Using Partial Discharge as Sample Signal Source to Identify Contamination Moisture Pattern in Power

Transformer Insulating Oil

G.F.C. Veloso, L.E. Borges da Silva, I. Noronha and G. Lambert Torres Federal University at Itajuba – Minas Gerais – Brazil {gveloso, leborges, ismael, germano}@unifei.edu.br

Abstract-The properly behavior of power transformers is highly dependent of the insulation health. So, the insulation condition assessment can provide information to a system of diagnosis and prognosis, allowing for planning interventions at the appropriate time to avoid unexpected interruptions. This paper presents a technique that can detect moisture contamination in the insulating oil using acoustic signals of partial discharge. In this work, a piezoelectric sensor is mounted in a reservoir with insulating oils with different levels of moisture. A device to simulate partial discharges is immersed in those oils. The detected signals are processed using Discrete Wavelet Transform to extract time-frequency characteristics which are processing using the Principal Component Analysis in order to identify patterns of water content in insulating oil. The results are presented and the methodology has shown a very good technique to be implemented as on line monitoring system.

I. INTRODUCTION

Nowadays, the development of predictive maintenance programs has allowed the optimization of industrial plants in order to promote not only the reduction of maintenance costs itself, but also improve productivity, quality and efficiency [1]. At the electric system, the power transformers are fundamental components and there reliability is a factor of vital importance for the operation of the system [2]. The predictive maintenance can play an important role in the economic profitability of the electric system, to improve performance and extend the life of power transformers, reducing the risks of unplanned outage in power systems, which it is known to generate large financial losses.

Among different kinds of stresses which a power transformer is submitted, the insulating oil can be considered one of the most important components to be monitored [3]. In addition to electrical insulation, the oil also provides cooling, stabilizes the aging of components and carries information about the condition of the equipment. The use of methods of condition assessment can provide information to a system of diagnosis and prognosis, allowing for planning interventions at the appropriate time to avoid unexpected interruptions [3].

The most common used oil condition assessment method is the chemical analysis, which provides indicative presence of contaminators substances in the oil related to different problems. Its use requires take oil samples from the transformer, and send it to a laboratory, which takes several days to weeks to provide some useful results [4]. Often, one wants to know only the information about the presence of one

or two critical substance, and not a full report. However, the tests can only be done by a laboratory using expensive equipments [5] and requiring some time.

One of the most abundant sources of information concerning insulation system can be the partial discharge signals. Partial discharges are tiny arcs occurring inside the insulation system of high voltage electrical equipments. They arise due to construction problems, wear, and aging or due to overloading [6], and their activity remain at acceptable levels for a long time [7]. For a power transformer, a minimum partial discharge activity can be detected immediately after its entry into operation [7]. When a partial discharge occurs, it releases energy in the form of acoustic wave that travels throughout the insulation system and hits the transformer wall, where can be detected using an appropriated piezoelectric sensor [8]. If the sensor is sufficiently sensitive, it is possible to monitor the partial discharge since the beginning of its activity until the end of the transformer life. The normal or accelerated aging of insulation changes the dynamic characteristics of the system composed by the metal structure, oil tank, paper and coil [9]. It will be show that such changes can be detected by analysis of an acoustic signal that passes through that system. So, the availability of acoustic signals from partial discharges can be a source of valuable information about insulation system.

The objective of this work is to show the possibility of detect the presence of moisture contamination in insulation oil by the analysis of the acoustic signals from partial discharges. Using Discrete Wavelet Transform, those signals can be decomposed in their frequency components varying in time. Then, these components, here called parameters, are examined with the Principal Component Analysis (PCA) in such way to find patterns related to water contamination. The same technique can be used to detect other changes in the insulation system, building the basis for a real time predictive maintenance system for power transformers.

II. SETUP AND MEASUREMENT PROCEDURE

The experimental setup consists in a reservoir to be filled with insulating oil, a device to produce electric discharges in order to simulate partial discharges (PD), and a piezoelectric sensor at the platform where the reservoir is placed (Fig.1). The sensor used is broadband type and has approximately flat

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frequency response up to 1MHz. An acquisition system of 2.5MSamples/sec of sampling rate is used to collect the acoustic signals from the discharges. The setup tries to emulate how acoustic signals, generated by partial discharges (PD), propagating through the oil can be detected and analyzed in order to extract information about the moisture content in the oil.

Fig. 1. The experimental setup. The experiment searches for characteristics or patterns

related to moisture inside the oil. In the first run two samples of oil were tested. One had high degree of moisture and the other had low degree of such a contamination. A certain number of discharges were produced inside these two samples and the acoustic signals acquired and stored for further analysis.

These two oil samples are the same type of oil, NYNAS NYTRO 4000X, but contaminated with different levels of moisture. The first one has 6.1ppm of water content and the second one has 39.1ppm, at 20 oC, both samples measured at the physical-chemical laboratory of Alstom Measurement Transformer Division at Itajuba, Brazil. It was produced 30 acoustic signals for each one. After the preprocessing the signals were analyzed using the Discrete Wavelet Transform to produce parameters characterizing the oils condition. These parameters are processed by Principal Component Analysis (PCA) in order to find hidden patterns related to known oil contamination.

A. Preprocessing

The acoustic signals preprocessing has two objectives: the first one is to synchronize the signals, taking only the essential samples, and the second takes care of the influence of the discharge intensity, which vary considerably. Each discharge occurs at different time instant and to guarantee alignment of time parameters synchronization is demanded. Also, the electrical discharges have different intensities and must be normalized in energy. It can be done calculating the energy of the signal using the expression (1), where x is the

signal sample of index i. Then, each sample must be divided by the square root of signal energy.

∑=

i

ixE )(2 (1)

Then, the total energy of the normalized signal will be

always the unit. For example, Fig.2 shows two acoustic signals before preprocessing and Fig.3 shows the same after the preprocessing.

Fig. 2. Two acoustic signals before preprocessing. Fig.3. The same two acoustic signals of the Fig.2 after preprocessing.

B. Discrete Wavelet Transform

The multi-resolution analysis can decompose the signal in frequency components keeping the time information. The decomposition of a signal into different resolutions can be accomplished with the Discrete Wavelet Transform. The importance of observe the signal at different detail levels relies on the fact that the signal characteristics can be distributed into such levels. By separating the levels, the characteristics of the wave, at each level, are also separated.

Discrete wavelet transform is a type of signal expansion [10]. In this case, the expanding set is made up of wavelet and scaling functions. The expression describing this can be written as:

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∑ ∑ ∑∞

−∞=

∞

=

∞

−∞=

⋅+⋅=k jj k

kjkjkjkj tdtctf0

00)()()( ,,,, ψϕ (2)

where cj0,k is the coefficient related to the scaling functions called approximation coefficient, dj,k is the coefficient related to the wavelet function called detail coefficient, φj0,k is the scaling functions’ expanding set, ψj,k is the wavelet functions’ expanding set, j is the scale index and k is the time index.

The coefficients in the Discrete Wavelet Transform are referenced using two variables covering the frequency and time content of the signals. These coefficients are organized into levels defined by the variable j and are related to the concept of scale. Scale is a parameter related to the frequency and can be thought as the inverse of it. The time information is represented by variation in k. Thus, the Discrete Wavelet Transform is the process to calculate the coefficients cj,k e dj,k.

When the signal is decomposed with the Discrete Wavelet Transform, it is broken into levels related to a particular frequency band. The number of levels is a parameter during this process. Therefore, the Discrete Wavelet Transform of a signal results in several levels of detail coefficients and one level of approximation coefficients that retain the lower frequency content. The frequency information is preserved at the levels of decomposition and the time information is preserved through the variation of the coefficients.

In this work, the information extracted by the decomposition with Discrete Wavelet Transform is used indirectly as parameters for signal characterization. The acoustic signal is divided in five time intervals and each one is decomposed in nine levels (9 details + 1 approximation) using the wavelet Daubechies 8 (db8). This wavelet was chosen because its good correlation with the acoustic signals waveforms and minimizes the effect of coefficients not related with the wave. Then a value of energy is calculated for each level to build a set of 50 parameters. The Fig.4 shows an example of a set of parameters. The Parseval's theorem, valid for the Discrete Wavelet Transform [10], guarantees the parameters reflect the energy distribution in frequency bands over time.

Fig. 4. Example of a parameters set from a discharge acoustic signal.

C. Principal Component Analysis

The Principal Component Analysis is a statistical technique usually applied to identify patterns contained in large data sets. Most of these patterns can not be easily perceived in a superficial analysis when it is hidden in large volume of

information. Additionally, it can sort the patterns by their contribution in the formation of the data set.

The PCA can be defined in terms of covariance of the data set, which is organized in a matrix form [11]. The covariance can be thought as a measure of how data dimension vary each other, in other word, it can be interpreted as how much one dimension is similar to the other. Another component in PCA performance is the concept of eigenvectors and eigenvalues. The important point here is that when eigenvectors are calculated from a covariance matrix, they are orthogonal to each other. The orthogonality is a measure of independence of data. So, finding eigenvectors from a covariance matrix of a data set can be interpreted as finding patterns in a data set. The eigenvalues are the contribution of the pattern in the formation of the data.

The data is organized in matrix form. The Discrete Wavelet Transform parameters are the columns and the signals are the lines. The covariance between each two lines is performed using the expression (3):

1

)()(),cov( 1

−

−⋅−=∑

=

n

YYXXYX

n

iii

(3)

where Xi is the parameter i from the line X, Yi is the parameter i from the line Y and n is the number of parameters in a line. The values of covariance are organized in a symmetric matrix, the covariance matrix, such as:

)],cov(|[ ,, YXccC YXYX

nxn == (4) After that, the eigenvectors and eigenvalues are calculated

from the covariance matrix. Each eigenvector is called a component and its contribution in the formation of the data is given by its associated eigenvalue. So, the components can be organized by its importance. Each eigenvector can be thought as a pattern found in the data.

Finally, to find the elements related to certain patterns the scores for each one has to be calculated. If the components are orthogonal vectors, representing patterns in the data, the scores are the projection of the original data in those vectors. These projections represent how much those element are related to a specific pattern indicated by these vectors. This is the interpretation of the PCA applied in the parameters used in this work.

III. RESULTS AND DISCUSSION

The parameters extracted from the 60 acoustic signals processed by PCA technique produce the data set 60x50 matrix. In this case, 50 patterns were found in the data, but the first 3 contribute with 97.8% of the information. In Fig.5, the first principal component (PC) with its scores is shown. The Fig.5 (top) presents the PC#1 and has two patterns. One is above zero and another below. The positive scores (bottom) are related to the pattern above the zero (Fig.5 top) and the

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negative scores with the value below zero are related to the pattern below zero (Fig.5 top). By the analysis of Fig.5 one can say the patterns presented are useless to identification of moisture in oil.

Fig. 5. First principal component and the scores for each signal set parameters.

Searching among other PCs, it was found a very interesting

pattern, showed in Fig.6. The third PC constitutes the pattern that separates the two samples of oils. The oil with 6.1ppm of moisture content has the negative score. Moreover, the oil with 39.1ppm of water content has almost all of its scores above zero. So, the parameters above zero (shown in Fig.6 top) can be related to a significant level of water contamination. This pattern can be used to detect moisture in insulating oil. In the other direction, the behavior of the parameters below zero (Fig.6 top) can be used to identifying oils with an acceptable level of water content.

It is important to notice the time variation in the parameters. Static parameters, that is, only frequency content of signals, was tested and do not provide necessary information about the moisture. When the time information was included, a pattern for moisture could be found. This effect can be explained by the acoustic dispersion of the oil. It seems that such property changes when the moisture contaminates the oil, so, a technique to detect that phenomenon is considering the evolution over time.

IV. CONCLUSION

The moisture contamination pattern in insulating oil can be identified applying Discrete Wavelet Transform and Principal Component Analysis in acoustic signals produced by partial discharges inside it. The experiment proved the viability of the technique and must be refined to include other types of oil. Moreover, other contaminants can also have their patterns identified in order to build a database for predictive maintenance. The technique developed to discover the contamination pattern can be used in on-line monitoring for oil diagnoses and prognoses not only for water content, but many other contaminants, like gases and particles.

Fig. 6. The third principal component and the scores for each signal set

parameters.

ACKNOWLEDGMENT

The authors would like to thank to FAPEMIG, Minas Gerais State research funding agency, CNPq, a Brazilian research funding agency, CAPES, in the form of research scholarships, supported this work, and Alstom subsidiary at Itajuba, Brazil.

REFERENCES [1] R. K. Mobley, An introduction to predictive maintenance, 2nd ed,

USA:Butterworth-Heinemann, 2002. [2] S.M. Strachan; S. Rudd; S.D.J. McArthur; M.D. Judd; S. Meijer; E.

Gulski, “Knowledge-based diagnosis of partial discharges in power transformers”, IEEE Transactions on Dielectrics and Electrical Insulation, vol 15, no. 1, pp. 259-268, February 2008.

[3] P.J. Baird; H. Herman; G.C. Stevens; P.N. Jarman, "Non-destructive measurement of the degradation of transformer insulating paper", IEEE Transactions on Dieletrics and Electrical Insulation, vol 13, no. 1, pp. 309-318, February 2006.

[4] T.K. Saha, "Review of modern diagnostic techniques for assessing insulation condition in aged transformers", IEEE Transactions on Dielectrics and Electrical Insulation, vol. 10, no. 5, pp. 903-917, October 2003.

[5] T.K. Saha; P. Purkait, "Understanding the impacts of moisture and thermal aging of transfomer's by dielectric response and molecular weight measurements", IEEE Transactions on Dielectrics and Electrical Insulation, vol. 15, no. 2, pp. 568-582, April 2008.

[6] O. Hassan, A.A. Shyegani, H. Borsi, E. Gockenbach, E.M. Abu-Elzahab, M.I. Gilany, “Detection of oil-pressboard insulation aging with dielectric spectroscopy in time and frequency domain measurements”, Proceeding of 2004 International Conference on Solid Dielectrics, Toulouse, France, July, 2004.

[7] D.A. Nattrass, “Partial discharge XVII: the early history of partial discharge research”, IEEE Electrical Insulation Magazine, vol. 9, no. 4, pp. 27-31, July/August 1993.

[8] L.E. Lundgaard, “Partial discharge XIV: acoustic partial discharge detection – practical application”, IEEE Electrical Insulation Magazine, vol. 8, no. 5, pp. 34-43, September/October 1995.

[9] D. Linhjell; L. Lundgaard; U. Gäfvert, "Dielectric response of mineral oil impregnated cellulose and the impact of aging", IEEE Transactions on Dielectrics and Electrical Insulation, vol. 14, no. 1, pp. 156-169, February 2009.

[10] C.S. Burrus, R. A. Gopinath, H. Guo, Introduction to wavelets and wavelet transforms. New Jersey: Prentice Hall, 1998.

[11] I.T. Jolliffe, Principal component analysis. New York: Springer, 2002.

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