Data-Driven Harmonic Modeling for Distribution Area in Distribution Networks with Distributed Harmonic Loads

doi:10.12204/j.issn.1000-7229.2021.08.006

Abstract

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.

Key words: data-driven, decentralized harmonic source, Norton model, non-intrusive load monitoring (NILM), Markov chain

CLC Number:

TM714

ZHANG Mengchen, LIN Lijuan, MENG Jing, NIU Yiguo, WANG Jun, SHI Leilei. Data-Driven Harmonic Modeling for Distribution Area in Distribution Networks with Distributed Harmonic Loads[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(8): 46-54.

Figures/Tables 13

Fig.1 Norton equivalent harmonic model of power electronic equipment

Fig.2 Schematic diagram of NILM framework

Fig.3 Schematic diagram of NILM decomposition results of residential users

Fig.4 Harmonic source and system-side equivalent harmonic circuit

Fig.5 Flowchart of evaluation method for group harmonic emission level in station area

Fig.6 Changes of the total number of inverter air-conditioners during 08:00-14:00

Table 1 Norton model of the 3th harmonic in inverter air-conditioners

Fig.7 The change of total harmonic current of inverter air-conditioners during the period

Fig.8 Monte Carlo method for forecasting total harmonic current of inverter air-conditioners

Table 2 Comparison of the simulation results with the mean, standard deviation of measured results

Fig.9 Dynamic emission curve of harmonic current

Table 3 Comparison of the simulation results with the mean, standard deviation of measured results

Fig.A1 Change curve of the number of different types of inverter air-conditioners during the period

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