False data injection attacks pose serious security threats to cyber-physical power system. Due to the class imbalance property between the attacked samples and the normal samples, machine learning detection methods tend to predict the majority of classes, which affects their detection accuracy of the attacks. Therefore, a false data injection attack detection based on Focal Loss^IM Transformer is proposed. Transformer utilizes itself attention mechanism to capture long-term dependencies in data, thereby identifies imbalanced false data injection attack data. Focal Loss^IM enhances the detection method's ability to identify imbalanced data by introducing modulation factors to better match the distribution and characteristics of false data injection attack samples, thereby improving the detection accuracy of the detection method for attacks.The effectiveness of the proposed method is verified through simulations on IEEE 14⁃node system, IEEE 30⁃node system, and IEEE 57⁃node system. Compared with traditional loss functions and other detection methods, the proposed method exhibits better generalization ability and recognition ability for minority classes, with high recognition accuracy and low false alarm rate.