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

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.

Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

“…The overall architecture has only one master, that is, the data acquisition system, while the remaining are slaves, that is, measurement systems, and each slave will have one unique address in order that the master can accurately receive the corresponding data of each slave during inquiry. Figure 5 shows the cloud system software architecture [1][2][3][4], including (1) local data center, (2) login management server, (3) real-time video streaming server, (4) video streaming transmission function, and (5) cloud data center, described as shown in Figure 5.…”