research
Low-Cost, Class D Testing of Spacecraft Photovoltaic Systems Can Reduce Risk
- Publication date
- Publisher
Abstract
The end-to-end verification of a spacecraft photovoltaic power generation system requires light! A lowcost, portable, and end-to-end photovoltaic-system test appropriate for NASA's new generation of Class D missions is presented. High risk, low-cost, and quick-turn satellites rarely have the resources to execute the traditional approaches from higher-class (A-C) missions. The Class D approach, as demonstrated on the Lunar Atmospheric and Dust Environment Explorer (LADEE), utilizes a portable, metalhalide, theatre lamp for an end-to-end photovoltaic system test. While not as precise and comprehensive as the traditional Large Area Pulsed Solar Simulator (LAPSS) test, the LADEE method leverages minimal resources into an ongoing assessment program that can be applied through numerous stages of the mission. The project takes a true Class D approach in assessing the technical value of a costly, highfidelity performance test versus a simpler approach with less programmatic risk. The resources required are a fraction of that for a LAPSS test, and is easy to repeat due to its portability. Further, the test equipment can be handed down to future projects without building an on-site facility. At the vanguard of Class D missions, the LADEE team frequently wrestled with and challenged the status quo. The philosophy of risk avoidance at all cost, typical to Class A-C missions, simply could not be executed. This innovative and simple testing solution is contextualized to NASA Class D programs and a specific risk encountered during development of the LADEE Electrical Power System (EPS). Selection of the appropriate lamp and safety concerns are discussed, with examples of test results. Combined with the vendor's panellevel data and periodic inspection, the method ensures system integrity from Integration and Test (I&T) through launch. Following launch, mission operations tools are utilized to assess system performance based on a scant amount of available data