复杂设备环境下的多状态负荷运行状态辨识方法  

Identification approach of multi-state load operating conditions in complex equipment environments

作　　者：柳青 刘小平 陈浩 张振宇 朱彦卿[2] 李勇[2] LIU Qing;LIU Xiaoping;CHEN Hao;ZHANG Zhenyu;ZHU Yanqing;LI Yong(Power Supply Service Center,State Grid Hunan Electric Power Company Limited,Changsha 410004,China;School of Electrical and Information Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]国网湖南省电力有限公司供电服务中心,长沙410004 [2]湖南大学电气与信息工程学院,长沙410082

出　　处：《电测与仪表》2024年第2期55-62,共8页Electrical Measurement & Instrumentation

基　　金：国家电网有限公司总部科技项目(5216A019000S)。

摘　　要：非侵入式负荷监测(NILM)是智能用电行为辨识中的关键组成部分。由于中压配电网下的负荷同时接入种类繁多,并且多具备变频功能,不具备恒功率特性,现有的聚焦于家庭中的负荷辨识方法难以直接应用在类似的复杂设备环境中。文中针对复杂设备环境中的负荷特点,选取了电梯作为典型负荷进行了负荷辨识实验,使用符合IEC 61000-4-30的测量数据作为输入,目标为辨识电梯是否处于运行状态。为了消除无关特征造成的运算压力,提出了基于皮尔逊相关系数的差分特征提取方法,结合卷积神经网络实现了实际含多未知负荷环境中的电梯负荷状态辨识。使用实测数据的结果表明,该方法仅需少量样本辨识出运行功耗变化复杂的电梯运行状态,且计算精确度要高于传统机器学习方法。Non-intrusive load monitoring(NILM)is a key component of intelligent electricity consumption behavior recognition.Due to the wide variety of simultaneous load accesses in medium-voltage distribution networks,and the fact that many of them have frequency conversion functions and do not have constant power characteristics,it is difficult to apply the existing load recognition methods focusing on households directly to complex equipment environments.In this paper,a load identification experiment is conducted with an elevator as a typical load in the complex equipment environments,IEC 61000-4-30 measurement data is used as input to identify whether the elevator is in operation.In order to eliminate the computational pressure caused by irrelevant features,a differential feature extraction method based on correlation coefficient of Pearson is proposed,which is combined with a convolutional neural network to realize the elevator load state identification in a real environment with multiple unknown loads.The results using the measured data show that the proposed method requires only a small number of samples to identify the elevator operating state with complex changes in operating power consumption,and the computational accuracy is higher than that of traditional machine learning methods.

关 键 词：非侵入式负荷监测 卷积神经网络 差分特征提取 中压配电网 

分 类 号：TM714[电气工程—电力系统及自动化]