基于庞加莱映射与多元特征融合构图的轻量化负荷识别方法  

A Lightweight Load Identification Method Based on PoincaréMapping and Multivariate Feature Fusion Composition

作　　者：梁志泓 罗庆全 余涛[1] 霍富铭 蓝超凡 梁敏航 潘振宁[1] LIANG Zhihong;LUO Qingquan;YU Tao;HUO Fuming;LAN Chaofan;LIANG Minhang;PAN Zhenning(School of Electric Power Engineering,South China University of Technology,Guangzhou 510640,Guangdong Province,China)

机构地区：[1]华南理工大学电力学院,广东省广州市510640

出　　处：《电网技术》2024年第11期4710-4719,I0068-I0074,I0067,共18页Power System Technology

基　　金：国家自然科学基金项目(52207105);广州市基础研究计划基础与应用基础研究项目(SL2022A04J01135)。

摘　　要：随着新型电力系统对需求侧资源调控要求的提高,负荷识别作为负荷精细化管理的关键技术愈发受到重视。而现有方法仍可进一步丰富负荷电气量周期性表征角度、提升多元特征融合能力以提高模型性能,降低识别模型复杂度以增强实用性。因此,提出了一种基于庞加莱映射与多元特征融合构图的轻量化负荷识别方法。首先,采用庞加莱映射在相空间角度表征负荷运行的周期轨迹特征,构建原始电流、无功电流的U-I轨迹以刻画多维动态特性,更基于二进制编码嵌入多种电气专家特征,融合生成强区分性的彩色特征图像。再构建特征学习能力强且轻量化的MobileOne负荷识别模型,通过重参数化策略进一步降低模型计算复杂度并缩短推理时延以大幅提高边缘部署的可用性。在公开数据集PLAID和WHITED中进行实验,所提方法的准确率与宏F_1分数均高于现有方法,且相比现有基于图像化特征的负荷识别模型,至多可减少99.23%的参数量、98.27%的浮点数运算和89.15%的推理时延。With the improvement of demand-side resource regulation requirements of new power system,load identification as a key technology for rigorous load management has received more and more attention.However,the existing methods can further enrich the perspectives of periodic characterization of load electrical quantity,enhance the ability of multivariate feature fusion to improve the model performance,and reduce the complexity of the identification model to enhance the practicality.Therefore,a lightweight load identification method is proposed based on Poincarémapping and multivariate feature fusion composition.Firstly,Poincarémapping is used to characterize the periodic trajectory of load operation from the phase space perspective.The U-I trajectories of raw and reactive currents are constructed to portray the multidimensional dynamic characteristics.Multiple electrical expert features are embedded based on binary coding.Fuse the elements above to generate a color feature image with strongly distinguishable properties.MobileOne,the lightweight load identification model with strong feature learning capability,is then constructed.The computational complexity of the model is further reduced,and the inference latency is shortened by a reparameterization strategy to improve the availability of edge deployment significantly.Experiments are conducted in the public datasets PLAID and WHITED,and the proposed method achieves higher accuracy and F1-macro than the existing methods and reduces up to 99.23%of the number of parameters,98.27%of the floating-point operations,and 89.15%of the inference latency compared with the existing load identification models based on image-based features.

关 键 词：负荷识别 庞加莱映射 U-I轨迹 轻量化模型 重参数化 

分 类 号：TM73[电气工程—电力系统及自动化]