In recent years, harmonic current levels have increased in residential distribution networks due to the growing penetration of nonlinear loads at customer premises. To properly model the harmonic current injection of residences, probabilistic models that account for the stochastic behavior of domestic loads are needed. As a part of the study reported in this paper, a field measurement campaign was performed in 24 dwellings during a one-week period with a one-minute resolution. Harmonic current magnitudes and phase angles of odd harmonic orders up to the 25th as well as the active and reactive power demanded were recorded. By applying two different unsupervised learning techniques (i.e., nonparametric density estimation and Gaussian mixture models) to these measurements, two probabilistic models of harmonic injection of individual residential sites have been derived. Statistical probability distributions have been determined for the magnitude and phase of each harmonic order segmented in different power demand intervals. The developed models are validated on a test feeder by comparing harmonic voltages caused by the injection of the synthetically generated currents with those obtained from measurements. Both field measurement results and the detailed data defining the probabilistic harmonic current models are made public in an Open Science database repository.

中文翻译：

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基于现场测量的住宅谐波注入模型

近年来，由于客户端非线性负载的渗透率不断增加，住宅配电网络中的谐波电流水平有所增加。为了正确模拟住宅的谐波电流注入，需要考虑家庭负荷随机行为的概率模型。作为本文报告的研究的一部分，在一周内以一分钟的分辨率在 24 个住宅中进行了现场测量活动。记录高达 25 次的奇次谐波次数的谐波电流幅值和相角以及所需的有功和无功功率。通过将两种不同的无监督学习技术（即非参数密度估计和高斯混合模型）应用于这些测量，已经推导出了两个单个住宅站点谐波注入的概率模型。已经确定了在不同电力需求间隔中分段的每个谐波次数的幅度和相位的统计概率分布。通过将合成产生的电流注入引起的谐波电压与从测量中获得的谐波电压进行比较，开发的模型在测试馈线上得到验证。现场测量结果和定义概率谐波电流模型的详细数据都在开放科学数据库存储库中公开。通过将合成产生的电流注入引起的谐波电压与从测量中获得的谐波电压进行比较，开发的模型在测试馈线上得到验证。现场测量结果和定义概率谐波电流模型的详细数据都在开放科学数据库存储库中公开。通过将合成产生的电流注入引起的谐波电压与从测量中获得的谐波电压进行比较，开发的模型在测试馈线上得到验证。现场测量结果和定义概率谐波电流模型的详细数据都在开放科学数据库存储库中公开。