兵工自动化2025，Vol.44Issue(10)：21-25,5.DOI:10.7690/bgzdh.2025.10.005
基于混合深度学习的微电网检测模型
Microgrid Detection Model Based on Hybrid Deep Learning
吴方权 1刘亦驰 1汤成佳1
作者信息
- 1. 贵州电网有限责任公司信息中心,贵阳 550003
- 折叠
摘要
Abstract
In order to solve the problems of long computation time and low fault detection accuracy in current microgrid protection schemes,a microgrid fault detection model based on hybrid deep learning is proposed.The feature advancer is used to mine the signal information of power data,and the deep convolutional neural network is used to effectively extract the feature information of power fault data,and the AdaBoost classifier is used to classify the fault.Experimental results show that,contrary to the convolutional neural network(CNN)and AlexNet,the proposed hybrid deep learning detection model has higher training performance;Compared with SVM,LR,CNN and AlexNet models,the proposed hybrid deep learning model has better comprehensive index performance,and the fault detection accuracy can reach 98%.关键词
微电网/故障检测/深度学习/特征提取/分类器Key words
microgrid/fault detection/deep learning/feature extraction/classifier分类
信息技术与安全科学引用本文复制引用
吴方权,刘亦驰,汤成佳..基于混合深度学习的微电网检测模型[J].兵工自动化,2025,44(10):21-25,5.
