可再生能源2026，Vol.44Issue(8)：1123-1131,9.
基于物理引导多模态映射融合网络的抽水蓄能机组暂态故障诊断策略
Electricity-carbon joint quantity bidding strategy for wind power generation considering wind speed forecast error and transient operation of wind turbine generator
摘要
Abstract
Aiming at the problems of insufficient fusion of multi-source data and inadequate utilization of physical mechanisms in transient fault diagnosis of pumped storage units,this paper proposes a transient fault diagnosis strategy for pumped storage units based on a Physics-guided Multi-Modal Graph Fusion Network(PG-MGFN).This approach deeply integrates physical model constraints,graph representation learning,and adaptive fusion mechanisms to overcome the limitations of traditional Multi-Modal Deep Fusion Networks(MDFN)in complex systems.First,an a priori constraint model is constructed based on the electromagnetic transient characteristics of the unit's operation to preprocess multi-modal data such as vibration,current,and pressure.Raw signals are calibrated using physical indicators to enhance the physical interpretability of features.Second,data from each modality is mapped into dynamic graph structures that include node attributes and cross-modal correlations.Graph Neural Networks(GNNs)are employed to capture hierarchical features within modalities and non-linear coupling relationships between modalities.Finally,a physics-guided attention mechanism is designed to adaptively adjust the fusion weights of multi-modal graph features,achieving directional enhancement of fault features.Experimental results show that compared with the traditional MDFN,PG-MGFN has achieved remarkable improvements in fault diagnosis accuracy,model generalization ability,and robustness.In typical fault diagnosis tasks such as bearing and blade faults,PG-MGFN achieves an average accuracy of 93.2％,which is 18.7％higher than that of the traditional MDFN.It maintains a recognition accuracy of 99.5％in a 20 dB noise environment,verifying its excellent anti-interference capability.This provides new technical support for the intelligent operation and maintenance of pumped storage units.关键词
抽水蓄能机组/暂态故障诊断/多模态融合/物理引导神经网络/图卷积网络Key words
pumped storage units/transient fault diagnosis/multimodal fusion/physically guided neural networks/graph convolutional network分类
能源科技引用本文复制引用
张宏伟,白晓璟,相欢,王智东..基于物理引导多模态映射融合网络的抽水蓄能机组暂态故障诊断策略[J].可再生能源,2026,44(8):1123-1131,9.基金项目
河南新华五岳抽水蓄能发电有限公司科技项目(2018-411522-44-02-005249). （2018-411522-44-02-005249）
