Smart fault detection in satellite electrical power system

TL;DR

This paper introduces a neural network-based method for comprehensive fault detection in satellite electrical power systems, achieving over 99% accuracy and improving system reliability without relying on an Attitude Determination and Control Subsystem.

Contribution

It presents a novel integrated fault detection approach using machine learning techniques for the entire satellite power system, unlike prior component-specific methods.

Findings

Over 99% fault classification accuracy

Effective use of PCA and KNN for fault classification

Enhanced reliability of satellite power systems

Abstract

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