An MIP-Based Novel Capacitive Sensor to Detect 2-FAL Concentration in Transformer Oil

Nezami, Md. Manzar; Wani, Shufali Ashraf; Khan, Shakeb A.; Khera, Neeraj; Sohail, Shiraz

doi:10.1109/jsen.2018.2864793

Cited by 30 publications

(3 citation statements)

References 22 publications

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“…2-Furfuraldehyde (2-FAL) and carbon oxides (CO 2 and CO) are the major constituents of the by-products. These byproducts can directly provide the extent of CSKP degradation with high degree of preciseness as these by-products are a direct function of degree of polymerisation (DP) [6][7][8][9][10][11][12]. The DP is found to be the most prominent structural parameter of CSKP and thereby used as its direct ageing indicator.…”

Section: Introductionmentioning

confidence: 99%

A novel artificial neural network approach for residual life estimation of paper insulation in oil‐immersed power transformers

Nezami,

Equbal,

Ansari

et al. 2023

IET Electric Power Appl

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Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation.

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Section: Introductionmentioning

confidence: 99%

A novel artificial neural network approach for residual life estimation of paper insulation in oil‐immersed power transformers

Nezami,

Equbal,

Ansari

et al. 2023

IET Electric Power Appl

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“…Dervos [7] designed a cylindrical sensor for permittivity characterizations of high-voltage transformer oils. Nezami [8] developed a capacitive sensor to sense 2-FAL concentration in the transformer oil by dip coating a molecularly imprinted polymer on electrodes. Shi [9] developed an inductive-capacitive sensor to detect debris in hydraulic oil in order to determine the working state of hydraulic systems.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Electrical Discharge Machining Oil Quality by Analyzing Variations in the Equivalent Relative Permittivity of the Capacitive Sensor

Chen

Chang

et al. 2020

Sensors

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In this study, a concentration monitoring system was successfully developed. A sensor was immersed in electrical discharge machining oil, and the capacitance of the sensor changed as a function of the impurity concentration. Thus, DC voltage variations were produced via a conversion circuit. Carbon black and iron particles with different concentrations were successfully characterized. The capacitance increments were positively correlated with the particle concentration. The linear fitting results based on the impurity concentration were used to express the degree of influence of particles with different weight percentage concentrations on the increase in the overall capacitance value. An equivalent medium theory model was then developed according to the electrical characteristics of the impurities to predict different particle volume percentages.

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“…The conventional method of detecting 2FAL in transformer oil is by using high performance liquid chromatography (HPLC) technique based on the International Electrotechnical Commission (IEC) 61198 [6] standard. Although HPLC method is able to accurately determine the concentration of 2FAL, this measurement technique requires tedious preparation procedures [7].…”

Section: Introductionmentioning

confidence: 99%

Detection of 2FAL Furanic Compound in Transformer Oil Using Optical Spectroscopy Method and Verification Using Morse Oscillation Theory

et al. 2020

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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.

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An MIP-Based Novel Capacitive Sensor to Detect 2-FAL Concentration in Transformer Oil

Cited by 30 publications

References 22 publications

A novel artificial neural network approach for residual life estimation of paper insulation in oil‐immersed power transformers

A novel artificial neural network approach for residual life estimation of paper insulation in oil‐immersed power transformers

Measurement of Electrical Discharge Machining Oil Quality by Analyzing Variations in the Equivalent Relative Permittivity of the Capacitive Sensor

Detection of 2FAL Furanic Compound in Transformer Oil Using Optical Spectroscopy Method and Verification Using Morse Oscillation Theory

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