<div>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. </div><div><br></div><div>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</div><div>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.</div>