| Author: | Chan, Ka-ip |
| Title: | Transmission network planning using genetic algorithms |
| Degree: | M.Sc. |
| Year: | 1997 |
| Subject: | Electric networks -- Data processing Electric power transmission -- Planning Electric power systems -- Data processing Genetic algorithms Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies |
| Pages: | vii, 84 leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | The dissertation describes the development and application of genetic algorithm to the optimisation problem of transmission network expansion in an electric power system. The system parameters and the corresponding matrices can be built with the information of forecast loading conditions in substations, expandable paths, costs of building circuits, etc. All expandable paths are then coded as genes to form a chromosome. After random generation of the initial population, all chromosomes enter their game of life in the world of genetic algorithm. Under a pure probabilistic condition, any gene within any chromosome may undergo change in property or characteristic value to allow a certain degree of variation. The fitness of each member are calculated and compared according to not only the cost of installation but also the load flow condition. Then parent members amongst the population are selected with a probability proportional to their relative fitness values. Determined by the probability and mode of reproduction, the two chosen parents may undergo combination process to form two new members for the next generation. The fitness values of new born members are then calculated and those with higher fitness values will replace the old ones with lower fitness values to form the population of next generation. The life cycle will then be repeated a pre-defined number of times. In the final round of race, all the members with overloading are rejected and only the cost of installation will be considered and compared. Thus the expansion alternatives with relatively low cost and without overloading can be listed out for practical consideration. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b14201835.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.26 MB | Adobe PDF | View/Open |
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