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Contributions to Autonomous Operation of a Deep Space Vehicle Power System

thesis
posted on 14.12.2020, 22:44 by Pallavi Madhav Kulkarni
The electric power system of a deep space vehicle is mission-critical, and needs to operate autonomously because of high latency in communicating with ground-based mission control. Key tasks to be automated include managing loads under various physical constraints, continuously monitoring the system state to detect and locate faults, and efficiently responding to those faults.

This work focuses on three aspects for achieving autonomous, fault-tolerant operation in the dc power system of a spacecraft. First, a sequential procedure is proposed to estimate the node voltages and branch currents in the power system from erroneous sensor measurements. An optimal design for the sensor network is also put forth to enable reliable sensor fault detection and identification. Secondly, a machine-learning based approach that utilizes power-spectrum based features of the current signal is suggested to identify component faults in power electronic converters in the system. Finally, an optimization algorithm is set
forth that decides how to operate the power system under both normal and faulted conditions. Operational decisions include shedding loads, switching lines, and controlling battery charging. Results of case studies considering various faults in the system are presented.

Funding

NASA STTR Phase II contract no. 80NSSC18C0219

History

Degree Type

Master of Science in Electrical and Computer Engineering

Department

Electrical and Computer Engineering

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Dionysios Aliprantis

Additional Committee Member 2

Scott Sudhoff

Additional Committee Member 3

Ilias Bilionis