Smart Fault Detection in Nanosatellite Electrical Power System

TL;DR

This paper introduces a neural network-based fault detection method for nanosatellite electrical power systems, capable of diagnosing various faults without relying on an Attitude Determination Control Subsystem, using machine learning techniques.

Contribution

It proposes a novel fault detection approach utilizing neural networks and machine learning methods for nanosatellite power systems without ADCS.

Findings

Neural network effectively detects faults using solar radiation and temperature data.

Machine learning classifiers like PCA, decision tree, and KNN compare with neural network.

Fault diagnosis achieved with high accuracy in simulated environment.

Abstract

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.