期刊
ENERGIES
卷 14, 期 13, 页码 -出版社
MDPI
DOI: 10.3390/en14133842
关键词
power flow; distribution network; machine learning; slack node voltage; impedance estimation
资金
- Korea Agency for Infrastructure Technology Advancement (KAIA) - Ministry of Land, Infrastructure and Transport [21HSCTB157917-02]
Power flow analysis is an essential method in the planning and operation of the power grid. This paper proposes an alternative method for distribution systems based on available parameters at terminal nodes, and develops two machine learning schemes for high-voltage and low-voltage systems. The performance of the models is verified through numerical simulations and compared with another power flow scheme for distribution systems.
Power flow analysis is an inevitable methodology in the planning and operation of the power grid. It has been performed for the transmission system, however, along with the penetration of the distributed energy resources, the target has been expanded to the distribution system as well. However, it is not easy to apply the conventional method to the distribution system since the essential information for the power flow analysis, say the impedance and the topology, are not available for the distribution system. To this end, this paper proposes an alternative method based on practically available parameters at the terminal nodes without the precedent information. Since the available information is different between high-voltage and low-voltage systems, we develop two various machine learning schemes. Specifically, the high-voltage model incorporates the slack node voltage, which can be practically obtained at the substation, and yields a time-invariant model. On the other hand, the low voltage model utilizes the deviation of voltages at each node for the power changes, subsequently resulting in a time-varying model. The performance of the suggested models is also verified using numerical simulations. The results are analyzed and compared with another power flow scheme for the distribution system that the authors suggested beforehand.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据