In the time of rapid growth, there is an increase of demand for a reliable and stable power supply. Due to this, utility companies are forced to operate their power system nearer to its maximum capabilities since system expansion may be a costly option. As a result, the power system will be at risk to voltage collapse. Voltage collapse phenomenon is known to be complex and localised in nature but with a widespread effect. The ultimate effect of voltage collapse would be total system collapse which would incur high losses to utility companies. This thesis discusses the voltage collapse phenomenon, its causes, effects and its analytical tools. Looking into its analytical tools, it is observed that it relies upon system equations and models. Published results from these techniques are accurate but may require long computation time for a big and complex system. As a possible solution, this thesis looks into combining machine learning techniques with fuzzy logic in creating a fuzzy decision tree (FDT) tool for voltage collapse analysis. The algorithm utilises static power flow solution as data sets in partitioning the power system into strong and weak areas. From several test results and algorithm development, this research concludes with a possible voltage collapse analytical tool using a hybrid FDT approach based upon multiple attribute partitioning. This thesis concludes with discussions on test results highlighting the FDT performance and ends with a discussion on possible future development on the FDT in creating a more complete tool for voltage collapse analysis.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:248817 |
| Date | January 2002 |
| Creators | Abidin, Haji Izham Haji Zainal |
| Publisher | University of Strathclyde |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=20372 |
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