ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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Description
The growth of energy demands in recent years has been increasing faster than the expansion of transmission facility construction. This tendency cooperating with the continuous investing on the renewable energy resources drives the research, development, and construction of HVDC projects to create a more reliable, affordable, and environmentally friendly power grid.
Constructing the hybrid AC-HVDC grid is a significant move in the development of the HVDC techniques; the form of dc system is evolving from the point-to-point stand-alone dc links to the embedded HVDC system and the multi-terminal HVDC (MTDC) system. The MTDC is a solution for the renewable energy interconnections, and the MTDC grids can improve the power system reliability, flexibility in economic dispatches, and converter/cable utilizing efficiencies.
The dissertation reviews the HVDC technologies, discusses the stability issues regarding the ac and HVDC connections, proposes a novel power oscillation control strategy to improve system stability, and develops a nonlinear voltage droop control strategy for the MTDC grid.
To verify the effectiveness the proposed power oscillation control strategy, a long distance paralleled AC-HVDC transmission test system is employed. Based on the PSCAD/EMTDC platform simulation results, the proposed power oscillation control strategy can improve the system dynamic performance and attenuate the power oscillations effectively.
To validate the nonlinear voltage droop control strategy, three droop controls schemes are designed according to the proposed nonlinear voltage droop control design procedures. These control schemes are tested in a hybrid AC-MTDC system. The hybrid AC-MTDC system, which is first proposed in this dissertation, consists of two ac grids, two wind farms and a five-terminal HVDC grid connecting them. Simulation studies are performed in the PSCAD/EMTDC platform. According to the simulation results, all the three design schemes have their unique salient features.
Constructing the hybrid AC-HVDC grid is a significant move in the development of the HVDC techniques; the form of dc system is evolving from the point-to-point stand-alone dc links to the embedded HVDC system and the multi-terminal HVDC (MTDC) system. The MTDC is a solution for the renewable energy interconnections, and the MTDC grids can improve the power system reliability, flexibility in economic dispatches, and converter/cable utilizing efficiencies.
The dissertation reviews the HVDC technologies, discusses the stability issues regarding the ac and HVDC connections, proposes a novel power oscillation control strategy to improve system stability, and develops a nonlinear voltage droop control strategy for the MTDC grid.
To verify the effectiveness the proposed power oscillation control strategy, a long distance paralleled AC-HVDC transmission test system is employed. Based on the PSCAD/EMTDC platform simulation results, the proposed power oscillation control strategy can improve the system dynamic performance and attenuate the power oscillations effectively.
To validate the nonlinear voltage droop control strategy, three droop controls schemes are designed according to the proposed nonlinear voltage droop control design procedures. These control schemes are tested in a hybrid AC-MTDC system. The hybrid AC-MTDC system, which is first proposed in this dissertation, consists of two ac grids, two wind farms and a five-terminal HVDC grid connecting them. Simulation studies are performed in the PSCAD/EMTDC platform. According to the simulation results, all the three design schemes have their unique salient features.
ContributorsYu, Jicheng (Author) / Karady, George G. (Thesis advisor, Committee member) / Qin, Jiangchao (Thesis advisor, Committee member) / Ayyanar, Raja (Committee member) / Holbert, Keith E. (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2017
Description
In recent years, there has been an increasing need for effective voltage controls in power systems due to the growing complexity and dynamic nature of practical power grid operations. Deep reinforcement learning (DRL) techniques now have been widely explored and applied to various electric power operation analyses under different control structures. With massive data available from phasor measurement units (PMU), it is possible to explore the application of DRL to ensure that electricity is delivered reliably.For steady-state power system voltage regulation and control, this study proposed a novel deep reinforcement learning (DRL) based method to provide voltage control that can quickly remedy voltage violations under different operating conditions. Multiple types of devices, adjustable voltage ratio (AVR) and switched shunts, are considered as controlled devices. A modified deep deterministic policy gradient (DDPG) algorithm is applied to accommodate both the continuous and discrete control action spaces of different devices. A case study conducted on the WECC 240-Bus system validates the effectiveness of the proposed method.
System dynamic stability and performance after serious disturbances using DRL are further discussed in this study. A real-time voltage control method is proposed based on DRL, which continuously regulates the excitation system in response to system disturbances. Dynamic performance is considered by incorporating historical voltage data, voltage rate of change, voltage deviation, and regulation amount. A versatile transmission-level power system dynamic training and simulation platform is developed by integrating the simulation software PSS/E and a user-written DRL agent code developed in Python. The platform developed facilitates the training and testing of various power system algorithms and power grids in dynamic simulations with all the modeling capabilities available within PSS/E. The efficacy of the proposed method is evaluated based on the developed platform.
To enhance the controller's resilience in addressing communication failures, a dynamic voltage control method employing the Multi-agent DDPG algorithm is proposed. The algorithm follows the principle of centralized training and decentralized execution. Each agent has independent actor neural networks and critic neural networks. Simulation outcomes underscore the method’s efficacy, showcasing its capability in providing voltage support and handling communication failures among agents.
ContributorsWang, Yuling (Author) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Committee member) / Pal, Anamitra (Committee member) / Hedman, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2024