Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical Engineering | en_US |
| dc.creator | Wang, Jiashu | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/6457 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Artificial neural network based economic load dispatch optimization methodologies | en_US |
| dcterms.abstract | The economic load dispatch (ELD) problem is the core functional module of power transaction schemes under the competitive market environment. It is an on-line system for optimizing the load dispatch among the generators so as to minimize the total cost of power supply while the total power demand and the transmission losses at any instant is met by the total power generation. Many of the classical methods to solve the ELD problem suffer from natural complexity and slow convergence. This project deeply studies on the artificial neural network (ANN) algorithm combined with classical methods and challenges the determination of the optimum dispatch solution for practical applications. The status of both conventional and newly developed methods for solving the problem of ELD and the prerequisites of optimizing ELD based on neural networks, which are highly simplified models of the human nervous systems are also studied in this project. This project designs a back propagation neural network (BPNN) model and a radial basis function neural network (RBFNN) model cooperating with optimal power flow (OPF); these models are applied to solve the problem. The input-output OPF training patterns for these two neural networks are generated from the OPF function embedded in Matpower Toolbox. MATLAB programming, and a fast, accurate, efficient and reliable neural network framework is developed to train and simulate the economic load dispatch process. The effectiveness of the developed methodologies is illustrated by comparing the results with those obtained from the Matpower OPF method. In practice, ELD should solve the problem for any feasible changes at the load buses. In certain conditions, ANN approaches are not regarded as so effective. In order to expand the cognition of the ANN based ELD optimization, these conditions are taken into consideration and verified in the last chapter. | en_US |
| dcterms.extent | v, 99 p. : ill. ; 30 cm. | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2010 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Electric power systems. | en_US |
| dcterms.LCSH | Electric power system stability. | en_US |
| dcterms.LCSH | Neural networks (Computer science) | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b24630548.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.41 MB | Adobe PDF | View/Open |
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