東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Data-Driven Approaches for Enhancing...

Sohrabi, Behrouz.

Linked to FindBook

Google Book

Amazon

博客來

Data-Driven Approaches for Enhancing Power Grid Reliability.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-Driven Approaches for Enhancing Power Grid Reliability./
Author:	Sohrabi, Behrouz.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	76 p.
Notes:	Source: Masters Abstracts International, Volume: 85-10.
Contained By:	Masters Abstracts International85-10.
Subject:	Energy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30814054
ISBN:	9798382317328

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

Data-Driven Approaches for Enhancing Power Grid Reliability. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 76 p.

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

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

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.

ISBN: 9798382317328Subjects--Topical Terms:

876794
Energy.
Subjects--Index Terms:

Distribution networks

Data-Driven Approaches for Enhancing Power Grid Reliability.
LDR:02661nmm a2200385 4500 001 2400086
005 20240924101858.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798382317328
035 $a (MiAaPQ)AAI30814054
035 $a AAI30814054
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sohrabi, Behrouz. $3 3770057
245 1 0 $a Data-Driven Approaches for Enhancing Power Grid Reliability.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 76 p.
500 $a Source: Masters Abstracts International, Volume: 85-10.
500 $a Advisor: Khodaei, Amin.
502 $a Thesis (M.S.)--University of Denver, 2024.
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.
590 $a School code: 0061.
650 4 $a Energy. $3 876794
650 4 $a Applied physics. $3 3343996
650 4 $a Electrical engineering. $3 649834
653 $a Distribution networks
653 $a Load Phase Identification
653 $a Power systems
653 $a Transmission network components
653 $a Wildfire risk assessment
690 $a 0791
690 $a 0544
690 $a 0215
710 2 $a University of Denver. $b Computer Engineering. $3 1035894
773 0 $t Masters Abstracts International $g 85-10.
790 $a 0061
791 $a M.S.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30814054