Developing Data-Driven Approaches for Analyzing, Identifying, and Predicting Power System Outages

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Doostan, M. (2019). Developing Data-Driven Approaches for Analyzing, Identifying, and Predicting Power System Outages. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Outages that occur in power distribution systems can seriously endanger system operation in different ways. In recent years, with the explosion in data gathering within the smart grid framework, data analytics has emerged as a desirable tool in helping maintain power system security. In particular, with the deployment of an enormous number of intelligent electronic devices and various sensors, the data necessary for studying different outages have becoming available. Analyzing such data by employing analytical methods could shed light on understanding the characteristics of outages and could lead to developing models for analyzing, identifying, and predicting different outages.In this dissertation, the focus is on developing various approaches for analyzing, identifying, and predicting outages in power systems by using rigorous statistical methodologies, data analytics, and machine learning algorithms. Developing such approaches, however, requires addressing various practical challenges. As a result, those challenges are discussed throughout the dissertation and workable solutions are provided.The proposed approaches have the potential to provide not only a succinct view of the current system status but also more meaningful knowledge such as outage risks, and locations of potential problems, as well as suggestions on remedial actions. As such, this dissertation envisions a transformative framework that will demonstrate the use of innovative data analytics technologies for early warning of degrading operating conditions that may imperil system operation and/or quality.

Details
Author

Doostan, Milad

Title

Developing Data-Driven Approaches for Analyzing, Identifying, and Predicting Power System Outages

Physical Description

1 online resource (170 pages) : PDF

Date

2019

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Outages that occur in power distribution systems can seriously endanger system operation in different ways. In recent years, with the explosion in data gathering within the smart grid framework, data analytics has emerged as a desirable tool in helping maintain power system security. In particular, with the deployment of an enormous number of intelligent electronic devices and various sensors, the data necessary for studying different outages have becoming available. Analyzing such data by employing analytical methods could shed light on understanding the characteristics of outages and could lead to developing models for analyzing, identifying, and predicting different outages.In this dissertation, the focus is on developing various approaches for analyzing, identifying, and predicting outages in power systems by using rigorous statistical methodologies, data analytics, and machine learning algorithms. Developing such approaches, however, requires addressing various practical challenges. As a result, those challenges are discussed throughout the dissertation and workable solutions are provided.The proposed approaches have the potential to provide not only a succinct view of the current system status but also more meaningful knowledge such as outage risks, and locations of potential problems, as well as suggestions on remedial actions. As such, this dissertation envisions a transformative framework that will demonstrate the use of innovative data analytics technologies for early warning of degrading operating conditions that may imperil system operation and/or quality.

Genre

doctoral dissertations

Subjects--Topics

Electrical engineering
Statistics

Degree

Ph.D.

Keywords

Forecasting
Machine Learning
Power Distribution Systems
Power Outage Management
Predictive Modeling
Statistical Analysis

Subject Area

Electrical Engineering

Advisor(s)

Chowdhury, Badrul

Committee Members

Salami, Zia
Cecchi, Valentina
Lim, Churlzu
Dou, Wenwen

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2019.

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Rights Holder Information

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Identifier

Doostan_uncc_0694D_12028

Permalink

http://hdl.handle.net/20.500.13093/etd:1871