Parameter probabilistic prediction for satellite power system based on unsupervised multi-dimensional sequence segmentation

With the increasing operational autonomy and mission complexity of satellites, the power system, as one of the most critical subsystems in a satellite platform, needs to provide high-quality and fast-response power output under complex load requirements. State monitoring is valuable to ensure the power system's stable and safe operation. Monitoring and predicting the key parameters based on the satellite power system's telemetry data are meaningful in detecting potential anomalies. However, the multi-physical high-dimensional telemetry data of the satellite power system are oriented to the operating conditions. The dynamic operating conditions of the satellite power system make it difficult to establish the proper parameter prediction model with high adaptability. To address these challenging issues, this paper proposes a parameter probabilistic prediction based on unsupervised time series segmentation. Firstly, the telemetry data under dynamic operation conditions are divided based on the unsupervised dynamic factor model and K-Means algorithm. The dynamic factor model is used to extract the common factor sequences of the multidimensional telemetry data according to the dynamic relationship between different parameters, followed by the clustering operation using K-Means to classify the multidimensional telemetry data into different sub-sequences. Secondly, a probabilistic prediction method based on the spatiotemporal fusion of multi-physical parameters is proposed. The method utilizes a Bayesian neural network to learn the spatial relationship between parameters and applies Gaussian process regression to establish the temporal dependence of parameters. Afterward, a specific strategy is utilized to fuse the prediction results of the two, thus realizing the fusion of spatio-temporal information of satellite multi-dimensional telemetry data. The experimental results based on the real meteorological satellite power system telemetry data in 2010 show that the average absolute errors of the key parameter predictions after the division of the working conditions are all less than 0.25, which is improved by more than 37.08 % compared with the pre-division. This indicates that the proposed method can well recognize the dynamic operation modes and has a positive advancing effect on the parameter prediction task. Meanwhile, the average absolute error of the proposed prediction method is improved by at least 40.4 % compared with other single prediction models. It shows that the method proposed in this paper can realize high-precision prediction of parameters, and has good application prospects in condition monitoring and anomaly detection of satellite power systems.