Chandima Ekanayake

Frequency response of oil impregnated pressboard and paper samples for estimating moisture in transformer insulation

Knowledge about moisture content in oil impregnated paper insulation is essential when estimating remaining lifetime of power transformers. Direct evaluation of moisture content is rarely possible due to inaccessibility of the internal insulation system in transformers. Therefore, various indirect estimation techniques are utilized. Frequency domain spectroscopy (FDS) measurements of transformer insulation belong to this group. To perform high quality interpretation of results of FDS measurements a good knowledge on dielectric responses in oil impregnated pressboard and paper is required, especially as it refers to their variation with water content and temperature. The aim of this paper is to provide an open access to the frequency domain spectra of oil impregnated paper and pressboard samples, which can then be used in modeling of the results of diagnostic measurements in power transformers.

Field experiences with measurements of dielectric response in frequency domain for power transformer diagnostics

Some new methods, based on characterization of the dielectric response of transformer insulation in time and frequency domains, have been pointed out by CIGRE as suitable to assess moisture content in pressboard and paper. Measuring devices available today on the market are quite robust in field conditions; however there is a risk that undesired internal and external factors might influence the measurements. These influences can lead to mistaken interpretation of insulation wetness. Operators should therefore be aware of the risks appearing at different measuring conditions and of the precautions that can be taken to minimize their effects. This work presents a systematic investigation on the influence of some factors on the results of dielectric response measurements in field conditions. The errors that can be committed during the measurements if certain precautions are not taken, with different equipment connected in parallel, are evaluated. The influence of rain and electromagnetic disturbances are also analyzed proposing solutions to attenuate their effects. Finally, a discussion on the influence of temperature distribution follows. The work is concentrated on frequency response measurements, although some results from the use of polarization and depolarization current technique are also discussed.

Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources

Partial discharge (PD) source classification aims to identify the types of defects causing discharges in high voltage (HV) equipment. This paper presents a comprehensive study of applying pattern recognition techniques to automatic PD source classification. Three challenging issues are investigated in this paper. The first issue is the feature extraction for obtaining representative attributes from the original PD measurement data. Several approaches including stochastic neighbour embedding (SNE), principal component analysis (PCA), kernel principal component analysis (KPCA), discrete wavelet transform (DWT), and conventional statistic operators are adopted for feature extraction. The second issue is the pattern recognition algorithms for identifying various types of PD sources. A novel fuzzy support vector machine (FSVM) and a variety of artificial neural networks (ANNs) are applied in the paper. The third issue is the identification of multiple PD sources, which may occur in HV equipment simultaneously. Two approaches are proposed to address this issue. To evaluate the performance of various algorithms in this paper, extensive laboratory experiments on a number of artificial PD models are conducted. The classification results reveal that FSVM significantly outperforms a number of ANN algorithms. The practical PD sources classification for HV equipment is a considerable complicated problem. Therefore, this paper also discusses some issues of meaningful application of the above proposed pattern recognition techniques for practical PD sources classification of HV equipment.

Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers

With the integration of data and information obtained from a variety of chemical and electrical tests on transformer insulating oil, it is possible to evaluate the health condition of the insulation system of an in-service power transformer. This paper develops an intelligent algorithm for automatically processing the data collected from oil tests and determining a health index for the transformer insulation system. This intelligent algorithm adopts a fuzzy support vector machine (FSVM) approach, which constructs a statistical model using a training database based on the historic data collected from 181 in-service power transformers. The procedure of constructing the training database, the formulation and implementation of FSVM and the data preprocessing methods for dealing with a class imbalanced training database is presented in this paper. Numerical experiments are also conducted to evaluate the performance of the algorithms developed in the paper.

Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding

This paper proposes a self-adaptive technique for partial discharge (PD) signal denoising with automatic threshold determination based on ensemble empirical mode decomposition (EEMD) and mathematical morphology. By introducing extra noise in the decomposition process, EEMD can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Through the kurtosis-based selection criterion, the IMFs embedded with PD impulses can be extracted for reconstruction. On the basis of mathematical morphology, an automatic morphological thresholding (AMT) technique is developed to form upper and lower thresholds for automatically eliminating the residual noise while maintaining the PD signals. The results on both simulated and real PD signals show that the above PD denoising technique is superior to wavelet transform (WT) and conventional EMD-based PD de-noising techniques.

Understanding the impact of moisture and ageing of transformer insulation on frequency domain spectroscopy

Moisture is one of the most significant parameters that can accelerate ageing in paper/pressboard insulation. In order to understand the impact of moisture and ageing on Frequency Domain Spectroscopy (FDS) measurements, oil impregnated pressboard samples of different moisture contents are prepared and aged at 105°C temperature. FDS measurements on pressboard samples are carried out at different ageing time. Other measurements such as moisture, degree of polymerization (DP), Furan and Dissolved Gas Analysis (DGA) are also performed on oil and pressboard samples. It is observed that FDS measurements on pressboard samples are mostly influenced by the moisture contents. Several other pressboard samples are prepared under different conditions to determine how FDS measurements are affected by moisture, aged oil and ageing of pressboard sample considering each of the parameter individually as well as in different combinations. Results suggest that FDS measurements are sensitive to the change in moisture, ageing products and ageing of pressboard (DP) sample. However, impact of ageing products and ageing of pressboard sample (DP) on FDS measurements are smaller in comparison to moisture.

Statistical learning techniques and their applications for condition assessment of power transformer

The condition of power transformers has a significant impact on the reliable operation of the electric power grid. A number of techniques have been in use for condition assessment of transformers. However, interpreting measurement data obtained from these techniques is still a non-trivial task; correlating measurement data to transformer condition is even more difficult. This paper investigates statistical learning techniques, which is able to learn statistical properties of a system from known samples and to predict the system output for unknown samples. Within the statistical learning framework, this paper develops a support vector machine (SVM) algorithm, which can be utilised for automatically analyzing measurement data and assessing condition of transformers. Case studies are presented to demonstrate the applicability of the developed algorithm for condition assessment of power transformer.

Power transformer fault diagnosis under measurement originated uncertainties

This paper addresses the problem of diagnosing the fault symptoms of power transformers with measurement originated uncertainties, which arise from the imprecision of samples (i.e. due to noises and outliers) and the effect of class imbalance (i.e. samples are unequally distributed between different fault types) in a training dataset used to identify different fault types. Two fuzzy support vector machine (FSVM) algorithms namely fuzzy c-means clustering-based FSVM (FCM-FSVM) and kernel fuzzy c-means clustering-based FSVM (KFCM-FSVM) have been applied in this paper to deal with any noises and outliers in training dataset. In order to reduce the effect of class imbalance in training dataset, two approaches including between-class weighting and random oversampling have been adopted and integrated with FCM-FSVM and KFCM-FSVM. The case studies show that KFCM-FSVM algorithm and its variants have consistent tendency to attain satisfied classification accuracy in transformer fault diagnosis using dissolved gas analysis (DGA) measurements.

Application of polarization based measurement techniques for diagnosis of field transformers

This paper presents both quantitative and qualitative analysis of several polarization based measurements, which include frequency domain spectroscopy (FDS) and polarization depolarization current (PDC) measurements on four different field transformers. The X-Y [1] model and the RC circuit model [2] are mainly used in the analyses. In this study sequential FDS/PDC measurements on the same unit are used to identify the progressive change of the insulation condition. Influence of oil conductivity and geometrical parameters on the estimates from FDS response is shown in detail. Based on this study a simple technique to improve the reliability from FDS measurement is discussed. Influence of the oil refurbishment process on polarization based measurements is also presented and discussed.

An Updated Model to Determine the Life Remaining of Transformer Insulation

In this paper, we present an update to the models used to calculate the remaining life of transformer paper insulation. A drawback to the current IEEE method is that it does not take into account the availability of water and oxygen on the life expectancy of Kraft paper insulation. We therefore set out to test an algorithm which does take these into account. For our investigation, we loaded three test transformers and ran them to near their end of life. Kinetic equations were applied to model the fall in the degree of polymerization of paper. Our model showed better agreement with the test results than that given from using the IEEE standard. The IEEE standard gives life expectancy for Kraft paper being aged in minimal oxygen under dry conditions, which is not necessarily representative of an old transformer. Wet paper and using high levels of oxygen can age nearly 40 times faster. The IEEE method needs to take the synergistic effect of water and oxygen on increasing the rate of paper aging into account.