Please use this identifier to cite or link to this item:
http://10.1.7.192:80/jspui/handle/123456789/10518
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | TIWARI, ADITYA | - |
dc.date.accessioned | 2022-01-24T05:45:46Z | - |
dc.date.available | 2022-01-24T05:45:46Z | - |
dc.date.issued | 2021-06-01 | - |
dc.identifier.uri | http://10.1.7.192:80/jspui/handle/123456789/10518 | - |
dc.description.abstract | Economic load dispatch is the major problem of power system operation. ELD is one of the main optimization problems that give rise to the economic status of the Power System. The Conventional optimization methods are inefficient in solving complex ELD problems. PSO algorithm is selected over Conventional methods because of its high efficiency and faster convergence rate. ELD problem is solved on 3 test systems 6 unit, 10 unit and 15 unit system. Different PSO algorithms are used to minimize the total operating cost. Lambda iteration method, IPSO algorithm and CPSO algorithm are performed on 6 unit system. Standard PSO, CIPSO algorithm and TVACPSO algorithm are performed on ten unit and fifteen unit system to obtain better result in PSO. In this report, the ELD is considered without the losses for simplicity. The total cost and convergence characteristics are obtained for different PSO algorithms. The Particle swarm optimization (PSO) technique is the most effective approach used for the ELD problem. | en_US |
dc.publisher | Institute of Technology | en_US |
dc.relation.ispartofseries | 19MEEE01; | - |
dc.subject | Electrical 2019 | en_US |
dc.subject | Project Report 2019 | en_US |
dc.subject | Electrical Project Report | en_US |
dc.subject | Project Report | en_US |
dc.subject | 19MEE | en_US |
dc.subject | 19MEEE | en_US |
dc.subject | 19MEEE01 | en_US |
dc.subject | EPS | en_US |
dc.subject | EPS 2019 | en_US |
dc.subject | EE (EPS) | en_US |
dc.subject | Electrical Power Systems | en_US |
dc.title | Solving Economic Load Dispatch Problem using Particle Swarm Optimization Technique | en_US |
dc.type | Dissertation | en_US |
Appears in Collections: | Dissertation, EE (EPS) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
19MEEE01.pdf | 19MEEE01 | 1.63 MB | Adobe PDF | ![]() View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.