【摘要】：負(fù)荷監(jiān)測是獲取用戶詳細(xì)的用電情況、用電行為和開展開節(jié)能工作的基礎(chǔ),供電企業(yè)通過分析用戶行為制定合理的需求響應(yīng)策略,正確引導(dǎo)用戶用電,保證電力供應(yīng)的穩(wěn)定性和經(jīng)濟(jì)性,用戶通過了解供電情況、電網(wǎng)政策以及自身精確的用電信息,合理安排自身用電行為降低能源的消耗,減少開支。非侵入式負(fù)荷監(jiān)測方法只需要在被監(jiān)測系統(tǒng)電力入口處安裝信息采集裝置,并運(yùn)用合適算法對用電數(shù)據(jù)進(jìn)行處理和分析,就能獲取系統(tǒng)內(nèi)各個(gè)負(fù)荷的用電信息,有效的解決了監(jiān)測設(shè)備安裝難、維護(hù)難及管理難等問題,是未來負(fù)荷監(jiān)測發(fā)展的方向。本文主要研究了一種非侵入環(huán)境下的家用電器負(fù)荷在線參數(shù)辨識方法,從負(fù)荷特性入手,就負(fù)荷投切辨識、特征提取和負(fù)荷分類辨識等方面進(jìn)行了研究,并完成了家用電器負(fù)荷在線參數(shù)辨識演示系統(tǒng)搭建,具體工作如下：(1)闡述了家用電器負(fù)荷在線參數(shù)辨識方法的研究背景與意義,研究國內(nèi)外負(fù)荷監(jiān)測系統(tǒng)和家電負(fù)荷特征提取與分類的相關(guān)現(xiàn)狀,通過智能電網(wǎng)方法論的方案從用戶需求出發(fā),研究非侵入式負(fù)荷監(jiān)測系統(tǒng)的優(yōu)勢、物理架構(gòu)和工作原理；(2)建立負(fù)荷采集硬件系統(tǒng),完成常見家用電器單一運(yùn)行數(shù)據(jù)、混合運(yùn)行數(shù)據(jù)的采集,分析負(fù)荷用電特征,以負(fù)荷電流、電壓、有功、無功、高次諧波含量、相角等多維特征為先驗(yàn)訓(xùn)練樣本,結(jié)合負(fù)荷硬件結(jié)構(gòu),挖掘不同負(fù)荷的獨(dú)有特征；(3)研究基于負(fù)荷投切狀態(tài)的投切識別技術(shù),利用主成分分析法對8種典型電器的負(fù)荷特征樣本進(jìn)行降維處理,得到最優(yōu)辨識特征,根據(jù)負(fù)荷評估值建立二層分類器,通過Fisher有監(jiān)督線性判別將家用電器的主特征值矩陣在一維空間解聚,建立一層分類器,實(shí)驗(yàn)環(huán)境下使用Matlab下完成分類器設(shè)計(jì)。(4)使用虛擬儀器完成家用電器在線參數(shù)辨識系統(tǒng)搭建,實(shí)現(xiàn)登陸、電能監(jiān)測、開斷狀態(tài)監(jiān)測、單負(fù)荷運(yùn)行狀態(tài)顯示、歷史數(shù)據(jù)查詢以及通信功能,更加方便、直觀的演示本文所提出的負(fù)荷在線參數(shù)辨識方法。
[Abstract]:Load monitoring is the basis for obtaining users' detailed electricity consumption situation, power consumption behavior and energy saving work. Power supply enterprises make reasonable demand response strategies by analyzing user behavior, and guide users to use electricity correctly. In order to ensure the stability and economy of power supply, users can reasonably arrange their own power consumption and reduce energy consumption and expenditure by understanding the power supply situation, power grid policies and their own accurate power consumption information. The non-intrusive load monitoring method only needs to install the information collection device at the power entrance of the monitored system, and use the appropriate algorithm to process and analyze the power consumption data, so that the power consumption information of each load in the system can be obtained. It effectively solves the problems of difficult installation, maintenance and management of monitoring equipment, which is the development direction of load monitoring in the future. In this paper, a method of on-line parameter identification for household appliances under non-intrusive environment is studied. Starting with the load characteristics, the load switching identification, feature extraction and load classification identification are studied. The main work is as follows: (1) the research background and significance of on-line parameter identification method for household appliance load are described. This paper studies the current situation of load monitoring system and home appliance load feature extraction and classification at home and abroad, and studies the advantages, physical structure and working principle of non-intrusive load monitoring system based on smart grid methodology. (2) establish the hardware system of load collection, complete the collection of common household appliances single operation data and mixed operation data, analyze the characteristics of load electricity, take load current, voltage, active power, reactive power, high-order harmonic content, and make use of load current, voltage, active power, reactive power and high-order harmonic content. The multi-dimensional feature such as phase angle is a priori training sample, combined with load hardware structure, the unique characteristics of different loads are excavated. (3) the switching identification technology based on load switching state is studied. The load feature samples of eight typical electrical appliances are reduced by principal component analysis, the optimal identification characteristics are obtained, and a two-layer classifier is established according to the load evaluation value. The principal eigenvalue matrix of household appliances is deaggregated in one-dimensional space by Fisher supervised linear discriminant, and a classifier is established. In the experimental environment, the classifier is designed by using Matlab. (4) the on-line parameter identification system of household appliances is built with virtual instrument, which realizes the landing, electric energy monitoring, interrupt state monitoring, single load operation status display, and so on. Historical data query and communication functions make it more convenient and intuitive to demonstrate the load online parameter identification method proposed in this paper.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TM925.06

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