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Mathematical Problems in Engineering
Volume 2013, Article ID 819379, 7 pages
http://dx.doi.org/10.1155/2013/819379
Research Article

Intelligent Monitoring and Predicting Output Power Losses of Solar Arrays Based on Particle Filtering

1School of Automation, Nanjing University of Science and Technology, Nanjing, China
2Beijing Key Laboratory of High-Speed Transport Intelligent Diagnostic and Health Management, Beijing, China
3Beijing Aerospace Measure & Control Corp., Ltd., Beijing, China
4College of Electronic Engineering, Naval University of Engineering, Wuhan, China
5School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China

Received 10 April 2013; Revised 2 June 2013; Accepted 4 June 2013

Academic Editor: Chengjin Zhang

Copyright © 2013 Hongzheng Fang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Solar arrays are the main source of energy to the on-orbit satellite, whose output power largely determines the life cycle of on-orbit satellites. Monitoring and further forecasting the output power of solar arrays by using the real-time observational data are very important for the study of satellite design and on-orbit satellite control. In this paper, we firstly describe the dynamical model of output power with summarizing the influencing factors of attenuation for solar arrays and elaborating the evolution trend of influencing factors which change with time. Based on the empirical model, a particle filtering algorithm is formulated to predict the output power of solar arrays and update the model parameters, simultaneously. Finally, using eight-year observational data of voltage and current from a synchronous on-orbit satellite, an experiment is carried out to illustrate the reliability and accuracy of the particle filtering method. Comparative results with classical curve fitting also are presented with statistical root mean square error and mean relative error analysis.