基于自回归滑动平均模型的大口径天线风速预测方法

针对大口径、高性能射电望远镜天线受到的随机及时变风扰的问题, 利用自回归滑动平均模型预测望远镜周围风速, 提前计算风致结构变形量, 同时为望远镜伺服控制系统提供足够执行时间来降低风扰影响. 基于新疆奇台110m口径全向可动射电望远镜(QiTai Telescope, QTT)台址风场数据特征, 通过赤池信息准则和贝叶斯信息准则辨识模型阶次, 利用最大似然法估计模型参数, 分析模型残差特性以检验自回归滑动平均模型的有效性. 通过计算不同季度预测数据与测试数据偏差得到预测模型的精度, 夏季平均绝对误差为0.133mcdots^-1, 秋季平均绝对误差为0.162mcdots^-1, 冬季平均绝对误差为0.287mcdots^-1. 整体来看, 基于QTT台址不同季度风速数据建立的自回归滑动平均模型预测误差较小, 满足射电望远镜风扰控制系统的需求, 能为大口径射电望远镜风扰控制提供必要数据支撑.

ARMA Model-Based Wind Speed Prediction for Large Radio Telescope

The Autoregressive Moving Average Model (ARMA) predicts the wind speed around the telescope to lessen the impact of random and time-varying wind disturbances. This gives the telescope control system enough movement time to compensate for structural deformation. Combined wind speed prediction and servo control systems help reduce the pointing jitter caused by wind disturbance when the antenna performs observation tasks under wind disturbance and improve the pointing accuracy. This research analyzes the meteorological data, wind speed, and direction collected by the wind measurement tower at the Xinjiang 110 m aperture radio telescope (QTT) site to obtain the seasonal wind speed variation. The wind direction of the QTT site is primarily concentrated in the south and north. For different seasons, the wind direction characteristics are not apparent in summer and autumn and are primarily concentrated in the south-southeast direction in winter. For non-stationary wind speed time series, the proposed model uses the Augmented Dickey-Fuller Test (ADF) and Kwiatkowski-Phillips-Schmidt-Shin Test (KPSS) to verify the stationarity of wind speed time series in the training set or observe whether the autocorrelation coefficient decays quickly to zero with the lagged value. Based on the data characteristics of the wind field at the QTT site, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are used to identify the model order. Through the maximum likelihood estimation to estimate the model parameters, the model tests the validity of the autoregressive moving average prediction model of wind speed in different quarters at the QTT site by analyzing whether the residual characteristics of the model meet the normal distribution. Three error indexes are selected to examine the accuracy of prediction model: root means square error (RMSE), mean absolute error (MAE), and means absolute percent error (MAPE). For the MAE of the model prediction data and the test data, the MAE of the summer prediction model is 0.133mcdots^-1, the MAE of the autumn prediction model is 0.162mcdots^-1, and the MAE of the winter prediction model is 0.287mcdots^-1. These results of the prediction error of the ARMA established based on the wind speed data for different quarters of the QTT site verify the accuracy and stability, which proves that the ARMA can achieve high prediction accuracy and reliable effects. The model meets the needs of wind disturbance control of the radio telescope and provides a systematic scheme for wind speed prediction in practical and necessary data support for the wind disturbance control of the radio telescope.