Frequency Domain Spectroscopy Data Research Articles

Health assessment of converter transformer pressboard insulation based on FDS and digital image processing

The stable operation of the converter transformer is an essential task for power system operation reliability and security. Nowadays, Frequency domain spectroscopy (FDS) technology is prominently utilized for assessing oil-impregnated pressboard insulation. The present study examines the effect of the pressboard insulation material as a function of frequency and elevated temperature. The experimental analysis on oil-impregnated pressboard insulation is carried out at temperatures from 30 °C to 130 °C with an incremental rise of 20 °C intervals with frequency variation from 1 Hz to 10 MHz. The frequency-dependent permittivity, conductivity and loss tangent angle studies also confirm the deterioration of oil-impregnated pressboard insulation. The surface morphological changes inside the pressboard insulation are recorded with the help of scanning electron microscopy (SEM). The synergistic effect generated on pressboard insulation is examined by fiber width changeand image processing approach by randomly selecting the average of three local areas of SEM image. A canny operator is selected to extract the exact boundaries of images and more change is recorded in the edge detection count after 90 °C.The porosity and pore size distribution can be increased with elevated temperatures. A single-phase, 315 MVA valve side star winding with 60 discs of single-phase converter transformers model is developed in MATLAB Simulink. An impulse of 100 kV, 1.2/50μsec is applied across the star winding to identify the pressboard insulation degradation derived from FDS data with the help of mathematical morphology and wavelet transform technique. The energy of the wavelet coefficient on the neutral current capture during the impulse test adds a significant contribution to analyzing pressboard insulation degradation. The results presented are in good agreement with the published work.

An Improved Geometric Model for Constructing Frequency-Domain Spectroscopy of Nonuniform Aged Oil–Paper Insulation

Frequency-domain spectroscopy (FDS)-based methods are rarely reported for aging evaluation of nonuniform aged transformer insulation, in part due to the unavailability of the database. To address this issue, this work proposes an improved geometric model for constructing the FDS of oil–paper insulation under axial nonuniform aging. First, the mathematical expression of the model is theoretically derived based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${XY}$ </tex-math></inline-formula> model and nonuniform aging theory. Then, the composite structures for simulating the axial nonuniform aging are constructed in the lab condition, with the FDS data and degree of polymerization (DP) being collected by measurements. Finally, by comparing the FDS calculated by the proposed model and the collected data from the laboratory composite structure, the accuracy of the improved geometric model has been preliminarily verified. The obtained results indicate that the mean relative error (MRE) is less than 14.40%. In that respect, this work is expected to provide an effective tool for constructing the FDS database of nonuniform aged oil–paper insulation.

Acquisition of FDS for Oil-Immersed Insulation at Transformer Hotspot Region Based on Multiconstraint NSGA Model

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.

Influence of temperature on frequency domain spectroscopy detection of transformer bushings

Frequency domain spectroscopy (FDS) technology has been widely used to evaluate the insulation state of electrical equipment. However, there are still many in-adaptability when using FDS technology to evaluate the insulation state of transformer bushings. Especially, the result will be affected by temperature in the quantitative diagnosis of moisture degree. The influence of different temperatures on FDS detection data of transformer bushings has been studied to solve this problem in this paper. To obtain the FDS test data curves of transformer bushings at different temperatures, the transformer bushings experiment platform was built in the laboratory. And the bushing samples at different temperatures was measured by FDS detection method, which started at 30 °C and rising at 10 °C intervals until the temperature reached 80 °C. The result shows that the temperature will have a serious impact on the FDS detection data, especially the R0 and C5 in the extended Debye model. To solve the influence of temperature, FDS data correction curves with different temperature has been built in this paper. And its feasibility has been verified by FDS data of transformer bushings with 0% and 1.89% water content. The obtained results can promote the application of FDS method in the evaluation of transformer bushings insulation state.

Aging evaluation for transformer oil-immersed cellulose insulation by using frequency dependent dielectric modulus technique

Frequency domain spectroscopy (FDS) technique is widely used for the aging evaluation of the transformer oil-paper insulation system. However, the typical ‘relaxation information’ of FDS data in low-frequency regions is seriously affected by both the electrode polarization effect and conductance effect, which often leads to an unsatisfactory aging condition evaluation result. Existing researches reveal that the Frequency Dependent Dielectric Modulus M*(ω) technique can be utilized for solving this issue. Since the relevant parameters, that aims to represent the aging condition of transformer oil-immersed cellulose insulation, extracted from M*(ω) has been rarely reported, and the feasibility demonstration of the field testing by exploiting the relevant parameters extracted from M*(ω) is also rare. Therefore, the novel attempt for obtaining the relevant parameters of M*(ω) were reported in this paper. Moreover, the accuracy and feasibility of the extracted parameters were later verified with the evaluation test in the lab and field conditions. In that respect, the present contribution demonstrated that the extracted parameters could be used as an available tool for the aging evaluation of transformer oil-immersed cellulose insulation.

FDS Measurement-Based Moisture Estimation Model for Transformer Oil-Paper Insulation Including the Aging Effect

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.

A modified X-model of the oil-impregnated bushing including non-uniform thermal aging of cellulose insulation

The frequency domain spectroscopy (FDS) technique is widely used for the aging condition assessment of cellulose insulation on the bushing core. Generally, these assessment activities could be completed by the analysis of the FDS data between the laboratory sample and field bushing with the help of the traditional X-model. However, the aging condition of cellulose insulation on the bushing core is non-uniform due to the existence of temperature gradient. In this case, the traditional X-model is considered inappropriate since it ignored the effect of non-uniform thermal aging. In view of this issue, a modified X-model is reported in this work. Relying on the theoretical analysis of the modified X-model, both the mathematical expression and the equivalent circuit is derived naturally. The reliability of the modified X-model is confirmed by the experimental researches. In addition, the effect of aluminum foil in the modified X-model on FDS data was also studied. Findings reveal that the addition of aluminum foil would cause a slight deviation of the FDS curves. Therefore, the modified X-model might utilize to study the information of the non-uniform thermal aging. In that respect, the contribution of this work is in the exploration of the modified X-model as a potential tool for connecting FDS data corresponding to the lab condition and field condition including non-uniform thermal aging of cellulose insulation on the bushing core.

Normalization for FDS of Transformer Insulation Considering the Synergistic Effect Generated by Temperature and Moisture

Frequency domain spectroscopy (FDS) technique is widely applied in the condition assessment of the oil-paper system in power transformers. However, the synergistic effect generated by moisture and temperature on the FDS data cannot be analyzed by the existing model since the single independent variable (moisture or temperature) is considered in the construction of the model. To quantify such the synergistic effect, a novel method that utilized for normalizing (or standardizing) the FDS curve is reported based on the theory of the power series and fitting analysis. The present findings reveal that the reported method is capable of predicting the dielectric loss (tanδ) curve under diverse test conditions, in which the average error is less than 7%. The synergistic effect can be also explored by using the extracted feature parameters. The potential application is then proved to make up for the measurement errors during the FDS test, the findings are expected to promote the moisture analysis of the transformer insulation.

A Circuital Model-Based Analysis of Moisture Content in Oil-Impregnated-Paper Insulation Using Frequency Domain Spectroscopy

The explicitly numerical or well-defined correlations between the dielectrics responses (DRs) and dielectric essences of oil-impregnated paper (OIP) insulation have not been fully understood yet. As a result, it is rather difficult to quantitatively diagnose the critical insulation condition, like determining moisture content in insulation paper (MCP) of power equipment only using electrical-based techniques such as frequency domain spectroscopy (FDS). To obtain MCP value, from a new perspective of parametric study on the circuital DR equivalence of OIP insulation-extended Debye model (EDM), the present contribution introduces a novel approach by exploring the pattern when EDM parameters vary with MCP and temperatures T. Further, mathematical correlations are developed between sensitive R-C values and MCP-T values. On the above analysis, small-scale physical models of real-life transformer bushings were prepared as test samples. Their OIP condenser bodies were artificially absorbed different controlled quantities of moisture and conditioned at different temperatures to record corresponding FDS results. Then a hybrid genetic algorithm combined with the Levenberg-Marquardt algorithm was proposed to estimate the EDM parameters by fitting the measured FDS data. Sensitive parameters therein were identified using subspectrum decomposition and formulated with MCP-T values so that a reliable moisture content estimation can be achieved once the testing temperature and FDS recordings of an insulation body are known.

A Novel Curve Database for Moisture Evaluation of Transformer Oil-Immersed Cellulose Insulation Using FDS and Exponential Decay Model

In recent decades, moisture evaluation of transformer oil-immersed cellulose insulation has been widely studied by using the frequency domain spectroscopy (FDS) technique. However, due to the difficulties in sample preparation, most of the existing evaluation methods are based on a small number of samples, which greatly limits the evaluation accuracy of these methods. To accurately evaluate moisture inside transformer oil-immersed cellulose insulation, a novel method combining the FDS technique and the exponential decay model is proposed. In current work, the exponential decay model is introduced to analyze the FDS data, which can be used to assess the quantitative relationship between moisture and extracted parameters. Then, the FDS curves with different moisture can be predicted by using these parameters to form a curve database. Findings reveal the relative error of evaluation results in lab condition is less than 10.57% when using the proposed curve database. In that respect, the feasibility of the proposed database is proved for moisture evaluation of transformer oil-immersed cellulose insulation.

Effect of measurement temperature on power transformer insulation diagnosis using frequency‐domain spectroscopy

Frequency-domain spectroscopy (FDS) is a widely accepted method for the estimation of moisture content in oil-paper insulation of power transformer. Researchers have shown that measurement temperature is an important factor that not only shifts the tan δ curve both horizontally and vertically with respect to frequency axis but also affects the value of paper-moisture content of transformer insulation. This implies FDS data measured at different temperatures yields different results even if the insulation under consideration remains more or less unaffected. Availability and construction of master curve for in-service real-life units might not always be available. This study proposes a method that is capable of predicting the profile of tan δ curve at different measurement temperatures. Existing expressions available for predicting paper-moisture does not consider the effect of change in measurement temperature. This study also proposes modification of existing expression to predict the moisture content of insulation paper at any measurement temperature. In addition, this study outlines a method to evaluate the value of activation energy ( E a ) from the insulation response. The discussed technique is first tested successfully on data recorded from a laboratory sample. Thereafter, the method is applied on data collected from a real-life power transformer.

Parameter Identification and Calculation of Return Voltage Curve Based on FDS Data

Frequency domain spectroscopy (FDS) method based on dielectric response measurement technique is being investigated as a potential tool for condition assessment of transformer insulation. It is a simple and nondestructive method to monitor insulation status of transformer. This paper establishes a mathematical model with the data of frequency domain spectroscopy for solving parameter of dielectric response equivalent circuit, which is a group of nonlinear equations. It is difficult to solve the problem using nonlinear least square optimization method. The problem solving for nonlinear equations is transformed to the constrained optimization problem using nonlinear least square method, and then, the bacterial foraging algorithm is used for solving the problem. According to identified circuit parameters, the FDS curve is obtained, which compares the calculated data with the measured. The relative error is controlled in 1%, which demonstrates the parameter identification method is successful. The return voltage curve can be calculated from the estimated circuit parameters for verifying insulation condition. Therefore, the transformer insulation condition can be diagnosed using multi-physical parameters with FDS and RVM data comprehensively, to improve the accuracy of diagnosis.