Al-Attabi, Alaa Razzaq Khallawi (2021) Characterisation of dissolved decay products of transformer oil based on plastic optical fibre. Doctoral thesis, Universiti Teknikal Malaysia Melaka.
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Abstract
High-voltage transformers are essential for power systems, particularly in the transmission and distribution sectors of electrical energy. The reliability of this equipment typically depends on the condition of the insulation. Thus, considerable attention must be accorded to the development of diagnostic and monitoring systems for predicting the condition of transformer insulation. Transformer oil is widely used in high-voltage transformers as a cooling and insulation medium. Electrical and thermal stresses expose transformer oil to degradation, which may produce dissolved decay products (DDPs) that are partially adsorbed by the insulation paper and thus accelerate the degradation of oil and paper. The ducts of paper and solid insulation will be closed by DDPs, resulting in the increased overheating of winding and oil. Conventional test methods developed for the condition assessment of power transformers have certain limitations. To overcome such problems, fibre optic-based sensors for monitoring the condition of transformers have been developed. In this research, an evanescent field plastic optical fibre (POF) sensor was fabricated using chemical etching and polishing technique to detect DDPs in transformer oil. Three types of structures have been fabricated which are D-shaped, D-shaped U-bent and tapered POF sensors. The sensor performance was evaluated on the basis of different sensing lengths. The D-shape sensing lengths were 5, 10 and 20mm while for U-bent 5 and 10mm have been used. For tapered sensor, the sensing length was 15mm and the waist diameter was 0.7μm. Different measurements of transformer oil have been conducted namely moisture content, breakdown voltage test, refractive index (RI) and DDPs. Measurements were taken for two groups of oils that is artificially aged and on-site oil samples. The correlation between the measurements of transformer oil was based on two parameters that is correlation factor (R) and p-value. From the analysis of results, there is a correlation between the RI of transformer oil sample, DDPs and output power of the POF sensor. The performance of the sensor has been analysed based on the relation between the RI and output power of POF sensors by using statistical regression analysis. Different sensor parameters in terms of sensitivity, coefficient of determination (R2), standard error (SE), and linearity were discussed. The comparison between the D-shape, D-shaped U-bent and tapered POF sensors has been studied. In terms of sensitivity, the U-bent sensor recorded the highest, that is 47.86 μW/RIU and followed by D-shaped and tapered structure. The comparative study shows that there is no significant change of sensitivity between D-shaped and tapered POF sensor. The results also show that, when the relative amount of DDPs increased, attenuation in the output power of the POF sensor also increased as a result of increment in the RI of transformer oil. This led to reduction in the output power at the receiving end of POF sensor. The POF sensor is easily fabricated, low cost and can be used for real-time monitoring of high-voltage transformers. This work is important to improve the online monitoring of high-voltage transformers because it provides early information on the quality of transformer oils and thus increases the reliability of power system networks.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Electric insulators and insulation, Oils Electric transformers Plastic optical fibers, Dielectrics |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Library > Tesis > FKE |
| Depositing User: | F Haslinda Harun |
| Date Deposited: | 29 Sep 2022 12:47 |
| Last Modified: | 29 Sep 2022 12:47 |
| URI: | http://eprints.utem.edu.my/id/eprint/26041 |
| Statistic Details: | View Download Statistic |
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