F0244030037

The International Journal Of Engineering And Science (IJES) ||Volume||2 ||Issue|| 4 ||Pages|| 30-37||2013|| ISSN(e): 2319 – 1813 ISSN(p): 2319 – 1805

Recent Transformer Testing Methods Rajan Barnwal1, Pulkit Nahata1 1

Department of Electrical And Electronics Engineering, VIT University, Vellore, India

----------------------------------------------------Abstract---------------------------------------------------------Transformers play a pivotal role in the modern day energy distribution systems. Owing to their supreme importance in the power systems and their high cost, for a continued service, regular scheduled testing of the transformers isinevitable. This project aims at identifying few of the recent transformer testing methods and their innumerable significance in smooth transformer operation and henceforth avoiding faults in the transformer. Moisture content techniques for testing the paper insulation and modern SFRA tests are highlighted here.

Keywords: SFRA; Moisture content; KFT; Polarization --------------------------------------------------------------------------------------------------------------------------------------Date Of Submission: 18 April 2013 Date Of Publication: 30,April.2013 ---------------------------------------------------------------------------------------------------------------------------------------

I.

INTRODUCTION

Transformers play a critical role in delivering power to distribution equipment for homes, commercial establishments and industrial facilities. Sudden failure of a transformer can have many serious repercussions leading to loss of power for a few minutes to hours. There are instances an outage could last for weeks because of system configuration, such as a simple radial system or the transformer was the single point failure. In industrial complexes an outage lasting a few minutes could lead to loss opportunities as well as millions of dollars in lost production and start-up costs. This makes it essential to check that the transformer is in robust condition.

The knowledge of the actual status of transformer e.g. turns ratio, insulation behaviour, load tap changer performance, temperature, load loss, etc. is necessary in order to evaluate the service performance concerning reliability, availability and safety. So, the routine tests of transformers have an important role to reach these goals. These tests should be made on a transformer to ensure that it is ready for service, and to provide a basis for comparison with future maintenance tests. The paper discusses various recent trends in transformers.

Typically, the variability and frequency of routine tests to be done, depends on the equipment available and importance of particular transformer in use.

II.

RECENT MOISTURE CONTENT RECOGNITION TECHNIQUES – A COMPARITIVE STUDY

Moisture content in the paper insulation expedites the ageing process of the paper insulation of the transformers that may result in abrupt temperature changes. As a result, the production of bubbles and internal partial discharges within the transformers may be observed. Thus, the determination of the moisture content within the insulation system before its excitation and also during the normal operation is inevitable. In this regard, several chemical tests on the oil and improved methods to extract the moisture from the insulation system have been developed over the years.The subsequent sections discusses various electrical and nonelectrical chemical based tests to recognize the water content in the Oil-Impregnated Paper A. Non Electrical Chemical Tests Karl-Fisher Titration KFT is generally an outdated test these days but it holds considerable importance in identifying the moisture content. In KFT, the oil is sampled from the energized transformer. KFT instrument is employed then to obtain the moisture content of the sampled oil. The equilibrium curves are then used to determine the percentage water content. It is important to focus upon its drawbacks which produce considerable errors and hence make KFT skeptical. These are: [1] [2] [3] [4]

Errors due to non-standard sampling.

Errors due to the difference in temperatures at the time of sampling and at the time of testing.

Errors produced in the preparation of the titration samples.

Pre-existing errors in the equilibrium curve referred to identify the result.

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[5] Typically, the winding is wet at the bottom and dry at the top. Thus, oil sample does not represent average humidity in the transformer.

[6] Finally, the most tragic problem is caused by having bound water or related water inside the oil, which is different from the main water content of the oil. The KFT method is inept of separating various types of water contents in the oil sample.

[7] The results yielded by KFT does not come in with acute precision.

Dean and Stark Procedure The Dean and Stark procedure can be applied to a diverse range of samples. This method has varied use like measuring the water content in the petroleum oil or determining the transformer paper humidity. The percentage water content can directly be recognized through this method.The sample of paper insulation is taken and mixed with a solvent (generally a toluene/xylene mix) and is refluxed under a special receiver that is illustrated below.

There are two designs of receivers generally, one for solvents that are heavier than water, and the more common one for solvents that are lighter than water. The water and solvent are refluxed, and as they condense the two phases separate as they run into the receiver. Dean and Stark technique has an added advantage of separating the unwanted water from the transformer oil. This can be exploited to remove unwanted water from the reactors.

Practically, a sample from the transformer paper insulation is taken. Then, this sample is cut into maximum 10Ă—10 cm scraps. The flask is filled up to 2/3 with solvent and scraped papers are added to solvent afterwards.

The flask is heated up for 2 hours to condense water vapour and finally water drops are gathered in receiver.

To explore % moisture content in transformer paper insulation can be calculated as (1).

Where Y represents mass of accumulated water, P is massof paper scraps and K represents mass coefficient of oil. Generally, K=0.7 and K=1 are regarded as impregnated and un-impregnated oil paper insulation respectively.

B. Electrical Tests The electrical tests used for finding the water content in the transformer oil do not involve the use of oil samples. The most widely accepted tests are Polarization and Depolarization Current (PDC) and Frequency Domain Spectroscopy (FDS).

Polarization and Depolarization Current The principle behind this test is that for a given sufficient polarization time, the depolarizing current is a function dielectric reaction. Whenever a DC voltage is applied across a sample insulation material, the free charges and other types of limited charges interact with the electric field of the voltage applied and tend to move the electric charges in the insulation material constituting an electric current. This is Polarization. This current falls and attains a fixed value over time. After this, if the applied voltage is removed and the two ends of the insulation materials are shortened. The depolarizing current decreases and eventually reaches zero value.To perform the PDC method, the two ends of the insulation material is connected to a DC source and the polarization current is measured precisely using a micro-ampere meter. Then, the two ends of the insulation material are shortened to record depolarization current. The function of DC charge is used to characterize the influence of conductivity on the dielectric response of the insulation material.

Frequency Domain Spectroscopy Another approach towards analysing the polarization process can be done in the frequency domain rather than in the time domain by applying the AC sinusoidal voltage U(ω) is applied.Simply, complex impedance and dissipation factor (tanδ) are measured. In this method, imaginary and real parts of the capacity of an insulation system are estimated over a frequency range and humidity amount corresponding to this capacity

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Recent Transformer Testing... is determined using diagram comparison method. If the effective relative dielectric susceptibility is defined as (2):

C. COMPARISON OF FDS, KFT, DEAN AND STARK PROCEDURE FDS, KFT, Dean and Stark Procedure were performed simultaneously on three identical brand new distribution transformers under identical conditions to compare the sensitivity and accuracy of the methods based upon the results obtained. A quantitative analysis of the three tests has been presented below. According to the test results, it can be easily concluded that KFT displays major drawbacks as compared to the other two methods.

Determined water contents of the papers are shown below

III.

SFRA TEST

Sweep Frequency Response Analysis (SFRA) is a powerful and sensitive method for detecting the changes in electrical characteristics of a power transformer. The electrical transfer functions are used to evaluate the mechanical integrity of the core and the windings internal to the power transformer by varying the frequency over a wide range. The changes inside the transformer can take place because of the various types of electrical and mechanical stresses. This test can be performed as a standalone or along with a combination of other tests.

Suitable instrument with high accuracy and designed for field use is selected to ensure accurate and reproducible measurements. The laboratory instrumentation most of the time is designed to be used indoor and normally require at least 30 min warm-up period.

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3.1 SFRA frequency range The requested frequency range for SFRA varies between different standards and recommendation; the starting frequency ranges from 10 Hz, up to 1 kHz and the upper frequency ranges from 1 MHz, 1-2MHz, 2-5 MHz up to 10 MHz. The highest frequencies used for interpretation is limited, also with good cable practice, by the large geometrical dimensions of high voltage bushings. Therefore, the highest frequencies used for interpretation need to be limited to 1-2 MHz for high voltage winding on large transformers here as up to 5 MHz can be used for small transformers. In all our study we have not found any reference for successful use of SFRA above 10 MHz 3.2 Accuracy in the measurement range The dynamic range of the response usually covers the range from -100 dB up to 20 dB. Normally most recommendations require at least ±1dB accuracy. Many instruments are available with an accuracy of ±1dB but the accuracy decreases as the signal gain decreases i.e. reaching the bottom of measurement range of -100dB. Measurements using different instruments on laboratory samples as well as tests in the field have shown that as a simple role of thumb can be used: in order to fulfil ±1dB accuracy down to a specified level, e.g. -100dB, the dynamic range/floor of noise or cross-talk need to be at least 20 dB lower, e.g. below -120 dB (20 dB is a factor of ten, ±1dB is ±13%) Fig. 4 shows measurements of two different configurations, using two different instruments A and C; A with accuracy ±0.5dB down to 100 dB and C with an accuracy/repeatability of ±1dB down do -70dB/-80dB. Deviations between the instruments are clear below -75 dB, in the measurement range where only instrument A has accuracy specified.

Figure 4. Measurement From The Neutral Bushing To The Capacitive Tap Of Phase Bushing (Black And Red) and A Measurement Of A HV winding Of Another Transformer Using Instrument A (Red And Green) And C(Black And Blue)

IV.

SETTLING TIME AND FILTERING OF SIGNALS

Most SFRA instruments measure one frequency at the time. After changing the frequency, the instrument shall allow some settling time, as example at least one cycle or 0.01ms. Next step is filtering unwanted signals, and in case of low frequency open measurement, filter the fundamental frequency from the non-linear signal response. It is common that a requirement of an IF bandwidth of less than 10% of active frequency is used, that is equal to 12 cycles of integration using sine correlation technique.

V.

APPLIED VOLTAGE LEVEL

Most SFRA instruments uses a generator voltage level in the range 5.66 (2V RMS) to 24 V peak to peak. Since the output has 50 Ohm impedance and that the applied signal is measured using 50 Ohm input impedance, the applied voltage ranges from 2.83 to 12 V peak to peak without any external load. Measurements by the authors and by others show that the applied voltage in this range may affect the results at low frequencies in configurations where the core is involved, i.e. in measurement of a winding while other windings are floating.

See Fig. 5. This effect is expected to be more pronounced for low voltage windings, and the authors have only seen small influence on high voltage transformer windings.

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Figure 5. Measurements Of Same 11KV Winding Using 3 Different Voltage Levels 2.83V (Green), 5V (Grey) And 10V (Red) Peak To Peak. No Difference Is Observed At Higher Frequencies.

VI.

A NEW TECHNIQUE OF SFRA

SFRA was first proposed by Dick and Erven, and since then, it has been widely used by many industries and manufacturers. The SFRA technique consists of injecting a sinusoidal signal with sweeping frequency (from 20 Hz to 2 MHz) and with low and constant voltage into one of the transformer’s terminals and measuring the response at the other terminal. Then, the Fourier transform (TF) is used to evaluate the amplitude and phase of the response signal in the frequency domain. These frequency amplitudes and phases are the typical SFRA quantities used in any diagnosis.

It is a well-known fact that the FT is a 1-dimensinal mapping from the time domain (i.e. s(t)) into the frequency domain (i.e. S(f)). Two different signals in the time domain may present the same plot in the frequency domain. This makes the conventional SFRA more susceptible to noise and interference. The proposed technique will avoid misdiagnosing of a healthy transformer. To mitigate this problem, we propose to use a time-frequency distribution (TFD) instead of the FT (SFRA). The TFD is a 2dimensional mapping from the time domain to a joint time frequency domain. In the presence of white noise, with a given power, the noise gets spread all over the 2D plane and, consequently, will not affect the representation as much as it does for the 1D SFRA plot.

6.1 Time-frequency representation There are two classical representations for signal analysis. One is based on the temporal representation of the signal, s(t), and the other is based on its spectral representation, defined by the Fourier transform, S(f).

These two representations and their related classical methods such as autocorrelation and/or power spectrum proved to be powerful in the analysis of stationary signals. However, when the signal is non-stationary, such as SFRA signals, these methods fail to fully characterize it. To illustrate this point, let us consider the SFRA signal, which is just a linearly frequency modulated (FM) signal. The signal spectrum, displayed in Figure 6(a), gives no indication on how the frequency of the signal is changing with magnitude.

The SFRA kit provides us with the frequency representation of the signal under consideration. Using the inverse FT, we can easily recover the time-domain representation of the SFRA signal. Subsequently, this time-domain data is used to generate the joint time-frequency representation of the SFRA signal. Also note that, in the sequel, we need to evaluate cross correlation coefficients of TFDs. And since TFDs are numerically represented by 2D matrices, then, we will use a Cross-correlation coefficient suitable for 2D matrices. The expression of the selected cross-correlation coefficient is given by: (5) Where

and

are the means of corresponding matrices. The TFDs of two signals are shown in figure 6.

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Fig. 6 (a)

Fig. 6 (b)

Fig. 7

VII.

EFFECT OF NOISE

The top plot of Figure 8 displays an SFRA FT representation of a signal obtained from a healthy transformer. To this signal, a white Gaussian noise with Signal-to Noise Ratio (SNR) of 10 is added. The FT of the noisy signal is displayed in the bottom plot of Figure 6. As we can see, the bottom plot may be misinterpreted as the result of a signal generated from an unhealthy transformer. Also, the 1D cross correlation coefficient obtained using the data of the two curves of Figure 6 is found to be equal to 0.4629, which is very small. Thus, even SFRA techniques based on cross-correlation coefficient would suggest that the transformer is unhealthy. However, if we consider exactly the same signal and its same corresponding noisy counterpart, and use the TFDs in the analysis, we obtain the plots displayed in 9. As we can see, there is no significant difference between the two plots and, consequently, it is less probable to have the same misdiagnosis issue as we saw in the use of the classical SFRA based technique. Moreover, this qualitative is further reinforced by the result obtained using the 2D cross correlation coefficient. Indeed, we find that the coefficient obtained using the above two TFDs is equal to 0.9448, which is much higher than the 0.4629 obtained above.

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In conclusion, we can say that the above qualitative as well as quantitative analyses strongly prove the superiority and robustness of the proposed time-frequency based technique, compared to current SFRA techniques.

Fig. 8

Fig. 9

VIII.

CONCLUSIONS

In the Moisture content recognition tests it can be easily seen according to the test results, that KFT displays major drawbacks as compared to the other two methods. Meanwhile amongst the SFRA tests, the new SFRA gives a distinctive advantage over the earlier technique. since the time-frequency representation of each signal is unique and is less susceptible to noise, it is more robust. The quantitative measure (based on a 2D cross-correlation coefficient) validates and proves the superiority of the proposed method over existing SFRA ones.

IX.

ACKNOWLEDGEMENT

This work was supported in part by VIT University under the guidance of Prof. Sudha R (Assistant Professor, VIT University)

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REFERENCES [1]

[2]

[3]

[5] [6] [7]

M. Bagheri, Mohammad S. Naderi, T. R. Blackburn and B.T. Phung,.“Practical challenges in online transformer winding deformation diagnostics.” Int.’l. Conf. Electric Power and Energy Conversion Syst.

(EPECS.’11) ,Sharjah, UAE, pp. 1-6, 2011.

M. Bagheri, Mohammad S. Naderi, T. Blackburn, T. Phung. “FRA vs. short circuit impedance measurement in detection of mechanical defects within large power transformer.”IEEEInt.’l.Sympos.Electr.Insul.

(ISEI.’12), Puerto Rico, USA, pp. 301-305, 2012.

E.P. Dick and C.C. Erven, “Transformer Diagnostic Testing byFrequency Response Analysis”, IEEE Transactions PAS-97, No. 6, pp 2144-2153, 1978. [4]B. Barkat and H. Al-Marzouqi,”A Correlation based approach to interference suppression from phase resolved partial discharge Patterns”, 10th IEEE International Conference on Solid Dielectrics, Potsdam, Germany, July 04-09, 2010.

S. A. Ryder, “Methods for Comparing Frequency Response Analysis Measurements”. IEEE international symposium on electric insulation, Boston, MA USA, April 7-10, 2002 S. Kumar, M. Kumar, and S. Chauhan, “Diagnosis of power transformers through sweep frequency response analysis and comparison methods”.National system conference, NSC 2008. December 17-19, 2008 D. K .Xu, C. Z. Fu, Y. M. Li. "Application of Artificial Neural Network to the Detection of the Transformer Winding Deformation", paper presented at International Symposium on High Voltage Engineering, London, 1999

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