Adaptive Power Flow Prediction Based on Machine Learning

Jingyeong Park, Daisuke Kodaira, Kofi Afrifa Agyeman, Taeyoung Jyung and Sekyung Han
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Jingyeong Park: School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
Daisuke Kodaira: Department of Electrical Engineering, Tokyo University of Science, Tokyo 162-8601, Japan
Kofi Afrifa Agyeman: School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
Taeyoung Jyung: Korea Electric Power Corporation Engineering & Construction (KEPCO E&C), Gimcheon 39660, Korea
Sekyung Han: School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea

Energies, 2021, vol. 14, issue 13, 1-18

Abstract: 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.

Keywords: power flow; distribution network; machine learning; slack node voltage; impedance estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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