Source-Free Domain Adaptation Object Detection (SFOD) aims to adapt a source-pretrained detector to the target domain, using only unlabeled target domain data and without any data from the source domain. Most existing methods follow the Mean-Teacher self-training paradigm. However, the inherent domain shift between the source pretrained model and the target domain data results in noisy and false pseudo-labels, limiting detection performance. To address this problem, we propose an improved Mean-Teacher method, Teacher-Student Consistency Distillation (TSCD), which introduces a feature distillation regularization term to enhance the consistency of the Mean-Teacher framework. Specifically, we first introduce a feature fusion-alignment mechanism. The feature fusion network cascading multiple attention fusion modules aggregates domain-invariant knowledge for the student network. Aligning fused features can implicitly provide the cross-level consistency. Then, we design a novel feature distillation loss, mining the easily ignored regions caused by domain shift. Finally, we introduce a weighting strategy for the distillation loss, which dynamically allocates weights to each sample pair. Extensive experiments on multiple SFOD benchmarks show that our proposed method achieves competitive performance compared to related methods, demonstrating the effectiveness of our Teacher-Student Consistency Distillation method.