非侵入式负荷动态识别方法的研究及工程应用

doi:10.11985/2023.03.033

摘要/Abstract

摘要：

随着非侵入式负荷监测技术应用场景不断扩展，负荷类型日趋多样化，基于单层特征的静态识别方法需要构造更加全面、复杂的特征，难以兼顾负荷识别的准确度及速度。提出一种基于多层特征组的动态识别方法，综合考虑不同负荷特征提取的采样频率、监测窗口宽度、计算复杂度和负荷特征存储量等构建分层特征组，针对负荷群中不同的负荷类型提取不同的特征组作为分类特征以降低特征的综合提取代价，最后基于支持向量机多分类算法实现按需识别负荷类型。BLUED数据库的仿真对比分析和实际某工厂的工程测试结果表明，基于多层特征组的动态识别方法不仅能够提高负荷的综合识别速度，也能提升相似负荷的识别准确度，在负荷相似及投切频繁的场景下能够兼顾负荷识别准确度和速度，具有较好的工程适用性。

关键词: 非侵入式负荷监测, 负荷特征分层, 动态识别, 支持向量机多分类算法

Abstract:

As the application scenarios of non-intrusive load monitoring technology continue to expand and load types become increasingly diversified, static identification methods based on single-layer features need to construct more comprehensive and complex features, and it is difficult to give consideration to both the accuracy and speed of load identification. A dynamic identification method based on multi-layer feature groups is proposed, which takes into account the sampling frequency, monitoring window width, computational complexity and load characteristic storage of different load features to construct a hierarchical feature group, extracts different feature groups for different load types in the load group as classification features to reduce the comprehensive extraction cost of features, and finally realizes on-demand load type recognition based on support vector machine multi-classification algorithm. Results from BLUED database simulation and actual engineering test of a factory show that the dynamic identification method based on multi-layer feature groups can increase the identification speed of most loads, while accurately distinguish among similar loads. It can give consideration to the accuracy and speed of load identification under the scenario of similar load and frequent switching, and has good engineering applicability.

Key words: Non-intrusive load monitoring, load feature hierarchy, dynamic identification, SVM multi-classification algorithm

中图分类号:

TM714

刘春蕾, 庞鹏飞, 石纹赫, 孔令号, 黄洵, 戚军. 非侵入式负荷动态识别方法的研究及工程应用[J]. 电气工程学报, 2023, 18(3): 307-314.

LIU Chunlei, PANG Pengfei, SHI Wenhe, KONG Linghao, HUANG Xun, QI Jun. Research and Engineering Application of Non-intrusive Load Dynamic Identification Method[J]. Journal of Electrical Engineering, 2023, 18(3): 307-314.

图/表 9

表1

负荷特征综合分析表"

特征类型	特征名称	特征提取	采样频率/Hz	监测窗口宽度/s	计算复杂度	负荷特征存储量
稳态特征	有功功率P	$P = 1 N ∑ n = 0 N v (n) i (n)$	≈10³	>工频周期	低	小
	无功功率Q	$Q = 1 N ∑ n = 0 N v (n + N 4) i (n)$
	功率因数	$φ = P P 2 + Q 2$
	相角	$φ = arccos P P 2 + Q 2$
	电压-电流(V-I)轨迹	分别以目标设备的电压、电流瞬时值为横、纵坐标绘制图像			中	大
	电流谐波	对电流波形进行FFT变换分析	10³~10⁵		中	中
暂态特征	暂态电流峰值I_peak	$I p e a k = max {i (n), n = 1, 2, 3, ⋯, C}$	10³~10⁵	10^-2~10² (取决于实际负荷类型)	低	小
	暂态电流有效值I_rms	$I r m s = ∑ n = 0 N i (n) 2 N$
	暂态电流均值I_mean	$I m e a n = ∑ n = 0 C i (n) C$
	暂态电流方差I_var	$I v a r = ∑ n = 0 C i (n) − I m e a n 2 C$
	暂态时间T	$T = C f$
	S变换特征	对电流波形进行S变换分析			高	中
时间特征	负荷运行时段	统计负荷运行的常用时间段	10^-3~10²	10²~10⁵ (取决于用电规律)	低	小
	负荷工作周期性	统计负荷运行的时间周期
	负荷运行时长	统计负荷运行的单次时长

表1

表2

图1

图2

图3

表3

图4

图5

表4

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不同负荷	ω(%)				T_f(%)
不同负荷	方法①	方法②	方法③	方法①		方法②	方法③
计算机厨房顶灯办公室灯显示器浴室灯餐厅顶灯后院灯综合	92 94 94 90 92 53 54 81	98 96 92 98 98 92 94 95	92 94 94 90 92 92 94 92	100 100 100 100 100 100 100 100		456 477 430 450 457 440 442 459	100 100 100 100 100 522 510 218

指标	识别方法
指标	①	②	③
识别正确率(%) 平均识别时长/s 1 h系统监测时延/s 2 h系统监测时延/s	67.5 11.4 11 11	93.5 36.8 1 942 3 201	91 15.4 11 11