Search

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.

Oil change, as an effective method, could improve the performance of liquid insulation. However, due to the furfural loss in oil after the oil change, the aging assessment results of cellulose insulation based on the traditional furfural-degree of polymerization (DP) model is not accurate. Besides, the different oil/pressboard mass ratio of the transformer also has a significant impact on the aging assessment results. Current research only focuses on the aging assessment based on furfural analysis under the single oil/paper mass ratio and oil change conditions. In view of this, the aging assessment model for transformer insulation based on furfural indicator under different oil/pressboard ratios and oil change conditions was studied. First, oil/pressboard insulation systems with different oil/pressboard mass ratios were performed for total oil changes. Then, the initial furfural-DP equation and the furfural corrected equation were obtained. Finally, the modified furfural-DP model extended to different oil/pressboard mass ratios under oil change was established. The proposed model is expected to promote the accuracy of the aging assessment of transformer cellulose insulation under different oil/pressboard mass ratios and oil change conditions.

Furfural, as a typical chemical indicator, is used to evaluate the aging state of the cellulose insulation in the transformer. However, partial oil replacement will lead to the loss of furfural in oil and disturb the equilibrium distribution of furfural between oil and cellulose insulation, which could affect the evaluation results of the aging state of the cellulose insulation. Unfortunately, the influence of partial oil replacement has not been focused on. Given this issue, this work studies the influence of partial oil replacement on dynamic diffusion behavior of furfural in oil-pressboard insulation. First, the dynamic diffusion behavior of furfural between insulating pressboard and oil after partial oil replacement is discussed, and the theoretical diffusion model of furfural is established. Second, based on the mass gain curve of furfural in oil, the diffusion coefficients under different oil replacement ratios are obtained. Finally, a formula for calculating the diffusion equilibrium time is proposed. This paper attempts to report a method for analyzing the impact of the oil replacement ratio on the dynamic diffusion of furfural. These findings are expected to improve the accuracy of the aging assessment results by using furfural analysis.

A novel model for predicting the frequency domain spectroscopy (FDS) curve is reported in this work, which is capable of analyzing the synergistic effect generated by temperature and moisture. The innovation of this work is an exploration of the reported model as a potential tool for predicting the FDS (dispersion loss/permittivity) curves of oil-immersed cellulose insulation under diverse temperatures and moisture. In that respect, the verification experiments indicated that the average relative error of the prediction results in lab conditions is less than 17%.

The dielectric modulus Μ∗(ω) is used for studying the frequency domain response and activation energy (E a ) of transformer immersed cellulose insulation. Existing literatures show that the temperature is a crucial factor affecting the Μ∗(ω) and aging rate of transformer oil-paper insulation, while the E a can be regarded as an indicator to reflect the average aging rate of the transformer immersed cellulose insulation. Therefore, the discussion of the temperature-dependence on both Μ∗(ω) and E a is of great significance. This paper attempts to report an approach for understanding the temperature impact on both Μ∗(ω) and E a . The obtained knowledge can be used to establish two available models for temperature correction on Μ∗(ω) and activation energy prediction of transform immersed cellulose insulation, respectively. These findings are expected to promote the reliability of the condition prediction result of transformer immersed cellulose insulation by using Μ∗(ω) and E a technique.

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.

Over the past few years, frequency domain spectroscopy (FDS) has been widely utilized to assess transformer insulation. Many investigations have revealed that the FDS measurement has been observed to be dependent on service conditions (aging, moisture, geometry, and temperature). During the onsite FDS measurement, the inner insulation temperature of the de-energized transformer gradually decreases, which affects the FDS results due to moisture migration from oil to the cellulose insulation. This process gives rise to misinterpreting of the condition of the transformer insulation. Presently, the master curve technique reported by several researchers is utilized for temperature correction in FDS measurements. In fact, various researchers have obtained different master curves, so making a universal approach to temperature correction has become an important topic. In the present investigations, a series of experiments was performed on oil-impregnated pressboard insulation under laboratory conditions which was followed by FDS measurements at lower than operating temperature and a universal master curve for temperature correction was studied. Activation energies from oil-impregnated pressboard tests support the effectiveness of the proposed universal approach for temperature correction.

Furfural content in oil is a valuable indicator of cellulose paper degradation and is widely utilized in aging assessment for power transformers. However, with the same aging degree of insulation paper, the actual furfural content in oil is influenced by temperature because furfural partitioning behavior in the oil-paper insulation system of power transformers changes with temperature. Thus, correction of the temperature effect on furfural partitioning is crucial for accurate assessment of the aging of power transformers. In this research, oil-paper samples with different furfural contents were prepared through an accelerated thermal aging test. A furfural equilibrium experiment was conducted at different temperatures to construct a furfural partitioning diagram. The mass fraction of furfural in oil and paper was calculated. Results showed that the mass fraction of furfural in oil increases with temperature rising and aging deepening, which means that furfural tends to migrate from paper to oil at high temperatures and deep aging conditions. The change in furfural partitioning between oil and paper at different temperatures causes a deviation in paper aging assessment. To correct the temperature effect on furfural partitioning, a furfural correction factor was developed to normalize furfural contents in oil at different temperatures to a reference temperature. The furfural correction factor was verified by experimental data and then introduced into the relation between degree of polymerization of paper and furfural content of oil. Verification results showed that the corrected equation can enhance the accuracy of paper aging estimation at different temperatures. The findings of this work indicated temperature effect on furfural partitioning should be considered when determining the furfural thresholds and evaluating the aging condition of power transformers.

This paper aims to improve recognition accuracies of single sources of partial discharge (PD) by employing image-oriented feature extraction and selection algorithms. 419 diversified samples of PD data acquired from typically artificial defect models, where the defect size, applied voltage and insulation aging are taken into account, are employed for algorithms validation. In the proposed method, the PD gray images are decomposed into various vectors by two-dimensional principal component analysis (2DPCA) on horizontal and vertical directions, respectively, in which 9 representative parameters are extracted from each image vector. Based on the 419 PD samples, the proposed image features are confirmed to achieve better PD recognition performances than typical image compression methods relevant to 2DPCA. To further improve the recognition performance and reduce the feature dimension, the feature selection technique based on non-dominated sorting genetic algorithm II (NSGA-II) is adopted to search the optimal 2DPCA features. By using the proposed image-oriented feature extraction and selection algorithm, fuzzy k-nearest neighbor classifier (FkNNC), back-propagation neural network (BPNN) and support vector machine (SVM) with 16-dimensional 2DPCA features can now achieve 96.4%, 94.4% and 97.5% accuracies, respectively. The increment of average recognition accuracies of 5%–7% are obtained by different classifiers compared with phase-resolved partial discharge (PRPD) statistical features in previous studies. The investigations on the recognition results relevant to image distortion, cavity discharge change with time and PD contaminated by random noises with different level confirm the robustness of the 16-dimensional 2DPCA features, which signify a great potential in applying the proposed method to field PD pattern recognition in the future.

Dissolved gas analysis (DGA) of oil is used to detect the incipient fault of power transformers. This paper presents a new approach for transformer fault diagnosis based on selected gas ratios concentrated in oil and support vector machine (SVM). Firstly, based on IEC TC 10 database, the optimal dissolved gas ratios (ODGR) are obtained by genetic algorithm (GA) that is designed for simultaneous DGA ratios selection and SVM parameters optimization. Three traditional methods, namely, DGA data with SVM and back propagation neural network (BPNN), IEC criteria, and IEC three-key gas ratios with SVM and BPNN are employed for effectiveness comparison. The fault diagnosis results of IEC TC 10 database show that the proposed ODGR with SVM may be used as an alternative tool for transformer fault diagnosis. In addition, the robustness and generalization ability of ODGR is confirmed by the diagnosis accuracy of 87.18% of China DGA samples. The obtained results illustrate that it is preferable to apply the proposed ODGR to transformer fault diagnosis with the assistance of SVM.

Furfural analysis is considered as a reliable method to estimate aging of cellulose insulation paper and is widely applied in assessing the condition of field transformers. However, the actual concentration of furfural in insulating oil is influenced by oil replacement, and the correction method is seldom studied. In this research, an accelerated thermal aging test of oil-paper samples was conducted at 130 °C for 35 days. Insulating oil was replaced at early, middle, and late aging stages to investigate furfural loss during the oil replacement process and its effects on assessing the aging of insulation paper. Results showed that furfural in oil obviously declined after oil replacement, which caused a severe error in paper aging evaluation. To correct furfural loss, the furfural correction factor, which is defined as the ratio of the furfural content in oil after oil replacement to the furfural content before oil replacement, was introduced into the furfural analysis method. The parameters and influencing factors of the furfural correction function were researched. The furfural correction function was verified using experimental data obtained under laboratory conditions and field data collected from 44 in-service transformers. The results showed that the furfural correction function effectively corrects furfural loss during oil replacement and improves the accuracy of estimating paper aging. Finally, the furfural correction function was extended to the operating conditions of field transformers.