國立虎尾科技大學 |

Artificial Intelligence for Resilience in Smart Grid Operations.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Artificial Intelligence for Resilience in Smart Grid Operations./
作者:	Jayawardene, Randeniya.
面頁冊數:	1 online resource (131 pages)
附註:	Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Contained By:	Dissertations Abstracts International82-08B.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798569906468

Artificial Intelligence for Resilience in Smart Grid Operations.
Jayawardene, Randeniya.

Artificial Intelligence for Resilience in Smart Grid Operations. - 1 online resource (131 pages)

Source: Dissertations Abstracts International, Volume: 82-08, Section: B.

Thesis (Ph.D.)--Clemson University, 2020.

Includes bibliographical references

Today, the electric power grid is transforming into a highly interconnected network of advanced technologies, equipment, and controls to enable a smarter grid. The growing complexity of smart grid requires resilient operation and control. Power system resilience is defined as the ability to harden the system against and quickly recover from high-impact, low-frequency events. The introduction of two-way flows of information and electricity in the smart grid raises concerns of cyber-physical attacks. Proliferated penetration of renewable energy sources such as solar photovoltaic (PV) and wind power introduce challenges due to the high variability and uncertainty in generation. Unintentional disruptions and power system component outages have become a threat to real-time power system operations. Recent extreme weather events and natural disasters such as hurricanes, storms, and wildfires demonstrate the importance of resilience in the power system. It is essential to find solutions to overcome these challenges in maintaining resilience in smart grid. In this dissertation, artificial intelligence (AI) based approaches have been developed to enhance resilience in smart grid. Methods for optimal automatic generation control (AGC) have been developed for multi-area multi-machine power systems. Reliable AI models have been developed for predicting solar irradiance, PV power generation, and power system frequencies. The proposed short-horizon AI prediction models ranging from few seconds to a minute plus, outperform the state-of-art persistence models. The AI prediction models have been applied to provide situational intelligence for power system operations. An enhanced tie-line bias control in a multi-area power system for variable and uncertain environments has been developed with predicted PV power and bus frequencies. A distributed and parallel security-constrained optimal power flow (SCOPF) algorithm has been developed to overcome the challenges in solving SCOPF problem for large power networks. The methods have been developed and tested on an experimental laboratory platform consisting of real-time digital simulators, hardware/software phasor measurement units, and a real-time weather station.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798569906468Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Artificial IntelligenceIndex Terms--Genre/Form:

554714
Electronic books.

Artificial Intelligence for Resilience in Smart Grid Operations.
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