A study on intelligent anomaly detection in multi-source data using large-scale language models

Anomaly detection in modern power systems requires highly advanced techniques, as the heterogeneous data produced by power systems today can be high-volume and include sensor signals, textual logs, time series, and more. Nevertheless, conventional methods do not adapt well to dynamic data changes, lack structure, and require real-time processing, making them inefficient in more complex grids. This paper proposes a framework running on Qwen2 that leverages multi-source data fusion and cross-modal attention to address these issues. The framework can integrate sensor awareness and text logs via cross-attention, thereby aligning context and effectively finding anomalies. Additionally, it includes dynamic resolution processing to handle high-frequency sensor data and lightweight inference to support edge deployment. Experimental results on a real-world dataset show that the proposed approach delivers a 12% increase in F1-score relative to state-of-the-art models, such as Transformer-AD, and reduces false favorable rates by 50% compared with traditional methods. The framework also provides real-time footprint data with a 85 ms latency per batch, resulting in a scalable smart grid monitoring solution. The paper further develops the following practical applications of large language models in critical infrastructure by addressing deficiencies in heterogeneous data fusion, interpretability, and efficiency, given limited resources. The following plans entail minimizing energy use and scaling the system to identify multiple defect levels, such as cyberattacks and equipment wear.