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DC Field | Value | Language |
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dc.contributor.author | F. Benhamida, A. Bendaoud , K. Medles A. Tilmatine | - |
dc.date.accessioned | 2013-12-22T10:27:12Z | - |
dc.date.available | 2013-12-22T10:27:12Z | - |
dc.date.issued | 2013-12-22 | - |
dc.identifier.issn | MO | - |
dc.identifier.uri | http://hdl.handle.net/123456789/3511 | - |
dc.description | The International Conference on Electronics & Oil ICEO11 March 1-2 2011 | en_US |
dc.description.abstract | This paper proposes a solution to the prohibited zone dynamic economic dispatch (DED) problem in power system using a hybrid artificial neural network (HANN), which is a continuous model named Hopfield model. The constrained DED must not only satisfy the system load demand and the spinning reserve capacity, but some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone, are also considered in practical generator operation. The feasibility of the proposed HANN of Hopfield model method is demonstrated using two power systems, and it is compared with the other methods in terms of solution quality and computation efficiency. The experimental results showed that the proposed HANN method was indeed capable of obtaining higher quality solutions efficiently in constrained DED problems. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartofseries | 2011; | - |
dc.subject | dynamic economic dispatch | en_US |
dc.subject | Hopfield Neural Network | en_US |
dc.subject | dichotomy method | en_US |
dc.subject | prohibited operating zone | en_US |
dc.title | Prohibited Zone Dynamic Economic Dispatch Solution Using a Hybrid Artificial Neural Network | en_US |
dc.type | Article | en_US |
Appears in Collections: | 3. Faculté des sciences appliquées |
Files in This Item:
File | Description | Size | Format | |
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F. Benhamida.pdf | 1,62 MB | Adobe PDF | View/Open |
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