Transformer Oil-paper Insulation Research Articles

Sawtooth-Based FDS for Rapid and Accurate Testing of Transformer Oil–Paper Insulation

Sawtooth-Based FDS for Rapid and Accurate Testing of Transformer Oil–Paper Insulation

Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

The influence of small molecule products on natural ester impregnated cellulose insulation paper under electric-thermal coupling field

Transformer oil-paper insulation materials are vulnerable to insulation failure due to the influence of voltage, high temperature, and other complex conditions. Additionally, during the operation of the transformer, the generation of harmful substances such as small molecule acids and water becomes a significant concern. In this study, we employed molecular simulation technology to investigate the effect of small molecule products on natural ester-impregnated cellulose insulation paper under electric-thermal coupling field. The results show that: under the action of electric-thermal coupling, the introduction of the electric field can weaken the molecular thermal motion to a certain extent, and the higher the temperature, the more pronounced the weakening effect of the electric field. When subjected to an electric-thermal coupling field, the mean square displacement of the cellulose molecular chains inside the models doped with water and formic acid increases compared to the oil-paper composite model, with an increase of 60.13 % and 98.25 %, respectively, at 700 K. The fraction of free volume in the model increases and the cohesive energy density decreases, the diffusion ability of molecules inside the model remains strong. The weakening effect of the electric field does not diminish the hydrolysis reaction between the small molecular products and the cellulose molecular chains. This leads to a decrease in model stability and ultimately to the breakdown of the insulation paper.

Evaluating the Effects of Temperature on Moisture Dynamics and Relaxation Mechanism in Transformer Oil-Paper Insulation

Evaluating the Effects of Temperature on Moisture Dynamics and Relaxation Mechanism in Transformer Oil-Paper Insulation

Insulation aging condition assessment of transformer in the visual domain based on SE-CNN

The aging of transformer oil–paper insulation (OPI) contributes significantly to transformer failure and reduced service life. Therefore, reliable and precise evaluation of the aging status of transformer insulation condition is crucial in averting transformer failure. This article proposes a novel deep learning-based aging condition assessment technique of transformers by combining FDS (Frequency domain spectroscopy) and squeeze-and-excitation-enabled convolutional neural network (SE-CNN). An intrinsic product-based transformation using a Gramian angular field is employed to dimensionally expand the 1-D FDS signals acquired from OPI samples into 2D images. Each feature extraction channel in the SE-CNN-based aging diagnosis framework is assigned a specific weight, enabling the model to concentrate on the most relevant features while discarding the rest. Furthermore, the effect of moisture variation on the model is also considered under three different conditions to ensure the consequence of compensation for the effect of moisture on FDS. A comparison with traditional machine learning models has also been made to ensure the viability of the proposed methodology. Additionally, the model is interpreted by creating explanation weight plots of the feature coefficients using a local interpretable model-agnostic explanations (LIME) method in Python. The findings demonstrate that the novel aging diagnosis framework visually assesses the transformer’s OPI system with the highest accuracy of 99.12% in the test set. Furthermore, it facilitates the automatic extraction of deep aging features, thus providing better generalization and convergence capability.

Bubble Formation and Motion Characteristics in the Transformer Oil-Paper Insulation System Under the Extremely Inhomogeneous Electric Field

Bubble Formation and Motion Characteristics in the Transformer Oil-Paper Insulation System Under the Extremely Inhomogeneous Electric Field

Dynamic Behavior and Residence Characteristics of Space Charge in Oil-Paper Insulation Under Polarity–Reversal Electric Field

As one of the core components of the direct current (dc) transmission system, the converter transformer bears not only the alternate current (ac) electric field but also complex electric fields, such as polarity reversal. In the process of polarity reversal, the phenomenon of space charge residence occurs in the oil-paper insulation inside the converter transformer, which distorts the internal electric field, leading to insulation deterioration or even failure. However, studies on the dynamic characteristics of space charge during the reversal process and the mechanism of insulation degradation are not comprehensive. Therefore, in this study, a test pulse matching circuit was developed to accurately match the test pulse with the polarization voltage of polarity reversal and perform the multi-point rapid test of the space charge transient characteristics during polarity reversal. The space charge tests (of the unaged specimen) were performed at the polarity–reversal time of 60, 30, and 10 s, and the influence of polarity–reversal time on the charge residence characteristics of oil-paper insulation was compared and analyzed. Additionally, space charge tests were performed on oil-paper insulation samples with accelerated electro–thermal–mechanical aging treatment for 0, 2.5, 5, 10, and 15 days to compare and analyze the influence of oil-paper insulation aging degree on charge residence characteristics under the action of multi-physical field coupling. Research results indicate that the shorter the reversal time, the shorter the time required for the charge to dissipate, and the more the charge resides, the stronger its enhancement effect on the electric field. As the aging degree of the sample increases, the dissipation rate of the resident charge becomes slower during the reversal process, and the resident charge increases, so that the maximum electric field intensity inside the sample increases under the same polarization electric field. The study of the charge residence characteristics and the dynamic charge behavior during the polarity inversion process has important guiding significance for clarifying the deterioration mechanism of the converter transformer oil-paper insulation under polarity–reversal electric field.

Effect of Formic Acid on Furfural Diffusion in Transformer Oil–Paper Insulation Based on Molecular Dynamics Simulation

Furfural content dissolved in transformer oil is affected by the furfural diffusion in oil-paper insulation. Previous studies focused on the effects of temperature and moisture on furfural diffusion, while the effect of formic acid on furfural is ignored. Given this, the effect of formic acid on the diffusion properties of furfural is studied in this work through molecular dynamics simulations and macroscopic experiments. First, oil-cellulose-furfural molecular models with different formic acid mass fractions are built, molecular dynamics simulations are performed separately. Also, the furfural diffusion coefficients for different molecular models are determined. Then, insulating paper with different formic acid mass fractions is prepared, and the furfural diffusion coefficients are calculated by combining the measured data and Fick’s second law. Finally, the effect of intermolecular interaction energy on the furfural diffusion in the oil-paper insulation is analyzed. The results revealed that formic acid increased the van der Waals force between furfural and insulating oil, which enhanced the adsorption of furfural and promoted furfural diffusion into the oil. This work explains the effect of formic acid on furfural diffusion, and provides a theoretical basis for furfural analysis.

Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks

Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a R2 value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet.

Aging Analysis of Transformer Insulation at Weakest Region: Dielectric Parameters Extraction via Immune Optimization

The conventional method is insufficient for determining the worst aging state of traction transformer oil-paper insulation due to the nonuniform aging. To address this issue, an extended relaxation distribution model based on multipopulation immune algorithm (ERD-MPIA) is proposed. This work collects frequency-domain spectroscopy (FDS) on the oil-paper system in a variety of nonuniform aging phases. Then, the MPIA is used to optimize the ERD model to establish the appropriate relaxation parameters for characterizing the worst aging stages. The verification results demonstrate that ERD-MPIA is capable of accurately decoupling the FDS of oil-paper systems subjected to nonuniform aging, allowing for the determination of the ideal relaxation parameters. Additionally, a distinct association is discussed between the relaxation parameters and the worst aging states. In that regard, the findings are expected to evaluate and detect the worst aging states of the traction transformer insulation system.

Effect of doped nano-SiO2, Al2O3, and polyhedral oligomeric sesquisiloxane on the thermodynamic properties of cellulose

Cellulose insulation paper is an essential part of the transformer oil-paper insulation system, and many types of nano-modified insulating paper are available. For studying the effects of various nano-modified molecules on cellulose and the differences in performance, the pure cellulose model and polyoctaaminophenylsesquioxane and grafted 3-aminopropyltriethoxysilane (KH550) SiO2 and Al2O3 cellulose models were developed using molecular dynamics methods, and the mechanical properties and thermally stability were calculated and compared. The findings show that, in general, all three nano-modification models improved the thermal stability and mechanical properties, with nano-SiO2-KH550 cellulose having the best overall modification effect. In terms of mechanical properties, the nano-SiO2-KH550 cellulose showed the largest increase in compressive strength, with an 18.1% improvement. In terms of thermal stability, the KH550 grafted nano-silica/cellulose composite model showed the best modification effect, as evidenced by the smaller cohesive energy density and free volume region compressing the space between cellulose molecules, making the modified cellulose more compact internally, thus achieving the effect of inhibiting the movement of cellulose chains.

Comprehensive evaluation of the transformer oil-paper insulation state based on RF-combination weighting and an improved TOPSIS method

The accurate identification of the oil-paper insulation state of a transformer is crucial for most maintenance strategies. This paper presents a multi-feature comprehensive evaluation model based on combination weighting and an improved technique for order of preference by similarity to ideal solution (TOPSIS) method to perform an objective and scientific evaluation of the transformer oil-paper insulation state. Firstly, multiple aging features are extracted from the recovery voltage polarization spectrum and the extended Debye equivalent circuit owing to the limitations of using a single feature for evaluation. A standard evaluation index system is then established by using the collected time-domain dielectric spectrum data. Secondly, this study implements the per-unit value concept to integrate the dimension of the index matrix and calculates the objective weight by using the random forest algorithm. Furthermore, it combines the weighting model to overcome the drawbacks of the single weighting method by using the indicators and considering the subjective experience of experts and the random forest algorithm. Lastly, the enhanced TOPSIS approach is used to determine the insulation quality of an oil-paper transformer. A verification example demonstrates that the evaluation model developed in this study can efficiently and accurately diagnose the insulation status of transformers. Essentially, this study presents a novel approach for the assessment of transformer oil-paper insulation.

Research on nonlinear characteristics for frequency domain dielectric response of transformer oil-paper insulation

Frequency domain dielectric response technology is widely used to detect the major insulation status of transformers. The influence of the test excitation amplitude on the nonlinear change of the dielectric characteristics of oil-paper insulation will interfere with the accuracy of the assessment results. In this paper, the electric field intensity distribution law of the oil gap in the oil-paper insulation model is obtained through test and simulation analysis. The influence of insulation aging, test temperature, and excitation frequency on the electric field distribution in the oil gap is clarified, it provides a reference for field strength for nonlinear loss calculation of oil-paper insulation. Meanwhile, based on the Garton’s ion motion nonlinear response model, considering the influence of the external electric field on the ion recombination in the oil gap, the nonlinear loss calculation model of transformer oil-paper insulation is corrected. Furthermore, the mechanism of positive and negative nonlinear loss variation for the oil-paper insulation model in a wide frequency range is revealed, and the calculation method of critical frequency and critical field strength of nonlinear loss variation in the oil-paper insulation model is proposed. Finally, according to the test results of the frequency domain dielectric response of oil-paper insulation with different aging and the apparent ion mobility in transformer oil, combined with the corrected calculation method proposed in this paper, the influence of the test excitation amplitude on the nonlinear change of the loss factor of oil-paper insulation is eliminated at different temperatures. The mean square error (MSE) of the corrected loss factor is reduced by 88.03% on average compared with that before.

Study on surface discharge characteristics and design method of large size oil-paper insulation of electrical transformer

When partial discharge occurs in the power transformer, the arc can further develop on the oil-paper interface between high and low potentials, and then form a path, resulting in large-area flashover. This will lead to a wide range of insulation damage, insulation failure, equipment shutdown and even large-scale economic losses. For the insulation damage caused by surface discharge of oil paper insulation, relevant scientific research institutions have carried out a large number of experimental studies. However, limited by the test conditions, the research focuses on the small-size model below 50mm, and mostly adopts the test device of uneven electric field. The research on the “large-size“ of the same scale inside the transformer is still blank. In this study, an oil-paper insulation surface discharge test platform with a large size of 50-150mm is built, the electric field conditions at the oil paper interface during the operation of transformers in engineering are simulated, and the surface discharge test of insulating paperboard is carried out by taking the same sample processing means as the engineering. Combined with the test results, a typical design optimization case of setting along surface corner ring to improve the allowable value of along surface discharge field and enhance along surface insulation margin is proposed. It lays a foundation for enriching the transformer oil-paper insulation design parameter system and improving the transformer oil paper insulation performance and operation reliability.

Prediction method of residual life of transformer oil‐paper insulation based on Wiener random process improved by strong tracking filter

In order to solve the problems of oil-paper insulation deterioration of oil-immersed power transformers, including the instability of performance degradation quantity at the early stage, non-linear deterioration process and uncertainty of life prediction, an improved Wiener stochastic process based on strong tracking filter (STF) is proposed to predict the remaining life of transformer oil-paper insulation in this paper. Firstly, in order to grasp the dynamic process from healthy state to current deterioration state, a deterioration model is established based on Wiener random process. Secondly, in order to improve the accuracy, the initial values of model parameters are estimated based on expectation maximization (EM) algorithm, then with the increase of monitoring data, model parameters are updated by using STF algorithm, and the probability density function of remaining life is derived to predict the remaining life of oil-paper insulation. Finally, the accuracy of the proposed method is verified by using furfural content as performance degradation index and accelerated thermal aging experimental data. Compared with the Bayesian–Wiener algorithm, the MSE of the proposed algorithm is reduced to 0.2138 and its MAPE is reduced more than 4%. Besides, the proposed method has the advantages of strong robustness and low uncertainty of residual life prediction.

Power transformer oil–paper insulation degradation modelling and prediction method based on functional principal component analysis

This study is for the case where the available data of power transformer oil–paper insulation is limited to a small amount furfural data, to solve the problems in oil–paper insulation degradation modelling, such as few samples available, unknown function form of the degradation process, differences of individual transformers among degradation processes, and commonality of degradation trends. A power transformer oil–paper insulation degradation modelling and prediction method based on functional principal component analysis (FPCA) is proposed. First, discrete furfural data of oil–paper insulation degradation are converted into continuous functional data, and the common degradation information of transformers is extracted based on functional time warping technology. Second, the principal components of insulation degradation are extracted based on FPCA method, and the difference of degradation information of individual transformers is obtained by analysing the differential of principal component scores. Subsequently, power transformer oil–paper insulation degradation model is constructed, and finally, the degradation model is updated based on Bayesian theory and the oil–paper insulation degradation is predicted. The example results show that compared with traditional transformer oil–paper insulation degradation modelling method, the proposed method has obvious superiority in model accuracy.

A Novel Recognition Method of Aging Stage of Transformer Oil-Paper Insulation Using Raman Spectroscopic Recurrence Plots

Timely identification of the aging stage of the transformer can effectively ensure the safe operation of the transformer and prevent the aging fault of the transformer. This work proposes the aging diagnosis of transformer using the Raman spectroscopic recurrence plot combined with learning vector quantization (LVQ) neural network. Oil-paper insulation samples at different aging stages are obtained by accelerated thermal aging tests. Acquisition of Raman spectra of insulating oil is from 800 to 3000 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> and converts to the Raman spectroscopic recurrence plot. The gray-level cooccurrence matrix (GLCM) is used to extract the characteristic information of the Raman spectroscopic recurrence plot. The optimal number of neurons in competitive layer of LVQ neural network is determined by the experimental method. A transformer aging diagnosis model based on the Raman spectroscopic recurrence plot is established. The results show that the diagnostic accuracy of the proposed diagnostic model reached 95%. The Raman spectroscopic recurrence plot has more abundant aging “fingerprint” characteristic information and the proposed diagnostic method shows potential for monitoring oil-paper insulation.

Diffusion Mechanism of Furfural in Transformer Oil–Paper Insulation Under Moisture Effect

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.

The Dielectric Voltage Response Characteristics of Transformer Oil–Paper Insulation and Its Aging State Assessment

The Dielectric Voltage Response Characteristics of Transformer Oil–Paper Insulation and Its Aging State Assessment

Research on Time Saving and Accurate Testing Techniques for Frequency Domain Spectroscopy of Transformer Oil–Paper Insulation

Frequency domain spectroscopy (FDS) is a non-destructive insulation test method that has been widely applied to the insulation evaluation of power transformers. However, a major shortcoming of conventional FDS is the long test duration. Existing studies on accelerated FDS tests suffer from a lack of accuracy and are difficult to apply to field testing. To tackle this problem, a novel FDS test method (named iFDS) is proposed to reduce the duration and improve the accuracy of the oil-paper insulation test. Specifically, the test duration is reduced firstly by signal superposition and waveform prediction. Then, the test accuracy is improved from aspects of the parameters design of excitation, the optimization of the test process, and time-frequency domain data processing. Finally, a test platform is built based on the above methods to implement iFDS testing. To verify the effectiveness of iFDS, the dielectric parameters (Tan&#x03B4; and C*) of oil-paper insulation were measured using the iFDS platform and compared with that of existing FDS methods in terms of duration and accuracy. Comparative experimental results show that iFDS is a superior method for saving time and accurate testing and has the potential to be versatile for evaluating more insulation objects.