Doctoral Dissertations
Date of Award
12-2022
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Energy Science and Engineering
Major Professor
Srijib K Mukherjee
Committee Members
Fangxing Li, Russell Zaretzki, Brennan T. Smith
Abstract
In recent times, various efforts have been made to address the challenge of adequately representing hydropower systems in modeling frameworks, accounting for the lack of data to represent the multiple constraints in hydropower operation. This research is a pilot data-driven methodology for characterizing, classifying, and comparing the water-to-energy and energy-to-water signal transformations that hydropower facilities as signal processors accomplish. In this study, a Box Jenkins transfer function/noise model is used to identify the relationship between reservoir inflows and outflows. For examining the feasibility of this methodology, 5-minute fleet data for five storage and five run-of-river facilities was provided by the Tennessee Valley Authority (TVA) and transfer function models are developed. The influence of past inflow and outflow values on the current outflow decisions was investigated and summarized by examining the results of Box Jenkins methodology. Finally, dominance analysis was introduced to add value to the Box Jenkins model results and provide different stakeholders with a set of concepts to convey the functionality of hydropower.
Recommended Citation
Shibu, Asha, "A Hydropower Facility as an Energy Water Signal Processor. " PhD diss., University of Tennessee, 2022.
https://trace.tennessee.edu/utk_graddiss/7679