering

Advisor

Jianhui Wang

Subject Area

Computer Engineering, Electrical, Electronics Engineering

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.

Number of Pages

158

Format

.pdf

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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