RESUMO
As the focus tilts toward online detection methodologies for transformer oil aging, bypassing challenges associated with traditional offline methods, such as sample contamination and misinterpretation, fiber optic sensors are gaining traction due to their compact nature, cost-effectiveness, and resilience to electromagnetic disturbances that are typical in high-voltage environments. This study delves into the sensitivity analysis of intensity-modulated plastic optical fiber sensors. The investigation encompasses key determinants such as the influence of optical source wavelengths, noise response dynamics, ramifications of varying sensing lengths, and repeatability assessments. Our findings highlight that elongating sensing length detrimentally affects both linearity response and repeatability, largely attributed to a diminished resistance to noise. Additionally, the choice of the optical source wavelength proved to be a critical variable in assessing sensor sensitivity.
RESUMO
Transformers play an essential role in power networks, ensuring that generated power gets to consumers at the safest voltage level. However, they are prone to insulation failure from ageing, which has fatal and economic consequences if left undetected or unattended. Traditional detection methods are based on scheduled maintenance practices that often involve taking samples from in situ transformers and analysing them in laboratories using several techniques. This conventional method exposes the engineer performing the test to hazards, requires specialised training, and does not guarantee reliable results because samples can be contaminated during collection and transportation. This paper reviews the transformer oil types and some traditional ageing detection methods, including breakdown voltage (BDV), spectroscopy, dissolved gas analysis, total acid number, interfacial tension, and corresponding regulating standards. In addition, a review of sensors, technologies to improve the reliability of online ageing detection, and related online transformer ageing systems is covered in this work. A non-destructive online ageing detection method for in situ transformer oil is a better alternative to the traditional offline detection method. Moreover, when combined with the Internet of Things (IoT) and artificial intelligence, a prescriptive maintenance solution emerges, offering more advantages and robustness than offline preventive maintenance approaches.
