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首页> 外文期刊>IEEE Transactions on Instrumentation and Measurement >Development of an Improved Time–Frequency Analysis-Based Nonintrusive Load Monitor for Load Demand Identification
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Development of an Improved Time–Frequency Analysis-Based Nonintrusive Load Monitor for Load Demand Identification

机译：改进的基于时频分析的非侵入式负荷监控器，用于负荷需求识别

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摘要

In a smart house connected to a smart grid via advanced metering infrastructure, a nonintrusive load monitor (NILM) that identifies individual appliances by disaggregating composite electric load signal from the minimal number of sensors installed at the main distribution board in the field can be regarded as a part of a home/building energy management system. This type of load monitoring technique, not only for domestic but also for industrial applications, is relevant to electricity energy management and conservation issues. In this paper, an improved time–frequency analysis-based NILM composed of three system components, including data acquisition, transient feature extraction, and load identification, is proposed. The improved NILM proposed in this paper incorporates a multiresolution S-transform-based transient feature extraction scheme with a modified 0–1 multidimensional knapsack algorithm-based load identification method to identify individual household appliances that may either be energized simultaneously or be identified under similar real power consumption. For the load identification process, an ant colony optimization algorithm is employed to perform combinatorial search that is formulated as a modified 0–1 multidimensional knapsack problem. As shown from the experimental results, the improved NILM strategy proposed in this paper is confirmed to be feasible.
机译：在通过先进的计量基础设施连接到智能电网的智能房屋中，非侵入式负载监控器（NILM）可通过从现场安装在主配电板上的最少数量的传感器分解复合电力负载信号来识别单个设备家庭/建筑能源管理系统的一部分。这种类型的负载监控技术不仅适用于家庭，而且适用于工业应用，与电能管理和节约问题有关。本文提出了一种改进的基于时频分析的NILM，该NILM由三个系统组件组成，包括数据采集，瞬态特征提取和负载识别。本文提出的改进的NILM结合了基于多分辨率S变换的瞬态特征提取方案和基于0–1改进的基于背包的改进算法的负荷识别方法，以识别可同时通电或在类似真实环境下被识别的单个家用电器。能量消耗。对于负载识别过程，采用蚁群优化算法来执行组合搜索，该搜索被公式化为经过修改的0–1多维背包问题。实验结果表明，本文提出的改进的NILM策略是可行的。

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