标题： 智能故障检测在卫星电力系统中 显示英文标题

标题： Smart fault detection in satellite electrical power system

摘要： 本文提出了一种新的方法，用于检测在低地球轨道（LEO）运行的卫星电力系统中的故障，而无需姿态确定和控制系统子系统（ADCS）。这些系统的组件容易发生故障，例如光伏子系统中的相间故障、直流-直流转换器中的开路和短路以及电池中的接地故障。以前的研究主要集中在检测每个组件的故障，例如光伏阵列或转换器系统，因此对整个卫星电力系统作为一个整体的关注有限。我们的方法通过利用多层感知器（MLP）神经网络模型来弥补这一差距，该模型利用输入数据如太阳辐射和表面温度来预测电流和负载输出。这些机器学习技术通过使用不同的方法如主成分分析（PCA）和K最近邻（KNN）来有效分类故障。所提出的模型在识别多个子系统的故障方面实现了超过99%的准确率，相比之前的方法提供了完整的诊断解决方案，标志着显著的进步。这种全面的方法提高了系统可靠性，并有助于降低任务失败的可能性。 显示英文摘要

摘要： This paper presents an new approach for detecting in the electrical power system of satellites operating in Low Earth Orbit (LEO) without an Attitude Determination and Control Subsystem (ADCS). Components of these systems are prone to faults, such as line-to-line faults in the photovoltaic subsystem, open circuits, and short circuits in the DC-to-DC converter, as well as ground faults in batteries. In the previous research has largely focused on detecting faults in each components, such as photovoltaic arrays or converter systems, therefore, has been limited attention given to whole electrical power system of satellite as a whole system. Our approach addresses this gap by utilizing a Multi-Layer Perceptron (MLP) neural network model, which leverages input data such as solar radiation and surface temperature to predict current and load outputs. These machine learning techniques that classifiy use different approaches like Principal Component Analysis (PCA) and K-Nearest Neighbors (KNN), to classify faults effectively. The model presented achieves over 99% accuracy in identifying faults across multiple subsystems, marking a notable advancement from previous approaches by offering a complete diagnostic solution for the entire satellite power system. This thorough method boosts system reliability and helps lower the chances of mission failure