arXiv subjects

Niloofar Nobahari

Publications and source records attributed to Niloofar Nobahari.

3 recordsLinked to original sources

arXiv2026

Smart Fault Detection in Nanosatellite Electrical Power System

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.

cs.LG↗

arXiv2025

Reliability-Based Fault Analysis and Modeling of Satellite Electrical Power Subsystems Using Fault Tree and Simulation Tools

One of the most important satellite subsystems is its electric power subsystem. The occurrence of a fault in the satellite power system causes the failure of all or part of the satellite. Calculating the overall reliability of the power system before the mission is crucial in improving the design of the satellite power system. Each component of the power system may malfunction due to pressure, launch pressure, and operating conditions. Accordingly, in this paper, first, a healthy and faulty system for the components of the electrical power system is simulated with MATLAB. Finally, by drawing a fault tree to analyze the reliability of the power subsystem, overall mission reliability, power system fault rate, and overall fault rate of the mission are calculated by Windchill software. Finally, a total mission assurance of 0.999 was achieved, indicating the high reliability of the simulated system.

eess.SY↗

arXiv2025

Smart fault detection in satellite electrical power system

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

eess.SY↗