基于DWT和双通道LSTM的卫星电池阵电流预测方法  

Satellite battery array current prediction method based on DWT and dual-channel LSTM

作　　者：何利健 张锐 林晓冬 HE Lijian;ZHANG Rui;LIN Xiaodong(Innovation Academy for Microsatellites,Chinese Academy of Sciences,Shanghai 201203,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院微小卫星创新研究院,上海201203 [2]中国科学院大学,北京100049

出　　处：《中国科学院大学学报（中英文）》2023年第3期415-421,共7页Journal of University of Chinese Academy of Sciences

摘　　要：卫星太阳能电池阵输入电流受地球反照、卫星反照等多方面影响,会产生不同频率的波动起伏,造成预测精度不足,针对该问题提出一种基于离散小波变换(DWT)和长短期记忆网络(LSTM)的电流数据预测方法。首先对电流信号进行归一化,其次使用DWT对遥测信号进行小波分解,获取信号的多层高低频小波系数,提高信号数据特征,然后通过双通道LSTM进行特征学习,预测出各层小波系数,最后通过对预测出来的小波系数进行信号重构并反归一化获得最终预测信号。通过使用某在轨卫星太阳能电池阵电流遥测数据对预测模型进行验证,结果表明提出的方法相对于传统LSTM模型具有更好的预测精度,平均绝对误差减少16.4%,均方根误差减少29.9%,相关系数提高3.2%。The input current of the satellite solar array is affected by the earth albedo,satellite albedo,etc.,which will produce fluctuations of different frequencies,resulting in insufficient prediction accuracy.To solve this problem,a current data prediction method based on discrete wavelet transform(DWT)and long-term short-term memory(LSTM)is proposed.First,the signal is normalized,and then discrete wavelet transform is used to decompose the telemetry signal,to obtain the multi-layer high and low-frequency wavelet coefficients of the signal to improve the signal data characteristics,and then use dual-channel LSTM is used to perform feature learning to predict each layer of wavelet coefficients,and finally the final prediction signal is obtained by reconstructing and de-normalizing the predicted wavelet coefficients.The model is verified by using the current telemetry data of an on-orbit satellite solar array.The results show that the proposed method has better prediction accuracy than traditional LSTM.MAE is reduced by 16.4%,RMSE is reduced by 29.9%,and R is improved by 3.2%.

关 键 词：长短期记忆网络 离散小波变换 遥测数据 太阳电池阵 预测模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]