Graduation Semester and Year

2014

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering

Department

Electrical Engineering

First Advisor

Wei-Jen Lee

Abstract

Identifying and using Advanced Metering Infrastructure (AMI) data to improve customer experience, utility operations, and advanced power management is one of the most important challenges in the smart grid development. Smart meters, capable of capturing frequent interval customer consumption (and possibly other parameters) using communication networks, are vital components of smart grid technology. Thus, smart meters expand the available range of data and functionality. Making the most of information from smart meters and smart grids increasingly requires dealing with Big Data. Big Data is a game changer, enabling utilities to transform the ways they interact with and serve their customers.Today, many utilities are deploying smart meters as a vital step moving towards smart grids. Going from one meter reading per month to one meter reading at a sub-hourly rate (one minute, fifteen minutes, or thirty minutes) immediately poses a great technical challenge that can be overwhelming if not properly managed. AMI is becoming the standard in today's utility industry, making it possible to transform the performance of the grid and dramatically improve customer experience, utility operations, and advanced power management. To attain the maximum benefits from AMI, it is of utmost importance that utilities perform large-scale data analysis and transform them into information.Consequently, this dissertation addresses the efforts involved in turning smart meter raw data into actionable information. Algorithms are developed to utilize data collected from AMI system for three main purposes:1. To develop accurate customer daily load profiling for load estimation and network demand reconciliation to improve the efficiency and security of the utility grid.2. To enhance the performance of load forecasting which impacts operating practices and planning decisions to build, lease, or sell generation and transmission assets and the decisions to purchase or sell power at wholesale level.3. To investigate a nonintrusive load monitoring method for discerning individual appliances from a residential customer.

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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