【摘要】 针对高密度、分散化、全网化电力电子非线性设备导致谐波污染难以有效估计问题,提出一种数据驱动的配电台区谐波污染源群体谐波排放水平建模方法。考虑多种谐波源模型特点和适用性,选取谐波Norton等效模型对谐波源负荷设备进行建模,形成设备典型谐波排放表征。利用非侵入式负荷监测(non-intrusive load monitoring, NILM)技术分解用户用电数据,得到设备各时刻启停状态,进而得到各时刻设备总开启数量。通过马尔科夫链(Markov chain, MC)模拟用电设备开启数量在时序上动态变化,建立用户用电时序特性模型,并将谐波源等效模型代入时序特性模型得到群体谐波污染排放。将仿真结果与蒙特卡洛模拟结果、实测数据进行对比,结果表明所提方法建模过程更为高效,可以有效解决大量分散谐波源群体谐波估计问题。更多还原

【Abstract】 As high-density, decentralized and network-wide power electronic nonlinear devices make it difficult to effectively estimate the harmonic pollution, a data-driven method for modeling the harmonic emission level of decentralized harmonic source group is proposed. Firstly, by comprehensively considering the characteristics and applicability of multiple harmonic source models, and selecting the Norton equivalent harmonic model to present harmonic source load device, a typical harmonic emission characterization is formed. Then, non-intrusive load monitoring(NILM) technology is introduced to decompose user’s electricity consumption data to obtain status of the devices, and then obtain the total number of running devices at each time. Finally, the Markov Chain(MC) is used to simulate the dynamic changes of the number of running devices in the time sequence, and the time-series characteristic model of user’s electricity consumption is established. The time-series characteristic model is combined with the harmonic source model to obtain the collective harmonic emission model. Compared with Monte Carlo simulation results and measured data, the proposed method has a more efficient modeling process and effectively solves the problem of group harmonic estimation with a large number of dispersed harmonic sources.更多还原