Efficient determination of optimal radial power system structure using Hopfield neural network with constrained noise
- Ibaraki Univ., Hitachi (Japan). Dept. of Systems Engineering
- Waseda Univ., Tokyo (Japan). Dept. of Electrical Engineering
- Tokyo Electric Power Co. (Japan)
- Univ. of Washington, Seattle, WA (United States). Dept. of Electrical Engineering
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, the authors propose a constrained noise approach, which can avoid local minima, with the Hopfield neural network model. For checking the validity of the proposed approach the authors 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, they 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.
- OSTI ID:
- 372243
- Report Number(s):
- CONF-950727--
- Journal Information:
- IEEE Transactions on Power Delivery, Journal Name: IEEE Transactions on Power Delivery Journal Issue: 3 Vol. 11; ISSN 0885-8977; ISSN ITPDE5
- Country of Publication:
- United States
- Language:
- English
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