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文章摘要
基于系统模型的家电负荷辨识算法*
An appliance load identification algorithm based on system model
Received:May 08, 2017  Revised:July 27, 2017
DOI:
中文关键词: 负荷辨识  稳态数据  系统模型  预筛选  模型库
英文关键词: load identification, steady state date, system model, prescreen, model base
基金项目:国家重点研发计划项目课题资助(2016YFB0901104);中央高校基本科研业务费专项资金资助项目(2016MS13)
Author NameAffiliationE-mail
QI Bing School of Electrical and Electronic Engineering,North China Electric Power University qbing@ncepu.edu.cn 
LIU Liya* School of Electrical and Electronic Engineering,North China Electric Power University liuliyaa@foxmail.com 
HAN Lu School of Electrical and Electronic Engineering,North China Electric Power University free_hanlu@163.com 
WANG Lili State Grid Materials Co.Ltd wanglili@sgm.sgcc.com.cn 
RUAN Wenjun State Grid Jiangsu Electric Power Company liuliyaa@foxmail.com 
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中文摘要:
      家电负荷识别是需求侧管理的关键技术之一,有助于实现用户侧的智能用电。本文结合系统辨识的基本原理和方法,将各家电负荷看作一个独立的系统,以稳态电压、稳态电流为特征,提出一种基于系统模型的家电负荷辨识算法。通过预先获取用电网络中各负荷的稳态数据,构建ARMAX线性模型库和Hammerstein非线性模型库。根据稳态电流波峰系数这一特征值对待识别负荷进行预筛选确定所属模型库类型,通过模型匹配原则进行负荷识别。本文通过实测数据验证了算法的有效性,可以准确地识别线性负荷以及非线性负荷,运算效率高,并且可以有效应对家庭网络中有新负荷加入的情况。
英文摘要:
      Household load identification is one of the key techniques in demand side management, which is helpful to realize intelligent power utilization. Combined with the basic principle and method of system identification, each appliance load is seen as an independent system, and a new method of appliance load identification based on system model is proposed in this paper, which is characterized by steady-state current and steady-state voltage. The steady state data of each load in the power network is collected via priori approach in order to construct the ARMAX linear model library and the Hammerstein nonlinear model library. Computing the characteristic value of steady current crest, which can prescreen the load to identify which library it belongs to. Then the load identification is carried out by the model matching principle. The effectiveness of the algorithm is verified by the actual sampling load data, which can accurately determine the load state for both linear loads and nonlinear loads. Furthermore, the algorithm is efficient and can effectively cope with the situation when the new load joins the home network.
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