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基于DAG-SVMS的非侵入式负荷识别方法
2021年电子技术应用第10期
王 毅1,2,徐元源1,李松浓2
1.重庆邮电大学 通信与信息工程学院,重庆400065;2.国网重庆市电力公司电力科学研究院,重庆404100
摘要: 在供电入口处嵌入非侵入式负荷识别技术,有利于推动建筑节能、实现电网负荷预测、开发智能楼宇、完善智能电网体系建设。据此,提出一种基于有向无环图支持向量机(Directed Acyclic Graph Support Vector Machines,DAG-SVMS)的负荷辨识方法。首先,对总线电流信号进行事件检测,检测到暂态事件后,分离目标负荷暂态电流波形,提取特征,然后,将特征输入预先训练好的DAG-SVMS模型进行分类识别。为提升分类器性能,使用粒子群优化PSO(Particle Swarm Optimization)算法优化DAG-SVMS分类器的参数。为减小累积误差,提出Gini指数优化DAG-SVMS节点顺序的策略。实验结果表明,文中方法识别准确率高,识别速度快,具有可行性。
中图分类号: TN915
文献标识码: A
DOI:10.16157/j.issn.0258-7998.211451
中文引用格式: 王毅,徐元源,李松浓. 基于DAG-SVMS的非侵入式负荷识别方法[J].电子技术应用,2021,47(10):107-112.
英文引用格式: Wang Yi,Xu Yuanyuan,Li Songnong. Non-intrusive load identification method based on improved directed acyclic graph support vector machines[J]. Application of Electronic Technique,2021,47(10):107-112.
Non-intrusive load identification method based on improved directed acyclic graph support vector machines
Wang Yi1,Xu Yuanyuan1,Li Songnong2
1.School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications, Chongqing 400065,China; 2.Chongqing Electric Power Research Institute,Chongqing 404100,China
Abstract: Embedding non-intrusive load identification technology in the power supply entrance is conducive to promote building energy saving, realize power grid load forecasting, develop intelligent buildings and improve the construction of smart grid system. Therefore, this paper proposes a non-intrusive power load identification method based on directed acyclic graph support vector machines(DAG-SVMS). Firstly, the event detection of power system bus current signal is carried out. After the transient event is detected, the transient current waveform of the target load is separated and the features are extracted. Then, the features are input into the pre trained DAG-SVMS model for classification and identification. In order to improve the performance of the classifier, particle awarm optimization(PSO) algorithm is used to optimize the parameters of the DAG-SVMS model. In order to reduce the cumulative error, Gini index is proposed to optimize the node order of DAG-SVMS. The experimental results show that the proposed method has high recognition accuracy, fast recognition speed and feasibility.
Key words : non-intrusive load identification;transient event;DAG-SVMS model;Gini index;PSO algorithm

0 引言

    智能电网建设是以提高生态可持续性、供电安全性和经济竞争力为目标[1],表现为提高负荷监测技术、提高终端用户响应速度、提高需求侧的节约能效、提供智能控制技术、分布式能源的自由接入[2]非侵入式负荷识别作为非侵入式负荷监测的核心内容,在不改变用户电路结构的条件下,通过测量总负荷数据,即可获得系统内具体用电负荷的数量、类别、运行状态信息,安装和维护成本低,易于推广。该技术的实现,可为用户、电力公司以及设备提供参考[3]。用户端,用户用电信息得到反馈,提升节能意识,规范用电行为。电力公司端,能提高负荷预测的精确度,实现有效的负荷规划、电能调度。对设备制造商来说,可据此识别出故障或低效设备,加快技术革新,推动高能效设备研发。




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作者信息:

王  毅1,2,徐元源1,李松浓2

(1.重庆邮电大学 通信与信息工程学院,重庆400065;2.国网重庆市电力公司电力科学研究院,重庆404100)




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