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The traditional method for transformer moisture diagnosis is to establish empirical equations between feature parameters extracted from frequency domain spectroscopy (FDS) and the transformer’s moisture content. However, the established empirical equation may not be applicable to a novel testing environment, resulting in an unreliable evaluation result. In this regard, it is acknowledged that FDS combined with machine learning is more suitable for estimating moisture content in a variety of test environments. Nonetheless, the accuracy of the estimation results obtained using the existing method is limited by the algorithm’s inability to generalize. To address this issue, we propose an AdaBoost algorithm-enhanced back-propagation neural network (BP_AdaBoost). This study creates a database by extracting feature parameters from the FDS that characterize the insulation states of the prepared samples. Then, using the BP_AdaBoost algorithm and the newly constructed database, the moisture estimation models are trained. Finally, the results of the estimation are discussed in terms of laboratory and field transformers. By comparing the proposed BP_AdaBoost algorithm to other intelligence algorithms, it is demonstrated that it not only performs better in generalization, but also maintains a high level of accuracy. [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]

A growing body of evidence suggests that the ethanol dissolved in the oil can be used as an indicator to effectively monitor the aging behavior of insulating paper. In this study, thermal aging experiments are performed on oil-paper systems with different initial moisture contents and aging temperatures, and then a gas chromatography-mass spectrometer equipped with an automatic headspace sampler is used to test the ethanol contents of the oil samples. The variations and correlations of ethanol contents with temperature, moisture and aging degree are analyzed. The experimental results show that the ethanol contents in oil had a strong correlation with temperature, and is also affected by the cellulose hydrolysis. In addition, a preliminary lifespan assessment of the oil-paper systems under laboratory conditions is conducted by establishing the relationship between ethanol and the degree of polymerization. Then, the time-temperature superposition method is applied to the relationship, and a lifespan model with any temperature is determined according to the ethanol kinetic equation. Based on the model, further observation of the fitting parameters in the case of the two moisture contents (0.8%, 2.3%), the goodness of fit reached higher values of 0.961 and 0.973, respectively. The results of this promising model can help us to a better understanding of ethanol indicator to condition assessment for the transformer paper insulation. [ABSTRACT FROM AUTHOR]

The total/partial oil change treatment can affect the furfural partitioning in oil-paper-pressboard insulation, resulting in poor accuracy of furfural analysis. Moreover, due to the high cost of total oil change, partial oil change is more practical during transformer operation. This paper investigates the furfural partitioning in the oil-paper-pressboard insulation under partial oil change conditions. First, the initial furfural partitioning equation with non-oil change is proposed based on the obtained furfural content in oil, paper, and pressboard. Then, the modified furfural partitioning model under partial oil change condition is established by introducing a correction factor. Moreover, a preliminary correction scheme for the furfural partitioning model considering the influence of moisture and acid is provided. Finally, furfural content in oil-Kraft paper and oil-thermally upgraded Kraft (TUK) paper insulation are discussed, and results indicate that the proposed modified furfural partitioning model is not suitable for the oil-TUK paper insulation. This work can provide theoretical support for furfural analysis of oil-immersed transformers. [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]

Frequency domain spectroscopy (FDS) is employed to evaluate the moisture content and aging state of oil-paper insulation in power transformers. Oil-paper insulation samples with different moisture content and aging states were primarily prepared in the laboratory. A method was proposed based on a study of the influence of the aging state and moisture content on the frequency domain dielectric property of oil-paper insulation. This method distinguishes the influence of moisture content and aging state on FDS through the imaginary part of complex relative permittivity ( εr″). Relevant characteristic parameters that characterize the moisture content and aging state of oil-impregnated pressboards were also extracted. Results reveal that the extracted characteristic parameters are sensitive to moisture content in the range of 10−1 Hz to 101 Hz and in the range of 10−3 Hz to 10−1 Hz for the aging state. An obvious exponential relationship exists between the maximum values of the characteristic parameters in the given frequency ranges and degree of polymerization and moisture content. The new method can be utilized for a non-destructive condition assessment of oil-paper insulation of transformers. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The service life of a transformer is determined by the condition of its oil?paper insulation. A modified Cole?Cole model was introduced to describe the frequency domain spectroscopy (FDS) curves based on the classic Debye relaxation model and thereby accurately determined the ageing state of the insulation of a power transformer using FDS. Oil?paper insulation samples with different ageing states are prepared under laboratory conditions, and a characteristic parameter is extracted to quantify their ageing states after a detailed investigation of the frequency domain dielectric properties of the samples. Results reveal that the modified Cole?Cole model, including the ? relaxation components, dc and hopping conductivity components, can be employed to effectively model the frequency dielectric characteristics of oil?paper insulation. Furthermore, the extracted characteristic parameter from this model, ??, can be utilized in the quantitative evaluation of the ageing states of transformer insulation. A case example is discussed to demonstrate the applicability of the proposed technique in the condition assessment of field power transformers. [ABSTRACT FROM AUTHOR]

In order to distinguish the influences of moisture and aging on the frequency domain dielectric response of oil-paper insulation and better apply frequency domain spectroscopy (FDS) to assess the insulation condition of power transformers, the oil-paper insulation samples with different moisture contents and different aging states were prepared in the laboratory. The FDS of the samples were tested and a group of characteristic parameters were extracted from dissipation factor (tan?) curves which could be used to assess the moisture content and aging states of oil-paper insulation respectively. The quantitative relationship among characteristic parameters, degree of polymerization (DP) and moisture contents (Km.c) was accurately established. The observations show that the proposed characteristic parameters are sensitive to the moisture in 10-3-102Hz, while the aging states influence the characteristic parameters in 10-3-10-1Hz. Meanwhile, an exponential relationship equation which could be used to assess the oil-paper insulation condition was established among the characteristic parameters, DP and the moisture content. Finally, the evaluation technique proposed in this paper was used to diagnose the insulation condition of several field transformers in this way, and its validity was preliminary and reasonably verified. [ABSTRACT FROM AUTHOR]

The support vector machine (SVM) combined with the genetic algorithm (GA) has been utilized for the fault diagnosis of transformers since its high accuracy. In addition to the fault diagnosis, the condition assessment of transformer oil-immersed insulation conveys the crucial engineering significance as well. However, the approaches for getting GA-SVM used to the moisture prediction of oil-immersed insulation have been rarely reported. In view of this issue, this paper pioneers the application of GA-SVM and frequency domain spectroscopy (FDS) to realize the moisture prediction of transformer oil-immersed insulation. In the present work, a method of constructing a GA-SVM multi-classifier for moisture diagnosis based on the fitting analysis model is firstly reported. Then, the feasibility and reliability of the reported method are proved by employing the laboratory and field test experiments. The experimental results indicate that the reported prediction model might be serviced as a potential tool for the moisture prediction of transformer oil-immersed polymer insulation. [ABSTRACT FROM AUTHOR]