The increasing significance of text data in power system intelligence has highlighted the out-of-distribution (OOD) problem as a critical challenge, hindering the deployment of artificial intelligence (AI) models. In a closed-world setting, most AI models cannot detect and reject unexpected data, which exacerbates the harmful impact of the OOD problem. The high similarity between OOD and in-distribution (IND) samples in the power system presents challenges for existing OOD detection methods in achieving effective results. This study aims to elucidate and address the OOD problem in power systems through a text classification task. First, the underlying causes of OOD sample generation are analyzed, highlighting the inherent nature of the OOD problem in the power system. Second, a novel method integrating the enhanced Mahalanobis distance with calibration strategies is introduced to improve OOD detection for text data in power system applications. Finally, the case study utilizing the actual text data from power system field operation (PSFO) is conducted, demonstrating the effectiveness of the proposed OOD detection method. Experimental results indicate that the proposed method outperformed existing methods in text OOD detection tasks within the power system, achieving a remarkable 21.03% enhancement of metric in the false positive rate at 95% true positive recall (FPR95) and a 12.97% enhancement in classification accuracy for the mixed IND-OOD scenarios.