中国电机工程学报2025，Vol.45Issue(11)：4305-4317,13.DOI:10.13334/j.0258-8013.pcsee.232540
基于多源数据的配变低压侧谐波估计方法
Harmonic Estimation Method for Low Voltage Side of Distribution Transformer Based on Multi-source Data
摘要
Abstract
To address the issue of insufficient harmonic measurements on the low-voltage side of distribution transformers,this study proposes a deep learning-based method for estimating harmonics on the low-voltage side of distribution transformers by combining short-term test data and long-term power data.Initially,a method combining Fisher's optimal segmentation and the derivative dynamic time warping algorithm is used to identify the harmonic-dominant users.Subsequently,a data transformation method combining variational mode decomposition and Gramian angular field is proposed to convert the power signal of harmonic-dominant users and the harmonic signal on the low-voltage side of distribution transformers into pseudo-color Gramian power images and gray Gramian harmonic images.Finally,these two types of images are input into an improved PSRGAN(pix2pix-Super-resolution generative adversarial network)model for training to learn the mapping relationship between the power data of the harmonic source user and the harmonic data on the low-voltage side of distribution transformers,enabling the generation of long-term monitoring data for harmonics on the low-voltage side of distribution transformers.The accuracy of the proposed method is validated through simulation models and real measurement cases,and the required data are easily obtainable,demonstrating the practicality of the method for engineering.关键词
多源数据/变分模态分解/格拉姆变换/改进生成对抗模型/谐波估计Key words
multisource data/variational modal decomposition/gram transform/improved generative adversarial model/harmonic estimation分类
动力与电气工程引用本文复制引用
张逸,林楠,刘必杰,欧杰宇,黄雁..基于多源数据的配变低压侧谐波估计方法[J].中国电机工程学报,2025,45(11):4305-4317,13.基金项目
国家自然科学基金项目(51777035) （51777035）
福建自然科学基金项目(2020J01123) （2020J01123）
宁夏回族自治区自然科学基金项目(2023AAC03834).Project Supported by National Natural Science Foundation of China(51777035) （2023AAC03834）
Natural Science Foundation of Fujian Province(2020J01123) （2020J01123）
Natural Science Foundation of Ningxia Hui Autonomous Region(2023AAC03834). （2023AAC03834）
