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DC Field | Value | Language |
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dc.contributor.author | Patel, Nitish Rajubhai | - |
dc.date.accessioned | 2019-09-13T08:21:45Z | - |
dc.date.available | 2019-09-13T08:21:45Z | - |
dc.date.issued | 2018-06-01 | - |
dc.identifier.uri | http://10.1.7.192:80/jspui/handle/123456789/8881 | - |
dc.description.abstract | This project proposes a powerful Sine Cosine Algorithm (SCA) to explain the Economic Load Dispatch (ELD) problem including equality and inequality constraints. The Economic Load Dispatch accomplishes the most reliable and nominal dispatching among the accessible thermal generators. The main aim of ELD is to satisfy the entire electric load at minimum cost. The SCA is a population based optimization technique which guides its search agents, that are randomly place in the search space, towards an optimal point using their fitness function and also keeps a track of the best solution achieved by each search agent. The Sine Cosine Algorithm is being used for the Economic Load Dispatch problem due to its high exploration and local optima avoidance technique com- pared to other individual based algorithms. This algorithm confirms that the promising areas of the search space are exploited to have a smooth transition from exploration to exploitation using adaptive range in the sine and cosine functions. | en_US |
dc.publisher | Institute of Technology | en_US |
dc.relation.ispartofseries | 16MEEE18; | - |
dc.subject | Electrical 2016 | en_US |
dc.subject | Project Report 2016 | en_US |
dc.subject | Electrical Project Report | en_US |
dc.subject | Project Report | en_US |
dc.subject | 16MEE | en_US |
dc.subject | 16MEEE | en_US |
dc.subject | 16MEEE18 | en_US |
dc.subject | EPS | en_US |
dc.subject | EPS 2016 | en_US |
dc.subject | EE (EPS) | en_US |
dc.subject | Electrical Power Systems | en_US |
dc.title | Economic Load Dispatch using Sine Cosine Algorithm | en_US |
dc.type | Dissertation | en_US |
Appears in Collections: | Dissertation, EE (EPS) |
Files in This Item:
File | Description | Size | Format | |
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16MEEE18.pdf | 16MEEE18 | 905.12 kB | Adobe PDF | ![]() View/Open |
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