电力系统保护与控制2024，Vol.52Issue(4)：12-25,14.DOI:10.19783/j.cnki.pspc.230241
基于混合分解多尺度时频图和Res-GRU-AT的电能质量复合扰动识别
Composite PQDs identification based on a hybrid decomposition multi-scale time-frequency map and Res-GRU-AT
摘要
Abstract
The power quality problem in the context of the energy internet is becoming more and more prominent.However,there are several problems in the traditional power quality disturbance(PQD)identification process,such as the signal feature extraction is complex,the algorithm recognition ability is insufficient,and it is difficult to differentiate composite disturbance,etc.Thus a new method—Res-GRU-AT,combining hybrid component multi-scale time-frequency diagram,residual neural network(ResNet),gated recurrent units(GRU)network and attention(AT)mechanism,is proposed for power quality composite disturbance identification.First,the PQDs signals are decomposed at multiple scales using singular spectrum decomposition(SSD)and successive variational modal decomposition(SVMD)respectively to obtain the hybrid components.Then the hybrid components are analyzed by Hilbert-Huang transform(HHT)to obtain the multi-scale time-frequency diagram.Secondly,multi-scale time-frequency diagrams are deeply extracted,strengthened,and recognized using the Res-GRU-AT model.The Res-GRU-AT model can perform feature fusion by using the spatial feature extraction capability for 2D images of ResNet and the temporal feature extraction capability of GRU.Then the feature-weighted enhancement is done by AT to improve the recognition capability of PQDs.Simulation results of different schemes show that the proposed method has strong feature extraction capability,good noise immunity,and high recognition rate of composite perturbation.关键词
电能质量/故障识别/时频分析/混合模式分解/深度学习Key words
power quality/fault identification/time and frequency analysis,hybrid decomposition/deep learning引用本文复制引用
毕贵红,鲍童语,陈臣鹏,赵四洪,陈仕龙,张梓睿..基于混合分解多尺度时频图和Res-GRU-AT的电能质量复合扰动识别[J].电力系统保护与控制,2024,52(4):12-25,14.基金项目
This work is supported by the National Natural Science Foundation of China(No.51767012). 国家自然科学基金项目资助(51767012) （No.51767012）
