Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions. When a spacecraft is in orbit, because the solar array is exposed to the harsh space environment, with increasing working time, the performance of its internal electronic components gradually degrade until abnormal damage occurs. This damage makes solar array power generation unable to fully meet the energy demand of a spacecraft. Therefore, timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of spacecraft. In this paper, we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine (ILS-SVM) model: it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set, then gets n corresponding least squares support vector machine (LS-SVM) submodels by training on these training subsets, respectively; after that, the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on; finally, based on the obtained ILS-SVM model, a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar arrays. We use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs, which reflects the applicability of the method.
Power supply capability is the main factor that affects and restricts the mission planning of earth observation satellite. In reality, in order to ensure that the battery in the satellite power system reaches the predetermined service life, users of earth observation satellites usually make mission plans based on fixed mode settings given by manufacturers, lacking a method to evaluate the power system's emergency mission supported capability. To address this issue, a data-driven method is proposed based on the telemetry data and mission instruction information of the Earth observations satellites. The method evaluates the power system supportability for emergency missions by realizing power generation capability prediction, mission energy consumption analysis and energy balance simulation. In addition, we use an on-orbit satellite as an example to verify the effectiveness of this method, which shows that the proposed method can evaluate the ability of Earth observation satellites to perform emergency missions in unconventional application scenarios well.
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