The existing image retrieval methods struggle to distinguish and extract similar structural information and texture details of power equipment effectively， resulting in low retrieval accuracy and efficiency. To solve these problems， a Power Image Retrieval method based on improved Swin Transformer （PIR-iSwinT） was proposed. Firstly， a Multi-Feature Structure Cross-Enhancement module （MFSCE） was introduced to enhance the model's perception ability of equipment structural and edge features by combining cross-attention mechanism of the gradient magnitude map. Secondly， an Adaptive Inter-class Difference Center Loss module （AIDCL） was designed to strengthen the model's ability to distinguish between similar and dissimilar samples. Finally， a Hierarchical Clustering Retrieval module （HCR） was constructed to optimize the sample matching strategy during retrieval and reduce computational complexity， thereby further enhancing retrieval accuracy and efficiency. Experimental results on the self-built power scenario dataset and the NUS-WIDE dataset show that PIR-iSwinT achieves the mean Average Precision （mAP） of 96.76% and 92.68%， respectively， at a 32 bit hash code length， outperforming HRMPA （Hash image Retrieval based on Mixed attention and Polarization Asymmetric loss） by 2.35% and 0.56%， respectively. It can be seen that PIR-iSwinT extracts and distinguishes detailed structural features of power equipment effectively， enhances retrieval efficiency， and demonstrates good generalization capability， verifying effectiveness of the proposed method.