Smart Fault Detection in Nanosatellite Electrical Power System
This addresses reliability issues for nanosatellite operations in low Earth orbit, but it is incremental as it applies existing machine learning techniques to a specific domain.
The paper tackles fault detection in nanosatellite electrical power systems by developing a neural network-based method that uses solar radiation and panel temperature to simulate normal operation and classify faults like line-to-line and short circuits, achieving fault diagnosis through pattern recognition.
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.