Using Advanced Hybrid Power Systems Controls for Precision Sustainment Through AI

D. Moorman
Moser Energy Systems, Colorado, United States

Keywords: AI, machine learning, Precision sustainment, Predictive logistics, hybrid controls, digital twin

Advanced diagnostics and “smart” microgrid controls coupled with the energy storage used on a hybrid power system provide a controllable load that can be connected to the power system’s prime mover. Specific data can be measured, stored, and compared during consistent loading and unloading events made possible by the available capacity of the energy storage, the precise energy analysis provided by advanced power electronics, and the measurement of relevant engine diagnostic data. Each test can be evaluated and compared against the system’s baseline digital twin and with past IDS tests to better inform predictive maintenance events, overhaul schedules, and end-of-life tracking. The inverter and associated controls precisely measure the power output regarding voltage, current, ramp rates, and harmonic distortion. This provides a relative and measured response to power commands. The prime mover is instrumented with various measuring devices to identify engine performance, stability, thermal efficiency, mechanical efficiency, and vibration. The value of each measured parameter would be used to establish the “score” on that particular engine and compared to the stored digital twin for that power system to accurately track advancement through the life cycle.