In view of the inaccurate and incomplete fault data in the accumulated telemetry data of space power system, it is difficult for the ground long-time management system to comprehensively select and evaluate the effectiveness of fault detection model according to actual fault data. This paper focuses on the research on the optimal selection method of twin data-driven fault detection model for space power system. Based on the full analysis of the composition, working principle and input-output relationship of the power system, the digital twin model of each component unit of the spacecraft power system is constructed by Simulink. Combined with the analysis of fault mechanism, typical faults are injected into the twin model to enrich the types and quantity of fault data, and the effectiveness of various fault detection models is evaluated based on the twin data. Experiments show that the twin data generated based on this framework are more than 90%, which is similar to the real telemetry data, and six typical failure modes can be injected, where the step-type and gradient-type fault detection ability of the fault detection model can be effectively evaluated. The research of this method can effectively serve the actual ground long-time management system and provide an important model and data basis for the selection of effective fault detection model.