國立虎尾科技大學 |

Data-Driven Approaches for Enhancing Power Grid Reliability.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Data-Driven Approaches for Enhancing Power Grid Reliability./
作者:	Sohrabi, Behrouz.
面頁冊數:	1 online resource (76 pages)
附註:	Source: Masters Abstracts International, Volume: 85-10.
Contained By:	Masters Abstracts International85-10.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798382317328

Data-Driven Approaches for Enhancing Power Grid Reliability.
Sohrabi, Behrouz.

Data-Driven Approaches for Enhancing Power Grid Reliability. - 1 online resource (76 pages)

Source: Masters Abstracts International, Volume: 85-10.

Thesis (M.S.)--University of Denver, 2024.

Includes bibliographical references

This thesis explores the transformative potential of data-driven approaches in addressing key operational and reliability issues in power systems. The first part of this thesis addresses a prevalent problem in power distribution networks: the accurate identification of load phases. This study develops a data-driven model leveraging consumption measurements from smart meters and corresponding substation data to reconstruct topology information in low-voltage distribution networks. The proposed model is extensively tested using a dataset with more than 5,000 real load profiles, demonstrating satisfactory performance for large-scale networks. The second part of the thesis pivots to a crucial safety concern: the risk and vulnerability of power system to wildfires. This study presents a comprehensive data-driven framework that integrates a robust wildfire spread simulator and power flow analysis to assess metrics such as risk and vulnerability associated with transmission network components against grid-ignited wildfires. A 30-bus test system serves as the case study. Results suggest that this framework can support power system planners and operators in determining the optimal allocation of investments for resilience and risk mitigation strategies.This research demonstrates how harnessing data, particularly from smart meters and robust simulation tools, can drive strategic decision-making in power system planning and operations, and contribute significantly towards a reliable and resilient energy future.

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

Mode of access: World Wide Web

ISBN: 9798382317328Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Distribution networksIndex Terms--Genre/Form:

554714
Electronic books.

Data-Driven Approaches for Enhancing Power Grid Reliability.
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520 $a This thesis explores the transformative potential of data-driven approaches in addressing key operational and reliability issues in power systems. The first part of this thesis addresses a prevalent problem in power distribution networks: the accurate identification of load phases. This study develops a data-driven model leveraging consumption measurements from smart meters and corresponding substation data to reconstruct topology information in low-voltage distribution networks. The proposed model is extensively tested using a dataset with more than 5,000 real load profiles, demonstrating satisfactory performance for large-scale networks. The second part of the thesis pivots to a crucial safety concern: the risk and vulnerability of power system to wildfires. This study presents a comprehensive data-driven framework that integrates a robust wildfire spread simulator and power flow analysis to assess metrics such as risk and vulnerability associated with transmission network components against grid-ignited wildfires. A 30-bus test system serves as the case study. Results suggest that this framework can support power system planners and operators in determining the optimal allocation of investments for resilience and risk mitigation strategies.This research demonstrates how harnessing data, particularly from smart meters and robust simulation tools, can drive strategic decision-making in power system planning and operations, and contribute significantly towards a reliable and resilient energy future.
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