Efficient determination of optimal radial power system structure using hopfield neural network with constrained noise

Yasuhiro Hayashi, S. Iwamoto, S. Furuya, C. C. Liu

研究成果: Article

16 引用 (Scopus)

抜粋

When a radial power system has a number of connected feeders, the total number of possible system structures can be very large. In order to determine the optimal radial power system structure rapidly, we propose a constrained noise approach, which can avoid local minima, with the Hopfield neural network model. For checking the validity of the proposed approach we compare the proposed method with a conventional branch-and-bound method which is popular in the field of mathematical programming. Simulations are carried out for two actual subsystems of Tokyo Electric Power Co.(TEPCO). Furthermore, because engineering knowledge is necessary to operate or plan the radial power system securely, we combine the proposed Hopfield model with engineering knowledge in order to obtain a more practical system structure considering cases of fault occurrence at each substation. The combined technique is demonstrated with one of the TEPCO subsystems.

元の言語English
ページ(範囲)1529-1535
ページ数7
ジャーナルIEEE Transactions on Power Delivery
11
発行部数3
DOI
出版物ステータスPublished - 1996
外部発表Yes

    フィンガープリント

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

これを引用