【作者】 蔡志强；

【导师】 吕卫； 国狄非；

【作者基本信息】 天津大学 ， 电子与通信工程， 2017， 硕士

【摘要】 智能微网在智能电网的发展中扮演着重要的角色,基于用户的用电详情分析伴随着新一代电网的发展和研究而备受人们关注。智能电表作为一种非侵入式设备,能够在用户总线上获取用户家中的各个用电负荷的电力信息,对电力数据加以分析,可以将结果反馈给用户,使用户得知更详细的用电细节信息,以实现节约用电的目的。本文设计了一款智能电表,并基于该智能电表对数据进行分析处理,实现对用户家中负荷的在线分解。本文所研究的系统包括智能电表数据采集系统,智能电表上位机模块、智能电表数据分析算法三个方面。设计的智能电表具备电力数据的采集、数据存储、传输和多功能菜单显示等功能。上位机是实现智能电表与PC端之间的双向通信软件,通过上位机对智能电表进行系统参数的设置。本文基于智能电表的数据分析算法包括基于电流的暂态事件检测和基于暂态电流特征的负荷识别两类算法。在暂态事件检测中,首先获取电流周期最大值信号序列,使用滑动窗残差模型对负荷的投切事件进行监测。为了缩小负荷识别的范围,将暂态事件对应的负荷划分为非电阻型(非R型)和电阻型(R型)。本文针对生活中大多数的非R型负荷提出了两种负荷识别算法,第一种方法是提取暂态电流的多维度波形特征与S变换的谐波幅值特征,并使用典型相关分析对这两种暂态电流特征进行特征融合形成特征集,第二种方法是直接使用双向二维主成分分析对暂态电流S变换矩阵进行压缩处理。最后使用支持向量机对公开电力数据集6类负荷进行分类。实验结果表明:该两种算法对于家用负荷识别都具备较高准确率。特别是对于电气特性相似负荷的在线分解,两种算法都具备一定的优势。更多还原

【Abstract】 Smart micro-grids play an important role in the development of smart grids,they have drawn much attention due to the development and research of a new generation of power grids based on the details of the users’ electricity consumption.As a non-invasive device,the smart meter can obtain the power information of each electricity load in the user’s home by analyzing the power data,and can give the result to the user so that the user can learn more detailed electricity consumption details information from the smart meter.A smart meter is designed in this thesis,and the data is processed based on the smart meter.The smart meter is able to achieve the user’s home online load decomposition in order to achieve the purpose of saving electricity.The system in this thesis includes three aspects: smart meter data acquisition system,connection module between smart meter and PC,smart meter data analysis algorithm.The Smart meter is used for power data collection,data storage,transmission and multi-function menu display.Upper computer software is to achieve two-way communication and set the smart meter system parameters between PC-side and smart meters.The data analysis algorithm based on smart meters includes two types of algorithms: current-based transient event detection and load identification based on transient current characteristics.In the transient event detection,the maximum current signal sequence is obtained first,and the sliding window residual model is used to monitor the switching events of the load.In order to reduce the range of load identification,the load corresponding to transient events is divided into non-resistance type(non-R type)and resistance type(R type).Two load identification algorithms for most non-R loads in life are proposed is this thesis.The first method is to extract the multi-dimensional waveform characteristics of transient current and the harmonic amplitude characteristics of S-transform,and use Canonical Correlation Analysis to combine these two types of features.The bi-directional 2DPCA is used to compress the transient current S-transform matrix in the second method.Finally,six categories from BLUED dataset are classified by SVM.Experimental results show that both algorithms achieve better classification results.Both algorithms have some advantages especially for on-line decomposition of similar electrical loads.更多还原