人工蜂群优化BP神经网络的太阳电池阵电流预测

Solar Array Output Current Prediction of Optimized BP Neural Network Based on Artificial Bee Colony Algorithm

通过对卫星太阳电池阵输出电流影响因子进行分析,提出了一种基于人工蜂群(Artificial bee colony,ABC)算法优化BP神经网络的太阳电池阵输出电流预测方法。将太阳入射角、卫星太阳电池阵工作温度、卫星星时等遥测量变换后作为神经网络输入,进行输出电流预测。考虑到神经网络对初始权值及偏置敏感的特点,采用ABC改进算法对神经网络初始参数进行优化。该模型可用于卫星太阳电池阵电流输出能力分析、太阳电池阵预警及异常检测等。实验测试表明,模型能够取得较高预测精度,同星预测均方根误差(Mean squared error,MSE)为0.10 A,跨星预测均方根误差为0.12 A,其精度明显优于传统数据拟合方法。利用该模型及本文提出的预警策略进行预警,对于7年5个月的正常卫星数据没有发生误报,对于某异常卫星数据能够及时进行预警。

By analyzing influence factors of the satellite solar array output current,a solar array output current prediction method based on artificial bee colony(ABC)-BP neural network is proposed.Sunlight incident angle,solar array working temperature,satellite time are used as the input of neural network to predict the output current.An improved ABC algorithm is used to optimize initial parameters considering neural network’s sensitivity to the initial weights and bias.The trained model can be used for output current analyzing,detecting and alarming abnormality of the solar array.The results show that the trained model can achieve high prediction accuracy.The mean squared error(MSE)is 0.10 A for the same satellite and 0.12 A for different satellites,which are obviously better than those of the traditional data fitting method.By using this model and the proposed alarm method,there is no false alarm for the normal satellite data of 7 years and 5months,and the abnormal satellite data can be alarmed timely.