Models and criteria for performance prediction of solar power space-based
- Paper number
IAC-09.E3.P.5
- Author
Mrs. Mariia Iurchenko, Institute of Technical Mechanics of the National Academy of Science and National Space Agency of Ukraine, Ukraine
- Coauthor
Dr. Anatoliy Alpatov, Institute of Technical Mechanics of the National Academy of Science and National Space Agency of Ukraine, Ukraine
- Coauthor
Dr. Vjacheslav Gusynin, National Space Agency of Ukraine (NSAU), Ukraine
- Year
2009
- Abstract
Space-based solar energy requires the use of large orbiting platforms with a complex infrastructure of the various modules and systems. The main functional modules of such systems are the converters of solar energy into electric power - photovoltaic (PV) power generation, bearing structures, systems deployment and management of solar power stations (SPS) in the space, power transmission systems, delivery system design elements into the working orbit, storage of energy. The effectiveness of each of the modules determines the efficiency of HPS as a whole. In addition, the efficiency of HPS depends on the target system solvable. Several such tasks, depending on the location of the receiver of energy: the Earth, Moon, circum-terrestrial orbit or circum-lunar orbit. To evaluate the performance indicators of two groups are used – general and special. General characterize the novelty, relevance, cost-effectiveness, scientific and technical level; perspectives, the potential scale of practical use, and other qualities. Special activities characterize the quality of technology modules. The generalized indicator is developed using these two to indicate the performance of SPS. The core of the models to assess the effectiveness and prediction of the dynamics of the SPS is the function of the preferences. As a generalized indicator of the effectiveness of each group of indicators, and on the totality of the indicators is used by the respective functions of preference. The calculation of the functions of preference made on the basis of distribution functions - exponential, normal, logarithmic normal distribution and Weibull distribution. For the projections auto-regressive equations systems are used. Models reflect the multidimensional and interrelatedness of the discussed indicators. The problems of estimating the coefficients in the regression models are solved. Conducted at the training samples, the calculations show a satisfactory accuracy of the forecasts. The analysis of the influence of individual parameters on the performance of SPS is conducted.
- Abstract document
- Manuscript document
(absent)