Reactive power convex optimization of active distribution network based on Improved GreyWolf Optimizer

• Autorzy: Li Yuancheng , Yang Rongyan , Zhao Xiaoyu

Identyfikatory

DOI

10.24425/aee.2020.131762

• Języki publikacji: EN

Abstrakty

EN

The smart grid concept is predicated upon the pervasive With the construction and development of distribution automation, distributed power supply needs to be comprehensively considered in reactive power optimization as a supplement to reactive power. The traditional reactive power optimization of a distribution network cannot meet the requirements of an active distribution network (ADN), so the Improved Grey Wolf Optimizer (IGWO) is proposed to solve the reactive power optimization problem of the ADN, which can improve the convergence speed of the conventional GWO by changing the level of exploration and development. In addition, a weighted distance strategy is employed in the proposed IGWO to overcome the shortcomings of the conventional GWO. Aiming at the problem that reactive power optimization of an ADN is non-linear and non-convex optimization, a convex model of reactive power optimization of the ADN is proposed, and tested on IEEE33 nodes and IEEE69 nodes, which verifies the effectiveness of the proposed model. Finally, the experimental results verify that the proposed IGWO runs faster and converges more accurately than the GWO.

Słowa kluczowe

EN

active distribution network (ADN); Improved Grey Wolf Optimizer (IGWO); reactive power optimization; second-order cone relaxed convex model

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2020
• Tom: Vol. 69, nr 1
• Strony: 117--131
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wz.

Twórcy

autor

Li Yuancheng

• School of Control and Computer Engineering, North China Electric Power University Beijing, China

autor

Yang Rongyan

• School of Control and Computer Engineering, North China Electric Power University Beijing, China

autor

Zhao Xiaoyu

• School of Control and Computer Engineering, North China Electric Power University Beijing, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).