Power Transformer Insulation Diagnosis Research Articles

Effective analysis of time‐domain dielectric response for reliable diagnosis of power transformer insulation using statistical parameter evaluated from time‐varying model

Various types of insulation models with time-invariant parameters are available in the literature. Depending on the aging sensitive performance parameters to be evaluated, different models need to be employed (e.g. XY model for oil and paper-conductivity, conventional Debye model (CDM) for paper-moisture and tanδ). While the XY model cannot be used for estimating paper-moisture directly, analysis based on CDM parameter becomes dependent on its branch parameters, which are non-unique. These factors lead to either incomplete or ambiguous insulation diagnosis. These problems are resolved using the proposed new insulation model containing unique time-varying branch parameters. Another major advantage of the proposed model is that it can be used to evaluate a host of performance parameters (like paper-conductivity, oil and paper-moisture, dielectric loss) thus giving a complete picture of the insulation concerned. The application of the proposed model is also tested on data collected from several real-life power transformers.

Corrigendum: Effect of charge accumulated at oil–paper interface on parameters considered for power transformer insulation diagnosis

Corrigendum: Effect of charge accumulated at oil–paper interface on parameters considered for power transformer insulation diagnosis

Compensating the effect of residual dipole energy on dielectric response for effective diagnosis of power transformer insulation

Analysis of relaxation current is a widely accepted method for diagnosis of power transformer insulation. The accuracy of such diagnostic tool is dependent on insulation model parameters which are formulated using relaxation current. This implies that the accuracy and hence the reliability of existing insulation diagnosis methods indirectly depends on the accuracy of the recorded polarisation depolarisation current. Sometimes during field measurement relaxation current measurement equipment fails to record proper current, even after application of dc charging voltage. As per utilities, this primarily happens due to improper/loose connections (this cannot be avoided entirely due to the involvement of human factors) and such situation is usually followed by checking and rectifying improper connection. The analysis presented in this study shows that the polarisation current recorded immediately after rectifying the correction is inaccurate and leads to the erroneous diagnosis. Furthermore, it is observed that in these cases, the measured and calculated (using insulation model) values of performance parameters like dissipation factor, polarisation index, and paper-moisture differ by a large extent. This work is aimed at removing the effect of this residual dipole energy introduced during the improper connection phase.

Effect of charge accumulated at oil–paper interface on parameters considered for power transformer insulation diagnosis

Polarisation and depolarisation current (PDC) measurement and analysis is one of the popular tools for effective diagnosis of power transformer insulation. Normally, it is assumed that polarisation current is the combination of the current due to dipole movement and conduction current. Similarly, the depolarisation current is only due to the relaxation of dipoles. However, it is found that after eliminating the effect of dc conduction from polarisation current the resulting current is not similar to that of measured depolarisation current. This shows some non-linearity is present in the system. This non-linearity occurs due to movement of trapped charge that resides in the interfacial region of oil–paper insulation. This study shows the effect of de-trapping charge on various performance parameters that are used for insulation diagnosis like paper moisture and dielectric dissipation factor (tanδ).

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.

Improvement of power transformer insulation diagnosis using oil characteristics data preprocessed by SMOTEBoost technique

This paper proposes a novel method for power transformer insulation assessment using oil characteristics. A hybrid algorithm, named as SMOTEBoost is implemented in the paper to improve the diagnosis accuracy and consistency. The SMOTEBoost can significantly enhance the generalization capability of artificial intelligence (AI) algorithms for transformer insulation diagnosis. This will provide important benefits for applying AI techniques in utility companies, i.e., an AI algorithm with its model built upon on a local dataset can be utilized globally to make transformer insulation diagnosis. The SMOTEBoost adopts Synthetic Minority Over-sampling Technique (SMOTE) to handle the class imbalance problem, in which data points belonging to different fault types or insulation conditions are unevenly distributed in the training dataset. By using this boosting approach for reweighting and grouping data points in the training dataset, the SMOTEBoost facilitates AI algorithms consistently attaining desirable diagnosis accuracy. To verify the advantages of SMOTEBoost algorithm, it is integrated with a number of representative AI algorithms including support vector machine (SVM), C4.5 decision tree, radial basis function (RBF) network and k-nearest neighbor (KNN) to make transformer insulation diagnosis using various oil characteristic datasets collected from different utility companies. A statistical performance comparison amongst these algorithms is presented in the paper.

Pattern recognition techniques for power transformer insulation diagnosis-a comparative study part 2: implementation, case study, and statistical analysis

Transformer oil tests such as breakdown voltage, resistivity, dielectric dissipation factor, water content, 2-furfuraldehyde, acidity, and different dissolved gasses have been adopted in utility companies for evaluating the conditions of transformer insulation. Over the past 20years, various pattern recognition techniques have been applied for power transformer insulation diagnosis using oil tests results (oil characteristics). This paper investigates a variety of state-of-the-art pattern recognition algorithms for transformer insulation diagnosis. To verify the applicability and generalization capability of different pattern recognition algorithms, this paper implements 15 representative algorithms and conducts extensive case studies on eight oil characteristics datasets collected from different utility companies. A statistical performance (in terms of classification accuracy) comparison among different pattern recognition algorithms for transformer insulation diagnosis using oil characteristics is also conducted in the paper. Copyright (c) 2014 John Wiley & Sons, Ltd.

Pattern recognition techniques for power transformer insulation diagnosis-a comparative study part 1: framework, literature, and illustration

The condition of the insulation system of a power transformer has a significant impact on its overall reliability and serviceability. Transformer oil tests including breakdown voltage, acidity, dielectric dissipation factor, 2-furfuraldehyde, water content, and dissolved gases analysis have been commonly performed in utility companies to provide information regarding the conditions of transformer insulation. Over the past two decades, various pattern recognition techniques are proposed to interpret the oil tests results and make diagnosis on transformer insulation. However, there are still considerable challenging issues to be investigated before the pattern recognition technique can become a ready-to-use tool at utility companies. This paper provides a comparative study of pattern recognition techniques for power transformer insulation diagnosis using oil tests results. A general pattern recognition application framework will be outlined in the paper. And a comprehensive literature review on various pattern recognition techniques for transformer insulation diagnosis will be provided in the paper. The important issues for improving the applicability of pattern recognition techniques for transformer insulation diagnosis will also be discussed. A case study will be presented to demonstrate the procedure of applying pattern recognition techniques to practical transformer insulation diagnosis using oil test results. Copyright (c) 2014 John Wiley & Sons, Ltd.