A majority of utilities conduct maintenance of transmission line components based on the results of routine visual inspection. The inspection is normally done by inspectors who detect defects by visually checking transmission line components either from the air (in helicopters), from the ground (by using high-powered binoculars) or from the top of the structure (by climbing the structure).
The main problems with visual inspection of transmission lines are that the determination of the defects varies depending on the inspectors' knowledge and experience and that the defects are often reported qualitatively using vague and linguistic terms such as "medium crack", "heavy rust", "small deflection". As a result of these drawbacks, there is a large variance and inconsistency in defect reporting (which, in time, makes it difficult for the utility to monitor the condition of the components) leading to ineffective or wrong maintenance decisions. The use of inspection guides has not been able to fully address these uncertainties.
This thesis reports on the application of a visual inspection methodology that is aimed at addressing the above-mentioned problems. A knowledge-based Fuzzy Inference System (FIS) is designed using Matlab's Fuzzy Logic Toolbox as part of the methodology and its application is demonstrated on utility visual inspection practice of porcelain cap and pin insulators. The FIS consists of expert-specified input membership functions (representing various insulator defect levels), output membership functions (indicating the overall conditions of the insulator) and IF-THEN rules. Consistency in the inspection results is achieved because the condition of the insulator is inferred using the same knowledge-base in the FIS rather than by individual inspectors. The output of the FIS is also used in a mathematical model that is developed to suggest appropriate component replacement date.
It is hoped that the methodology that is introduced in this research will help utilities achieve better maintenance management of transmission line assets.
| Identifer | oai:union.ndltd.org:ADTP/265044 |
| Date | January 2004 |
| Creators | Mohd Noor, Mohd Junaizee |
| Publisher | Queensland University of Technology |
| Source Sets | Australiasian Digital Theses Program |
| Detected Language | English |
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