基于神经网络的纳卫星电源系统故障智能诊断方法
Smart Fault Detection in Nanosatellite Electrical Power System
- 用神经网络结合太阳辐射与板温预测正常电流负载
- 可识别光伏、转换器、电池等多类故障，准确率超95%
- 适合航天器电源故障检测与无姿控系统的轻量化设计
本文提出一种在低地球轨道运行、无姿态确定与控制子系统（ADCS）的纳卫星电力系统故障检测新方法。由于压力耐受、发射压力及环境因素，该系统各部件易发生故障，常见类型包括光伏子系统的相间短路与开路，直流-直流转换器中的IGBT短路与开路，以及地面电池调节器故障。系统基于神经网络构建无故障仿真模型，以太阳辐射和太阳能板表面温度为输入，输出电流与负载。通过神经网络分类器，依据故障模式与类型进行诊断；同时对比使用主成分分析（PCA）、决策树与KNN等机器学习方法进行分类。
原文摘要 · Abstract (English)
This paper presents a new detection method of faults at Nanosatellites' electrical power without an Attitude Determination Control Subsystem (ADCS) at the LEO orbit. Each part of this system is at risk of fault due to pressure tolerance, launcher pressure, and environmental circumstances. Common faults are line to line fault and open circuit for the photovoltaic subsystem, short circuit and open circuit IGBT at DC to DC converter, and regulator fault of the ground battery. The system is simulated without fault based on a neural network using solar radiation and solar panel's surface temperature as input data and current and load as outputs. Finally, using the neural network classifier, different faults are diagnosed by pattern and type of fault. For fault classification, other machine learning methods are also used, such as PCA classification, decision tree, and KNN.
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