Classification of thermal ageing impact of ester fluid‐impregnated pressboard material adopting LIBS

Amalanathan, A. J.; Vasa, Nilesh J.; Harid, Noureddine; Griffiths, Huw; Sarathi, R.

doi:10.1049/hve2.12092

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…The laser-induced breakdown spectroscopy (LIBS) has been adopted in the present work for identifying thermally aged pressboard samples by coupling the spectral elements with multivariate analysis. It has been concluded from our previous studies that increasing the delay period diminishes the strength of the elemental peaks and the pulsed laser's energy [23]. In the present work, the laser and the spectrometer were synchronized with the help of a control device in order to capture the emission of plasma generating the atomic spectra at the time of ablation.…”

Section: Laser Induced Breakdown Spectroscopymentioning

confidence: 98%

“…The laser induced breakdown spectroscopy (LIBS) instrument consists of a Q switched Nd 3+ : YAG laser (LAB15010S2K, QuantaRay LAB series, Spectra Physics) to generate a 10 ns pulsed beam with a 10 Hz repetition rate [23]. A lens, with a focal length of 25 cm, was used to focus the laser on the pressboard surface, and once the laser's energy exceeded the material's threshold fluence, the optically induced plasma was ignited.…”

Section: Laser Induced Breakdown Spectroscopymentioning

confidence: 99%

“…Furthermore, to identify and classify the aged pressboard specimens due to DBDS and Ag2S diffusion from the unaged ones, the principal component analysis (PCA) of the LIBS emission spectra was adopted. Compared to our previous studies where the ageing conditions in the pressboard were classified based on the emission spectra throughout the wavelength range of 200-800 nm [23], the present work considers only the prominent peaks of Cu I, C II, S II and Ag I as input to PCA algorithm. Totally, 200 LIBS spectral data were given as input to the model out of which 50 spectra corresponds to each specimen.…”

Section: Laser Induced Breakdown Spectroscopymentioning

confidence: 99%

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Impact of DBDS and Silver Sulfide on the Performance of Thermally Aged Mineral oil Impregnated Pressboard Material

et al. 2022

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The corrosive sulfur compounds present in the mineral oil causes the formation of dibenzyl disulfide (DBDS) complex and it can also erode the on-load tap changing selector contacts leading to generation of silver sulfide (AgS2).The present work reports the effect of DBDS and Ag2S concentrations in mineral oil on thermally aged pressboard material. The results demonstrate that surface discharge inception voltage (SDIV) is higher for the aged pressboard specimen. The higher concentrations of DBDS showed a reduction of more than 50% in the magnitude of SDIV compared to its effect with the addition of Ag2S. The surface potential analysis indicates a higher initial potential for the aged specimens with an increased half lifetime. In addition, only deep traps observed on the aged specimens with a slight right shift in its trap energy level. The mechanical strength of aged pressboard specimens with DBDS and Ag2S is understood using tensile testing and simultaneous analysis is captured using Digital Image Correlation (DIC) technique to understand its strain percentage at different areas on the pressboard material during its elongation. The laser induced breakdown spectroscopy (LIBS) analysis is performed to identify the elements responsible for thermal ageing such as carbon, copper and sulfur on the surface of pressboard material. The plasma temperature calculated from sulfur peaks is higher for DBDS aged specimen compared to AgS2 and a linear correlation is observed on the LIBS intensity with ageing duration. Further, the Principal Component Analysis (PCA) is used for its classification on the degradation of pressboard due to the diffusion of both copper and silver sulfide from the mineral oil. The two principal components (PC1 and PC2) provided a higher variance of 99.8% with a clear classification observed between the unaged and aged pressboard specimens due to the addition of both DBDS and Ag2S in mineral oil.

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Section: Laser Induced Breakdown Spectroscopymentioning

confidence: 98%

Section: Laser Induced Breakdown Spectroscopymentioning

confidence: 99%

Section: Laser Induced Breakdown Spectroscopymentioning

confidence: 99%

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Impact of DBDS and Silver Sulfide on the Performance of Thermally Aged Mineral oil Impregnated Pressboard Material

et al. 2022

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“…Amlanathan and his team performed LIBS analysis on heat-aged ester-impregnated paperboard, verifying the degradation of the paperboard material. By combining PCA and neural network analysis, they accurately distinguished between aged and unaged paperboard samples [24]. Yuan introduced the enhancing effect of spark discharge on the detection of iron particles in transformer oil using LIBS.…”

Section: Introductionmentioning

confidence: 99%

Laser-Induced Breakdown Spectroscopy Analysis of Sheet Molding Compound Materials

Shen,

Jian,

Zhen

et al. 2024

Energies

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Sheet Molding Compound (SMC) materials are extensively utilized as high-voltage insulation materials in electrical equipment. SMC materials are prone to aging after long-term operation. Conducting non-destructive testing to assess their electrical and physicochemical properties is crucial for the safe operation of electrical equipment. This study identifies the optimal equipment parameters for testing SMC materials using Laser-Induced Breakdown Spectroscopy (LIBS) technology through experimental investigation and also explores the ablation characteristics of SMC under various laser parameters. The results indicate a significant positive correlation between the ablation depth and laser pulse number, while there is no correlation with single laser pulse energy. However, the ablation area demonstrates a strong positive correlation with both single laser pulse energy and laser pulse number. Additionally, LIBS spectral analysis provides elemental results comparable to Energy Dispersive Spectroscopy (EDS), facilitating the examination of variations in Na, Ti, Fe, Mg, Ca, and C elemental contents with depth. Moreover, an enhanced iterative Boltzmann plot method is suggested for calculating the plasma temperature using 21 Fe I spectral lines and the electron density using the Fe II 422.608 nm line. The variations of these plasma parameters with laser pulse number are documented, and the results show consistent trends, confirming that the laser-induced SMC plasma adheres to local thermodynamic equilibrium.

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Clustering assisted artificial neural network for handling noisy big data: An application for estimation of parameter in combined mode conduction and radiation heat transfer

2022

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A pattern net assisted mapping artificial neural network (PAMANN) model for estimation of parameters in problem with large data (1300 × 121 matrix size) is reported. A pattern net‐based multilayer perceptron neural network (MLPNN) model for clustering the data, followed by mapping MLPNN model for mapping the target with the input, is developed as PAMANN model. A heat transfer problem with combined mode conduction and radiation in porous medium is solved numerically, and is called direct model. In the inverse model, a PAMANN model is developed by using data generated through the direct model. The PAMANN model is able to estimate two parameters (extinction coefficient β and convective coupling P2) after taking temperature profile as input. The model is tested for different number of neurons in hidden layer, and different levels of noise in input data. Twelve different algorithms are explored in training of mapping MLPNN, and compared for performance. Levenberg–Marquardt algorithm is found to estimate the parameters with high accuracy, but took high CPU time. Bayesian regularization is found to consume very high CPU time with moderate accuracy in estimation of parameters. Variations in hidden layer neuron number and noise in input data, were done to analyze the performance of mapping MLPNN with different training algorithms. Algorithms O‐Step Secant, conjugate gradient with Polak‐Ribiére updates, and conjugate gradient with Fletcher‐Reeves updates are able to handle all variations of noise and number of neurons in hidden layer, with good accuracy of estimation and low CPU time consumption. Under high computational resource LM algorithm can be used for all cases. Up to 0.99132 value of regression coefficient is obtained in mapping MLPNN model with 15 hidden neurons, indicating the high accuracy of the model. With the help of PAMANN model, highly accurate (absolute error 1.78%) estimation of parameters is obtained. The model can handle upto 1% noise in input data, while giving accurate results.

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Classification of thermal ageing impact of ester fluid‐impregnated pressboard material adopting LIBS

Cited by 6 publications

References 31 publications

Impact of DBDS and Silver Sulfide on the Performance of Thermally Aged Mineral oil Impregnated Pressboard Material

Impact of DBDS and Silver Sulfide on the Performance of Thermally Aged Mineral oil Impregnated Pressboard Material

Laser-Induced Breakdown Spectroscopy Analysis of Sheet Molding Compound Materials

Clustering assisted artificial neural network for handling noisy big data: An application for estimation of parameter in combined mode conduction and radiation heat transfer

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