B. T. Phung

A novel wavelet transform technique for on-line partial discharge measurements. 1. WT de-noising algorithm

Medium and high voltage power cables are widely used in the electrical industry with substantial growth over the last 20-30 years ago, particular in the use of XLPE insulated systems. Ageing of the cable insulation is becoming an increasing problem that requires development of reliable methods for on-line condition assessment. For insulation condition assessment of MV and HV cables, partial discharge (PD) monitoring is one of the most effective techniques. However on-site and on-line PD measurements are affected by electromagnetic interference (EMI) that makes sensitive PD detection very difficult, if not impossible. This paper describes implementation of wavelet transform techniques to reject noise from on-line partial discharge measurements on cables. A new wavelet threshold determination method is proposed with the technique. With implementation of this novel de-noising method, PD measurement sensitivity has been greatly improved. In addition, a full AC cycle data recovery can be achieved instead of focusing only on recovering individual PD pulses. Other wavelet threshold de-noising methods are discussed and examined under a noisy environment to compare their performance with the new method proposed here. The method described here has been found to be superior to the other wavelet-based methods

Development of computer-based measurements and their application to PD pattern analysis

After referring briefly to the earlier developments of computer-based PD measurements in the 1970s, the paper outlines how the introduction of new technology has resulted in the design and production of a number of digital systems for use in test laboratories and on site. In order to avoid overgeneralization, the basis of the hardware required for detection and recording of the PD pulses is described by reference to a particular system, it being understood that many detailed variations are possible depending on the application. The paper describes a number of parameters that can be calculated by software and used to characterize the PD behavior. These include the IEC-270 integrated quantities, statistical moments and other fingerprints for recognizing the PD patterns within the power frequency cycle. Examples are given of the patterns obtained on equipment and samples. These are necessarily selective and are intended to indicate how the techniques may be applied. It is concluded that these new methods will become valuable in procedures for assessing the condition of electrical insulation, in particular faults or defects, but must be used in conjunction with existing techniques until improved confidence is achieved in interpreting results, especially in respect of the various phase-resolved distributions and calibration procedures. >

Application of digital filtering techniques to the determination of partial discharge location in transformers

The capacitative network representation of transformer windings has long been used for the study of pulse distribution along the windings. However, the authors test results and those published in the literature show that theoretical analysis based on this equivalent network may not be applicable. It was found that the capacitive network approximations were valid only within certain ranges of frequency. The frequency ranges were determined through the analysis of terminal measurements. The capacitatively transferred components of the input pulse along the windings in the appropriate frequency range were then extracted by the aid of digital filtering techniques, and good agreement between measured and theoretical distributions was obtained. These techniques, in conjunction with a new straight-line interpolation method, were employed to locate simulated partial discharges in a 66 and 132 kV transformer winding. Measurement results showed that an accuracy of better than 5% of winding length was obtained. >

A novel wavelet transform technique for on-line partial discharge measurements. 2. On-site noise rejection application

For pt.I see ibid., p.3-14, (2007). Insulation assessment of HV cables requires continuous partial discharge (PD) monitoring to identify the nature of insulation defects and to determine any degradation trends. However to recover PD signals with sufficient sensitivity to determine such insulation degradation in substations with high levels of electromagnetic interference is a major challenge. This paper is the second of two papers addressing this challenge for on-line PD measurements in a noisy environment. The first paper described a wavelet transform-based method of interference rejection. This paper applies that method to the problem of on-site testing, using both laboratory tests and on-site tests. The laboratory tests were used to stimulate the noisy on-site testing environment, with use of transient pulse-like noise, discrete spectral interference (DSI) and white noise. These noise types have been successfully rejected by the method proposed in the first paper. In addition, on-site tests have been undertaken and have been able to detect PD signals in an old 11 kV substation multi-cable installation

Partial discharge localization in transformers using UHF detection method

The location of a partial discharge (PD) source inside a transformer can be determined from the time differences of arrival (TDOA) between signals that are captured by an array of UHF sensors. The TDOA can be acquired from the received PD waveforms. In this paper, three different methods of acquiring the TDOA from the PD waveforms are discussed. The time difference can be calculated either by taking the first peak of the signal as the arrival instant, or from the cross-correlation of the PD waveforms, or by applying the similarity function to the plots of the PD signals cumulative energy. Computation algorithms for determining the TDOA automatically are introduced so that possible bias from human interpretation is avoided. The presence of noise and its effect on the accuracy of the PD localization will also be presented. Experimental results show the first-peak method has higher accuracy than the two other methods. The application of signal denoising further improves the localization accuracy.

Application of data mining on partial discharge part I: predictive modelling classification

Innovations in computer technology have made possible continuous on-line monitoring of partial discharge (PD) activities. The power industry aims to assess the condition of power system equipment through on-line monitoring of PD activities. This involves long-term continuous data recording and it is very difficult to extract useful information from such a large amount of raw data, particularly if it is done manually. Instead, data mining can be applied in solving this problem. Data mining can be categorized into predictive modelling and descriptive modelling. In this paper, work was mainly focused on predictive data mining, which is classification of PD. The back propagation neural network (BPN), self-organizing map (SOM) and support vector machine (SVM) were used for classification and compared. Results indicate SVM is the best method in terms of classification accuracy and processing speed.

Influence of temperature and moisture content on frequency response analysis of transformer winding

Frequency Response Analysis (FRA) has been in use since the last decade as a sensitive method for detecting transformer internal defects. This study is focused on the influence of temperature and moisture migration on the FRA response of transformer winding. It also discusses the feasibility of FRA capability in moisture diffusion recognition in transformer paper insulation. To conduct this study, a single phase model transformer involving concentric LV and HV windings and a 20/0.4 kV, 1.6 MVA three-phase two windings transformer are taken as test objects. Experiments are carried out at different temperature and moisture conditions. FRA spectra are then recorded and analyzed. FRA spectra deviations as well as total capacitance variations due to the temperature and moisture changes in the test objects are calculated. Karl-Fischer Titration (KFT) is utilized to monitor the moisture migration within oil and paper insulations. Furthermore, a mathematical model is used to simulate one of the test object windings and verify the experimental result, and also clarify the main reason of FRA spectrum deviation in this circumstance. Finally, statistical indices in FRA evaluation are calculated to explore their capability in FRA spectrum interpretation once the moisture content of paper insulation is changed.

A hybrid transformer model for determination of partial discharge location in transformer winding

Partial discharges are well known as a source for insulation degradation in power transformers. A hybrid transformer model is introduced to simulate the transformer winding transient response. Transformer structural data is used to determine the hybrid model parameters. Calculations of the hybrid transient model parameters are based on the parameters of the lumped parameter equivalent transformer model and electromagnetic rules. Modern computation techniques and optimizations are employed beside this model for PD location using the multi conductor transmission line model and also to analyze its propagation aimed at achieving (i) more reliable simulation results (ii) less computational time (iii) accurate results for a wide range of frequency. The simulation results on a 66 kV, 25 MVA fully interleaved winding are presented. The measurement results on this winding are employed to validate this model

Transformer frequency response analysis: mathematical and practical approach to interpret mid-frequency oscillations

Frequency Response Analysis (FRA) has been utilized as an off-line diagnosis test since last decade to investigate mechanical integrity of transformer. FRA data typically reports as a spectrum in Bode diagram over determined frequency band. To evaluate FRA data, determined frequency band may be conveniently divided into three bands, namely low-, medium- and high- frequency bands. It is well-known low-frequency band dominated by core, mid-frequency region dominated by winding structure and high-frequency band influenced by measurement connection leads. This study has concentrated on midfrequency oscillations of transformer winding frequency response spectrum presented in Bode diagram. Mathematical approach using travelling wave theory is employed to explore frequency response trace behavior. Practical studies on a prepared glassy transformer as well as two 66 kV, 25 MVA continuous and interleaved disc windings have been performed to validate mathematical calculation. In addition, two 245 kV, 45 MVA and 66 MVA power transformers have been examined to study mid-frequency oscillations and compare the result with mathematical evaluation.

Descriptive data mining of partial discharge using decision tree with genetic algorithm

Partial discharge (PD) is a common phenomenon which occurs in insulation of high voltage equipments such as transformers and has a damaging effect to the insulation. In this paper, the application of descriptive data mining on PD occuring in insulating system is shown. Experiments were set up to create three basic types of PD: corona, surface discharges and internal discharges. Partial discharge data were analysed using phase resolved analysis and pulse height analysis. Descriptive data mining was applied on the collected data using decision tree with genetic algorithm (GA) to mine the rules/relationships which can be used to differentiate the PD. These extracted rules are useful as input to predictive data mining such as fuzzy logics.