As the scale of wind farms expands, the high-frequency data generated by wind turbine SCADA systems has become crucial for real-time status monitoring and fault diagnosis. However, traditional data processing methods are inefficient and heavily reliant on manual intervention, limiting operational response speed. To address this issue, this study has developed a wind turbine SCADA data interpreter based on a Large Language Model (LLM), aimed at automating data analysis, reducing manual involvement, and improving processing efficiency. This interpreter incorporates automated tools and employs iterative optimization processes, facilitating the automatic recognition of the real-time operational status of wind turbines. Consequently, this enhances the optimization of turbine operating conditions and improves energy efficiency. Experimental results demonstrate that this interpreter significantly outperforms traditional methods in terms of fault detection accuracy and operational efficiency, thereby enhancing the safety and intelligent management of wind farms. With an interactive interface provided, operators can monitor turbine status in real-time, supporting decision-making for optimized scheduling.