EXPERIMENTAL ACCURACY OF NILM LOAD DISAGGREGATION ON THREE-PHASE MICROGRID TEST BED

Abstract

Non-intrusive load monitoring (NILM) is a powerful tool that uses machine learning and special sensors to disaggregate large amounts of electrical data from a single central point of a power system. This service provides valuable insight into the system, allowing for anomaly detection and improvements to preventative maintenance. An important aspect of NILM is the accuracy of the sensors collecting the power data. The United States Navy is currently experimenting with the Mobile Power Meter (MPM), a load monitoring sensor that is proprietary to the Army Research Lab (ARL). These sensors are still being tested, optimized, and improved with every iteration. MPMs have not yet been validated for widespread use. In this thesis, we test the accuracy of MPM data collection and load disaggregation when applied to a three-phase microgrid testbed. The MPMs are run through tests with various loads and then meticulously compared to the ground truth data, which is obtained from oscilloscope probes attached at every phase of every load. Various power features, such as the frequency, voltage, and phase currents, are then selected for analysis. Based on percent error calculations and visual analysis, the MPM data is compared against the oscilloscopes for accuracy. Validating the accuracy of the MPMs is a significant step in ensuring NILM is precisely executed, thereby bolstering the energy security of the United States Navy.Distribution Statement A. Approved for public release: Distribution is unlimited.Ensign, United States Nav