An online updated linear power flow model based on regression learning

Autor: Molin An, Tianguang Lu, Xueshan Han
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Generation, Transmission & Distribution, Vol 18, Iss 10, Pp 2006-2019 (2024)
Druh dokumentu: article
ISSN: 1751-8695
1751-8687
DOI: 10.1049/gtd2.13170
Popis: Abstract The linear power flow (LPF) model is widely used in the optimization, operation, and control of distribution networks. These applications require the LPF model to be accurate, fast, and simple in order to simplify calculations as well as to efficiently perform operations and scheduling. In addition, it is difficult to realize the online update of parameters in the existing LPF models. The model retraining brings serious data burden and inefficiency. To serve these applications and comply with requirements, a brand new LPF model is proposed in this paper. A quadratic power flow model is trained by regression learning first, and then the proposed LPF model is derived by Taylor expansion. After only one initial regression learning, the proposed LPF model no longer needs retraining when updated. The refreshed parameter is simply updated online according to the real‐time measurement data, which improves the generalization ability. In conclusion, the proposed LPF model is accurate, generalizable, and greatly minimizes the data consumption and running time. Performance analysis verifies these superiorities.
Databáze: Directory of Open Access Journals
Externí odkaz: