Over the years, transformer oil has been used in majority of the power transformers to provide a reliable insulating system that is effective against dielectric stresses. Oxidation often occurred in transformer oil in the presence of oxygen and moisture which will affect the stability and insulating properties of the oil. The oxidation process cannot be eliminated but it can be delayed with the presence of inhibitor content. Even though inhibitor content can reduce the oxidation rate, the amount of inhibitor content still depletes over time. Thus, a monitoring system to detect the inhibitor content concentration is very crucial as it will be able to prolong the life span of the transformer. This paper focuses on the optical characterization of inhibitor content in transformer oil by utilizing the ultraviolet-visible (UV-Vis) spectroscopy technique. It was found that oil samples with inhibitor content produce multiple absorbance peaks in the range of 350 nm to 500 nm. A clear difference in peak absorbance near 450 nm indicates the difference in the inhibitor content concentrations. Based on the results of this work, a portable and low cost optical sensing device can potentially be developed for the detection of inhibitor content in transformer oil using UV-Vis spectroscopy.
Biological tissues have different optical windows that correspond to different absorption and transmittance. In this pa-per, we calibrated the dual-core fibre cantilever-based Lissajous scanning system and conducted multiwavelength imaging using visible and near-infrared light. Furthermore, the exact method to eliminate interlacing artifacts in the image reconstruction algorithm is demonstrated. The multiwavelength imaging of the biological sample was conducted. Multiwavelength imaging reveals remarkable features on the sample based on the visible and infrared spectrum.
Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes the utilization of single-wavelength spectroscopy with 405 nm laser diode using artificial neural network (ANN) to determine the color index of transformer oil. Two ANN models were developed using data collected from 50 oil samples with different optical pathlengths of 1 to 10 mm, and laser output powers of 1 to 15 mW. The first model classified the input into different color indices and another model correlated the input parameters through regression analysis to determine the color index. A hybrid ANN-fuzzy logic model was also developed to improve the color index prediction. The root-mean-squared error (RMSE) obtained from the prediction by ANN regressor and ANN classifier are 0.5602 and 0.6416, respectively. The hybrid ANN-fuzzy logic model improves the RMSE especially for optical pathlengths $<$ 5 mm, which is required for measuring samples with high color index. This proposed method reduces the dependency on complex optoelectronic hardware to obtain highly accurate results. Note to Practitioners —Unlike the conventional testing method for color index of transformer oil that requires human observation, the findings of this study enables the possibility of compact and smart portable device through the utilization of single wavelength spectroscopy with machine learning models. With no human involvement, more computational power with lesser hardware dependency, the maintenance cost and error can be reduced. This proposed method can potentially be applied to measure the color of other amber-colored liquid products such as olive oil, honey and others.
The color of transformer oil can be one of the first indicators determining the quality of the transformer oil and the condition of the power transformer. The current method of determining the color index (CI) of transformer oil utilizes a color comparator based on the American Society for Testing and Materials (ASTM) D1500 standard, which requires a human observer, leading to human error and a limited number of samples tested per day. This paper reports on the utilization of ultra violet-blue laser at 405- and 450-nm wavelengths to measure the CI of transformer oil. In total, 20 transformer oil samples with CI ranging from 0.5 to 7.5 were measured at optical pathlengths of 10 and 1 mm. A linear regression model was developed to determine the color index of the transformer oil. The equation was validated and verified by measuring the output power of a new batch of transformer oil samples. Data obtained from the measurements were able to quantify the CI accurately with root-mean-square errors (RMSEs) of 0.2229 for 405 nm and 0.4129 for 450 nm. This approach shows the commercialization potential of a low-cost portable device that can be used on-site for the monitoring of power transformers.
Optical fibre cantilever that is on Lissajous scanning systems are widely used for optical imaging. But, this type of system is vulnerable to frequency and phase shifts. We report the theory behind the shift in Lissajous scanning trajectories caused by dynamic discrepancies in the scanning system. The impact of phase mismatch between phase response of the scanning system and driving signal during the image reconstruction was demonstrated via simulation. As long as the scanning system's phase response is correctly monitored, new opportunities for enhanced Lissajous scanning systems can be explored.
The conventional method of detecting inhibitor content in transformer oil is by using fourier-transform infrared spectroscopy at the mid-infrared region. This wavelength of detection brings several technical challenges in fabricating the photodetector and light source. Therefore, as a fundamental research that can potentially lead to the development of an on-site measuring device, this study reports on the discovery of a new optical detection wavelength (959 nm) that can be used to measure the concentration of inhibitor in transformer oil. A total of 35 samples were manually prepared and measured using the Agilent Cary5000 Spectrophotometer from 950 nm - 970 nm with cuvettes of three different path lengths. The peak at 959 nm shows the second overtone of O-H stretch in inhibitor and has a strong correlation with Morse oscillation model. Two mathematical models were established for fixed and variable optical path lengths based on the correlation between the concentrations of inhibitor and the optical absorbance spectra of the samples. The mathematical models were verified with average errors of 4.00% and 5.49% for single and variable path length models, respectively. Finally, the importance of a variable path length measurement and the application of the newly discovered wavelength are critically analyzed and presented. The combination of the variable path length model and the detection of inhibitor at Silicon-detectable wavelengths (<; 1100 nm) enables the utilization of Silicon technology in the development of an inhibitor monitoring device which can be used in various industries.
The condition of a power transformer can be predicted based on the contents of the transformer oil. One of the most common parameters that are observed in the transformer oil is 2-Furaldehyde, a compound that is produced due to the aging of Kraft paper. Although the optical characterization of 2-Furaldehyde has been widely reported, the fundamental theories of the observation of the optical absorbance peaks have not been elaborated. This study investigates the optical characteristics of 2-Furaldehyde in the near infrared region. Ten samples with different concentrations of 2-Furaldehyde were prepared and verified using the conventional method. The samples were then characterized using optical spectroscopy method with 50 mm path length cuvettes. Three peaks were observed at 1610 nm - 1640 nm, 1100 nm - 1115 nm and 860 nm - 890 nm wavebands and were correlated to the overtones of the aromatic C-H stretch in 2-Furaldehyde. The appearance of these overtones corresponding to the Morse Oscillation Theory was discussed. This fundamental knowledge is significant in developing a portable optical device that enables the detection of 2-Furaldehyde on site.
Periodic preventive maintenance of power transformer should be conducted for its health monitoring and early fault detection. Transformer oil is a vital element where its contents and properties need to be monitored during the service life of a power transformer. This paper presents an optical spectroscopy measurement from 200 nm to 3300 nm to characterize the transformer oil, which were sampled from the main tanks and 'on-load tap changer' of power transformers. The correlation of the optical characteristics in the range of 2120 nm to 2220 nm to the Dissolved Gas Analysis results and Duval Triangle interpretation demonstrates that the low energy electrical discharges, high energy electrical discharges as well as the thermal faults rated at temperatures above 700°C in power transformers can be accurately predicted. For faster and accurate analysis of fault prediction, a data mining analytics tool was constructed using Rapid Miner server to analyze and verify the predictions for a total of 108 oil samples. For the optimization, continuous iterations were performed to determine the best absorbance-wavelength combination that can improve the accuracy of the prediction. The performance of the optical spectroscopy technique integrated with data analytic tool was analyzed and it was found that the technique contributes to a high accuracy of 98.1% in fault prediction. It is a cost-effective and quicker complementing approach to carry out pre-screening of the transformer oil in order to know the condition of the power transformers based on the transformer oil's optical characteristics.
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