湖北民族大学学报（自然科学版）2024，Vol.42Issue(3)：361-367,7.DOI:10.13501/j.cnki.42-1908/n.2024.06.024
基于凌日搜索优化CNN/BI-GRU的电能质量扰动分类方法
Power Quality Disturbance Classification Method Based on Transit Search Optimization CNN/BI-GRU
摘要
Abstract
Aiming at the problem of low identification accuracy of complex power quality disturbance classification methods,a method for power quality identification and classification based on the transit search optimized multi-modal network model was proposed.Firstly,the Gramian angular field was used to perform data processing on the initial one-dimensional time series signal to obtain two-dimensional image data.Secondly,the time series signal and image data were input into the multi-modal network for feature extraction,and the transit search algorithm was used to optimize the parameters of the multimodal network to improve the feature capture capability of the network.Then,through the feature fusion module,the time series features and image features were fused effectively.Finally,the self-attention mechanism was used to enhance the network model′s ability to understand contextual information.The results showed that the method proposed in this paper had a classification accuracy of 99.2%in a noise-free environment,and an average classification accuracy of 98.3%in different signal-to-noise ratio environments.The proposed method achieves accurate classification of increasingly complex power quality disturbances in new power systems,and it is more robust than traditional classification methods.关键词
电能质量扰动/深度学习/格拉姆角场/特征融合/凌日搜索算法/自注意力机制Key words
power quality disturbance/deep learning/Gramian angular field/feature fusion/transit search algorithm/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
高帅,杨永超,童占北,钟建伟..基于凌日搜索优化CNN/BI-GRU的电能质量扰动分类方法[J].湖北民族大学学报（自然科学版）,2024,42(3):361-367,7.基金项目
湖北省自然科学基金项目(2022CFB264) （2022CFB264）
恩施州技术支撑类科技计划项目(D20230042). （D20230042）
