期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 37, 期 4, 页码 2783-2795出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2021.3130301
关键词
Load modeling; Mathematical models; Load flow; Data models; Power systems; Reactive power; Voltage measurement; Data-driven; distribution power systems; linear power flow model; three-phase unbalancing; ZIP loads
资金
- National Natural Science Foundation of China [52007105]
- Young Elite Scientists Sponsorship Program of the CSEE [JLB-2020-170]
- Qilu Youth Scholar Program from Shandong University [TPWRS-00428-2021]
This paper proposes a data-driven-aided linear three-phase power flow model for distribution power systems, which shows relatively high accuracy by integrating data-driven techniques and remaining robust even when common assumptions are not valid.
Distribution power systems (DPSs) are generally unbalanced, and their loads may have notable static voltage characteristics (ZIP loads). Hence, although many papers have focused on linear single-phase power flow models, it is still necessary to study linear three-phase distribution power flow models. This paper proposes a data-driven-aided linear three-phase power flow model for DPSs. We first formulate how to amalgamate data-driven techniques into a linear power flow equation to establish our linear model. This amalgamation makes our linear model independent of the assumptions commonly used in the literature (e.g., nodal voltages are nearly 1.0 p.u.); therefore, our model is characterized by relatively high accuracy, even when the assumptions become invalid. We then demonstrate how to apply our model to DPSs with ZIP loads. We also show that with the Huber penalty function employed, the adverse impact of bad data on our model's accuracy is significantly reduced, rendering our model robust to poor data quality. Case studies demonstrate that our model is generally more accurate, with 2- to 100-fold smaller errors, than most existing linear models, and remains fairly accurate even under poor data conditions. Our model also contributes to a rapid solution to DPS analyses and optimization problems.
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