June 15, 2019
June 15, 2019
June 19, 2019
Energy Conversion and Conservation
Smart grid technologies are becoming ever more present in electric power infrastructures. One of the most important aspects of having a smart grid is to be able to detect loads, and use this detection information for automation. This research paper focuses on a non-intrusive load monitoring (NILM) algorithm that was developed as part of a senior design project and then carried over into a summer research project. The goal of this research is two-fold; to produce a working load detection algorithm for purely resistive, inductive, or capacitive loads; and to use this experience as the basis for creating a new laboratory assignment for undergraduate students. The NILM algorithm was developed in MATLAB and tested with both simulated data and real data collected in the institution’s power lab. Current transformers were used to read the three-phase voltage and current waveforms. Real data was collected and used to train the machine learning algorithm and confirm its ability to successfully identify purely resistive, inductive, and capacitive loads. While research shows much more advanced algorithms in development, the purpose of this project was to act as a proof-of-concept for prototyping a NILM algorithm in MATLAB that can be recreated by students in a classroom setting. As a lab assignment, this project combines knowledge of power systems, signal processing, and coding –providing students with a relevant assignment that relates to modern day challenges in smart grid and smart home technology. This paper describes the motivation for the project, the technical background, the procedure for experimentation and development, results, a plan for the future laboratory assignment and its assessment, and a conclusion of the work.
Kerestes, R. J., & Stokes, D., & Brody, R. M., & Emes, A., & Williams, A. (2019, June), Board 70: Development and Implementation of a Non-Intrusive Load Monitoring Algorithm Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32411
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