Abstract Accurate prediction of solar panel temperature can help keep on-orbit satellites in good condition. Traditional physical models have the ability to describe and predict temperature; however, the effect is not entirely satisfactory. To produce a better forecast, the panel current of the solar panel, which is strongly correlated with temperature signals, is chosen as the input, and a novel system identification model between the two signals is established. The model we propose is based on a Hammerstein-Wiener model and integrates wavelet neural networks that adopt self-constructed wavelet bases. In addition, a complete process of training and parameter optimization is designed to be less time consuming than previous methods. The result from a test set of real telemetry data demonstrates the efficiency and accuracy of our method. Moreover, the proposed prediction model, which is based on historical data, can be used in on board self-learning and our subsequent autonomous health management.

中文翻译：

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基于小波神经网络和Hammerstein-Wiener模型的太阳能电池板温度确定新方法

摘要 准确预测太阳能电池板温度有助于在轨卫星保持良好状态。传统物理模型具有描述和预测温度的能力；然而，效果并不完全令人满意。为了产生更好的预测，选择与温度信号强相关的太阳能电池板的电池板电流作为输入，并在这两个信号之间建立了一种新的系统识别模型。我们提出的模型基于 Hammerstein-Wiener 模型，并集成了采用自建小波基的小波神经网络。此外，完整的训练和参数优化过程被设计为比以前的方法耗时更少。来自真实遥测数据测试集的结果证明了我们方法的效率和准确性。而且，