# Probabilistic Analysis Techniques Applied to Complex Spacecraft Power System Modeling

Electric power system performance predictions are critical to spacecraft, such as the International Space Station (ISS), to ensure that sufficient power is available to support all the spacecraft’s power needs. In the case of the ISS power system, analyses to date have been deterministic, meaning that each analysis produces a single-valued result for power capability because of the complexity and large size of the model. As a result, the deterministic ISS analyses did not account for the sensitivity of the power capability to uncertainties in model input variables. Over the last 10 years, the NASA Glenn Research Center has developed advanced, computationally fast, probabilistic analysis techniques and successfully applied them to large (thousands of nodes) complex structural analysis models. These same techniques were recently applied to large, complex ISS power system models. This new application enables probabilistic power analyses that account for input uncertainties and produce results that include variations caused by these uncertainties. Specifically, N&R Engineering, under contract to NASA, integrated these advanced probabilistic techniques with Glenn’s internationally recognized ISS power system model, System Power Analysis for Capability Evaluation (SPACE).

An advanced, fast probabilistic integrator (FPI) technique was used in this effort, because the traditional method for performing probabilistic analyses, the Monte Carlo method, is time consuming and computationally prohibitive for large complex models. Monte Carlo techniques involve running thousands of individual analyses, each with a slightly different set of input variables. The results from all those cases are integrated to obtain the sensitivity of the output variable--in this case, power--to those input uncertainties. However, when FPI is used, only a few analyses need to be performed; and FPI then integrates those results to approximate a complete Monte Carlo analysis.

ISS power analyses require thousands of input variables to model all the power system components: solar arrays, which generate power from sunlight; batteries that are charged during the sunlight and provide power when the solar arrays do not; and a large power distribution network, consisting of cables, circuit breakers, converters, and controllers to route power to various ISS users. To demonstrate SPACE power system probabilistic analysis, researchers at N&R Engineering with assistance from Glenn varied a sample of five input variables, including Earth albedo (sunlight reflected off the Earth), the efficiency of a power-conversion unit, and the attitude of the ISS. Results show that the variation in power capability can now be represented by not just a single value, but rather as a set of probabilities that a certain power level will be achieved.

Sample showing the probability of achieving a particular power level on a single ISS power channel. Mean power, 13.7 kWe; standard deviation of power, σ, 0.22.>
Long description of figure.

The results of this project also proved that Glenn’s advanced probabilistic analysis techniques (FPI), which were developed for large complex structural analysis models, can be used to assess complex spacecraft power systems. Glenn now has an integrated tool that can be used for probabilistic power system performance assessment. This new capability will allow ISS operators more insight into the performance of the power system and to operate the ISS power system closer to its operating limits with higher confidence.

Glenn contacts: Jeffrey S. Hojnicki, 216–433–5393, Jeffrey.S.Hojnicki@nasa.gov; and Jeffrey J. Rusick, 216–433–5375, Jeffrey.J.Rusick@nasa.gov
Authors: Jeffrey S. Hojnicki and Jeffrey J. Rusick
Programs/Projects: ISS, Spacecraft Power Systems

Last updated: July 15, 2005 1:56 PM

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