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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Improved magnetic components for static inverters and converters - magnetic and physical properties of magnetic materials, conductors and insulation, and screening magnetic tests
Power converter for ion thrustors using electronic gate controlled switches in two phase-shifted parallel inverters
Magnetic components and static inverters and converters - sine current screening and sine flux screening core tests on test program materials
Design of medium power dc to 115 volt, 60 Hertz ac inverter
Optimization study and basic performance characteristics for conceptual designs for high power static inverters
Breadboard design and production units of integrated static inverter
Design stage reliability analysis application to static inverter
Sine current screening and ac square wave tests to evaluate magnetic and electrical properties of materials for use as magnetic components in static converters and inverters
Breadboard model development of integrated static inverter
Dual-element power devices for solid state inverters, evaluating potential hazards of reverse second breakdown
Integrated circuit arrays for solid state inverters, noting Johnson counter and packaging
Analytic model for describing operation of Marzolf tunnel diode inverter
Flip-flop arrays, power transistors, epitaxial stress, and other technological developments in integrated static inverter program
Timing channel circuit reliability in static inverter
Component reliability analysis for 250 volt-ampere static inverter
Circuitry, fabrication, and performance characteristics of 75-VA integrated static inverter for gyro platform power supply
Photovoltaic system production simulations are conventionally run using hourly weather datasets. Hourly simulations are sufficiently accurate to predict the majority of long-term system behavior but cannot resolve high-frequency effects like inverter clipping caused by short-duration irradiance variability. Direct modeling of this subhourly clipping error is only possible for the few locations with high-resolution irradiance datasets. This paper describes a method of predicting the magnitude of this error using a machine learning model and 30-minute satellite irradiance data. The method predicts a correction for each 30-minute interval with the potential to roll up into 60-minute corrections to match an hourly energy model. The model is trained and validated at locations where the error can be directly simulated from 1-minute ground data. The validation shows low bias at most ground station locations. The model is also applied to gridded satellite irradiance to produce a heatmap of the estimated clipping error across the United States. Finally, the relative importance of each predictor satellite variable is retrieved from the model and discussed.
This presentation gives a high-level overview of past and current results related to advanced PV inverter functions, such as volt/var control, and how such functions enable higher PV hosting capacity levels in distribution systems.