全球能源互联网（英文）2024，Vol.7Issue(3)：270-283,14.DOI:10.1016/j.gloei.2024.06.002
基于图像识别的农村光伏台区负荷分布建模方法研究
Modeling load distribution for rural photovoltaic grid areas using image recognition
摘要
Abstract
Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the"carbon peaking and carbon neutrality"objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.关键词
深度学习/遥感图像识别/光伏开发/负荷分布建模/潮流计算Key words
Deep learning/Remote sensing image recognition/Photovoltaic development/Load distribution modeling/Power flow calculation引用本文复制引用
周宁,尚博文,张金帅,徐铭铭..基于图像识别的农村光伏台区负荷分布建模方法研究[J].全球能源互联网（英文）,2024,7(3):270-283,14.基金项目
This work was supported by the State Grid Science&Technology Project of China(5400-202224153A-1-1-ZN). （5400-202224153A-1-1-ZN）
