多维度Transformer算法与电磁暂态建模融合驱动的PMSG故障辨识方法OA

As the core power generation component in wind power systems,the Permanent Magnet Synchronous Generator(PMSG)can significantly impact the entire system upon failure,making timely fault diagnosis and identification crucial for system maintenance.Addressing current challenges in PMSG internal fault identification-such as scarce and imbalanced real fault data,complex internal signal processing,and insufficient deep fault feature extraction-this paper proposes a PMSG fault identification method driven by the integration of multi-dimensional Transformer algorithms and electromagnetic transient modeling(EMT).First,an electromagnetic transient equivalent model of PMSG covering five typical internal faults is constructed on the PSCAD/EMTDC platform,with magnetic flux as the core state variable.Integrated into a wind turbine under multiple operating conditions,it generates a comprehensive and balanced dataset for model training.Then,a multi-dimensional Transformer encoder is proposed,employing parallel time-frequency feature extraction and a gating mechanism for fault classification.Test results demonstrate that the proposed method achieves 99.8%identification accuracy with 60%faster training convergence than traditional models,exhibiting excellent identification performance.