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The evaluation of the state of transformer oil-immersed insulation based on the traditional methods becomes unreliable due to the nonuniform aging effect. To address this issue, a novel model for accessing the aging information of transformer oil-immersed insulation at the hotspot is proposed. In this article, frequency-domain spectroscopy (FDS) is selected as the carrier for characterizing the aging states of oil-immersed insulation. The multiobjective function for reversing the FDS of the hotspot is constructed based on the dielectric response equivalent circuit. Then, the nondominated sorting genetic algorithm with the multiconstraint scheme is proposed to solve the multiobjective function, by which the FDS data of the transformer hotspot region are acquired. The verification results indicate that the average standard deviation between the computed FDS of the hotspot region and the measured data is less than 0.26. The contribution of this article is in the exploration of the proposed model as a potential tool to extract the FDS data of the hotspot region. This will promote a more reliable aging evaluation of the transformer oil-immersed paper insulation at the hotspot. [ABSTRACT FROM AUTHOR]

The generation and distribution of furfural are significantly affected by moisture content in the insulating paper. However, the diffusion mechanism of furfural under moisture effect is not clear, hence, this study investigates the effect of moisture on the diffusion characteristics of furfural by combining macro experiment and micro molecular dynamics simulation. The simulation results demonstrate that the increased moisture content in paper accelerates the diffusion of furfural from paper to oil, which is also proved by the experimental data. Then, the results of interaction energy analysis indicate that the increase of moisture enhances the van der Waals force between oil and furfural. The present findings are expected to improve the theoretical level of furfural analysis, which will facilitate the aging evaluation of the transformer insulating paper based on furfural. [ABSTRACT FROM AUTHOR]

Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer. The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation. However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored. Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm. In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture. Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm. The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions. In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures. [ABSTRACT FROM AUTHOR]

The results of frequency domain spectroscopy (FDS) will be affected by moisture which often leads to an unreliable condition prediction of oil-immersed cellulose insulation in transformers. In view of this, a solution is proposed in this study by adopting a fitting fingerprint database (FFD) technique, which can be utilized for condition prediction of aging and moisture content. In the current work, the relevant fingerprints that could characterize the insulation aging condition and moisture content are extracted from the FDS curves and the DC conductivity of the transformer oil. The FFD is then constructed by fitting fingerprints. The accuracy of the reported FFD technique is verified from tests on oil-immersed pressboard prepared in the lab and a transformer from the field. In this respect, the present contribution highlights FFD might be used as a powerful tool for condition prediction of oil-immersed cellulose insulation in transformers. [ABSTRACT FROM AUTHOR]

The distribution behavior of furfural in insulation systems is influenced by the oil–pressboard mass ratio. In addition, the equilibrium distribution of furfural between oil and pressboard will be disturbed after oil replacement. Therefore, it is of great significance to study the distribution ratio of furfural in oil with various oil–pressboard mass ratios after oil replacement. In this research, an accelerated thermal aging experiment and oil replacement experiment were conducted in the lab. Furthermore, the equilibrium characteristics of furfural dissolved in oil with various oil–pressboard mass ratios were studied. Multiple regression analysis was used to analyze the relationship between the oil–pressboard mass ratios and the distribution ratio of furfural in oil. The equilibrium distribution model of furfural was thus obtained. Afterwards, the modified furfural distribution model under oil replacement conditions was established. A novel scheme is provided for analyzing the equilibrium characteristics of furfural under various oil–pressboard mass ratios after oil replacement. The work of this paper is expected to improve the accuracy of furfural analysis. [ABSTRACT FROM AUTHOR]

The measurement of polarization and depolarization currents (PDC) based on time–domain response is an effective method for state assessment of cellulose insulation material in oil-immersed electrical equipment. However, the versatility of the data obtained at different temperatures is limited because of the temperature dependence of the PDC. In this respect, the universal conversion of PDC data at different temperatures is an essential aspect to improve the accuracy of the determination of insulating properties of cellulose materials immersed in the oil. Thus, an innovative temperature conversion method based on polarization time-varying current (PTC, obtained by multiplying the polarization current and time) is proposed in this article. In the current work, the PTC data at different temperatures are obtained from the oil-immersed cellulose pressboards with different moisture. Afterwards, the functional model based on the power series theory is used to simulate the PTC data, through which the coefficients of the power series are found related to the test temperature of the PTC and the moisture content (mc%) of the oil-immersed cellulose pressboards. Furthermore, the functional relationship among moisture, test temperatures, and the feature parameter calculated by these coefficients is established. Thus, the PTC data at various temperatures can be calculated by the established function. The potential application ability of the proposed method is verified by comparing the calculated results with the measured results obtained from the various samples. [ABSTRACT FROM AUTHOR]

Frequency-domain spectroscopy (FDS) is demonstrated to be affected by electrode polarization and conductance behavior in the low-frequency ranges, which causes the unreliable prediction results of transformer cellulose insulation. In order to solve this issue, a fingerprint database based on the dielectric modulus is reported to predict the degree of polymerization (DP) and moisture content of cellulose insulation. In the current work, the relevant fingerprints that characterize the insulation conditions are obtained by studying the dielectric modulus curves of cellulose insulation with various insulation conditions, as well as the DC conductivity of transformer oil. Then, the dielectric modulus fingerprint database is established in the lab, and the accuracy of the reported fingerprint database is later verified. As a potential tool, the dielectric modulus fingerprint database is tested by several samples, and the results demonstrate that the accuracy of this method is more than 80%. In that respect, an interesting discovery of this paper is that the dielectric modulus fingerprint database may be a helpful tool for conditions prediction of the transformer cellulose insulation system. [ABSTRACT FROM AUTHOR]