A Pre-trained Language Model-based Anomaly Detection Method for Grid Maintenance Plans
[Objective/Significance]Anomaly detection methods are designed to detect anomalies in grid maintenance plans,which are crucial for the normal implementation of grid maintenance and the stable operation of the grid system.Existing methods primarily rely on manual discernment,leading to issues of low accuracy and inefficiency.[Methods/Processes]This paper presents an anomaly detection method for grid maintenance plans based on pre-trained language models(PLMs).The method uses grid maintenance plan data to fine-tune the PLMs,so that the PLMs can obtain professional knowledge in related fields,while fully leveraging the contextual awareness and domain adaptability of PLMs.The combination of the two aspects enables the PLMs can deeply understand the complex context of the maintenance plans and detect anomalies in them.[Results/Conclusions]Experimental results demonstrate that the proposed method effectively enhances the performance of anomaly detection in grid maintenance plans.
Pre-trained Language ModelAnomaly DetectionGrid ManagementText Classification
万方数据
维普
