摘要
新型电力系统中电能质量扰动问题愈加复杂和严重,多种电能质量扰动同时出现,导致传统算法识别准确率降低。提出一种基于马尔可夫变迁场和EfficientNet的复合电能质量扰动识别算法。采用马尔可夫变迁场将电能质量扰动信号可视化映射为二维特征图像;通过EfficientNet卷积神经网络处理图像数据,实现扰动信号的特征提取;利用神经架构搜索自动调节卷积神经网络超参数进行网络训练,建立电能质量扰动分类识别模型。仿真结果表明,所提方法能够准确高效地提取扰动信号特征,对复合电能质量扰动分类效果好且抗噪声能力强。
As the new power system is being built with new energy as its main body,the number of power electronic devices connected to the grid is increasing day by day.The resultant power quality disturbances have become increasingly complex and severe,with multiple types of power quality disturbances occurring simultaneously,leading to a decrease in the recognition accuracy of traditional algorithms.To address this issue,this paper proposes a composite power quality disturbance identification algorithm based on Markov transition field and EfficientNet.Firstly,the Markov transition field is used to visualize and map power quality disturbance signals into two-dimensional feature images;secondly,the image data is processed by EfficientNet Convolutional neural network to realize the feature extraction of disturbance signal;Finally,neural architecture search is used to automatically adjust the super parameters of Convolutional neural network for network training,and a classification and recognition model of power quality disturbances is established.The simulation results show that the proposed method can accurately and efficiently extract disturbance signal features,and has good classification performance for composite power quality disturbances and strong noise resistance.
作者
付宽
王洪新
刘杰
郭靖
唐志勇
欧洋
陈家乐
FU Kuan;WANG Hongxin;LIU Jie;GUO Jing;TANG Zhiyong;OU Yang;CHEN Jiale(State Grid Xinjiang Aksu Power Supply Company,Aksu 843000,Xinjiang,China;School of Electrical Engineering,Sichuan University,Chengdu 610065,Sichuan,China)
出处
《电网与清洁能源》
CSCD
北大核心
2024年第4期74-83,共10页
Power System and Clean Energy
基金
国家自然科学基金项目(52277113)
国网新疆电力有限公司科技项目(D230AD230010)。
关键词
电能质量
电能质量扰动识别
马尔可夫变迁场
卷积神经网络
特征提取
模式识别
power quality
identification of power quality disturbances
Markov transition field
convolutional neural network
feature extraction
pattern recognition
