Real power transfer capability calculations using multi-layer feed-forward neural networks
| Autor: | A.D. Patton, X. Luo, Chanan Singh |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
Generator (circuit theory)
Artificial neural network Computer science Reliability (computer networking) Line (geometry) Energy Engineering and Power Technology Quickprop Maximum power transfer theorem Feedforward neural network Control engineering Electrical and Electronic Engineering Network topology |
| Zdroj: | IEEE Transactions on Power Systems. 15:903-908 |
| ISSN: | 0885-8950 |
| DOI: | 10.1109/59.867192 |
| Popis: | This paper proposes a neural network solution methodology for the problem of real power transfer capability calculations. Based on the optimal power flow formulation of the problem, the inputs, for the neural network are generator status, line status and load status and the output is the transfer capability. The Quickprop algorithm is used in the paper to train the neural network. A case study of the IEEE 30-bus system is presented demonstrating the feasibility of this approach. The new method will be useful for reliability assessment in the new utility environment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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