International space station (ISS) is a grand invention for human beings to have a chance at exploring the outer space. Its operation is completely dependent on the autonomous power distribution system which transforms energy by solar arrays from the sun. There is a high demand for a reliable monitoring system that can accurately and timely detect and localize faults in its power system for the special working environment of the ISS. In this paper, a fault detection and localization (FDL) based on multi-dimensional time-series trend extracted shapelet (MTES) method was proposed. A fast shapelet discovery was created to accelerate the process of extracting shape features from time series signals collected from the ISS electrical power distribution system (EPDS). Then the techniques of randomization and information gain were exploited for the further shapelet selection. Finally, multi-dimensional time-series classification for FDL was solved by a designed random forest classifier. The real-time FDL measurement instrument was emulated on the Xilinx VCU128 FPGA board, while a hardware-in-the-loop (HIL) testing platform was established to verify the effectiveness, execution speed, and accuracy of the MTES method. Comparing with other state-of-the-art data-driven methods, higher accuracy (above 96%) and easier hardware implementation were achieved using MTES.