____________________________ A. Abu-Siada, Lai Sin Pin and S. Islam are with the
Department of electrical and Computer Engineering, Curtin University of Technology, WA, Australia (e.mail: [email protected] )
Remnant Life Estimation of Power Transformer using Oil UV-Vis Spectral Response
A.Abu-Siada, Member, IEEE, Lai Sin Pin, Student Member, IEEE and Syed Islam, Senior member, IEEE
,
Abstract--UV-Spectrophotometry is a non-intrusive test used to determine the transformer’s integrity. Accurate interpretation of uv-spectrophotometry provides reasonable information on the health of power transformer that can be used to plan cost effective maintenance, retirement, relocation, and operational criteria. The Ultraviolet-to-Visible (UV-Vis) spectral response of transformer oil can be measured instantly with relatively cheap equipment and does not need an expert person to conduct the test. Results show that there is a good correlation between oil spectral response and its furan contents; consequently correlation between transformer aging and UV trend can be easily established. The paper introduces a novel fuzzy logic approach to estimate transformer aging using oil UV-Vis spectral response.
Index Terms— Transformer Diagnosis, Furan analysis,
Ultraviolet to Visible Spectroscopy, Gene Expression Programming, Fuzzy Logic.
I. INTRODUCTION
OWER transformers are a vital link in a power system. Monitoring and diagnostic techniques are essential to
decrease maintenance and improve reliability of the equipment. Currently there are several of chemical and electrical diagnostic techniques applied for power transformers[1]. The electrical windings in a power transformer consist of paper insulation immersed in insulating oil, hence transformer oil and paper insulation are essential sources to detect incipient faults, fast developing faults, insulation trending and generally reflects the health condition of the transformer. Generally, transformer life is equal to the insulation life. The mechanical forces on cellulose paper in a power transformer which is mostly attributed to transportation, electromagnetic forces and inrush current can reduce clamping pressure. Thus the aging of paper insulation determines the ultimate life of the transformer. Paper insulation is composed of approximately 90% of cellulose, 6-7% hemi-cellulose and 3-4% of lignin. Due to electrical and thermal stresses, oil and cellulose decomposition occurs evolving gases that will decrease the heat dissipation capability and the dielectric strength of the oil[2]. When degradation of paper insulation occurs, the cellulose molecular chains get shorter and chemical products such as furanic derivatives are produced and dissolve in the oil. Furans are the major degradation of paper in oil and the 2-furaldehyde in oil is the most prominent component of paper decomposition[3-5]. The increase in furans
concentration in the oil corresponds to the decrease in the tensile strength and the degree of polymerization (DP) of the paper. Furan level in a transformer can be correlated with paper DP, and therefore an in-service assessment of the mechanical strength of the paper insulation can be made. De Pablo reported the following relation between furfural and Degree of polymerization based on viscosity DPV [2, 6]
FALDPV 288.8
7100
+= (1)
where 2FAL is the furfural concentration in mg/kg of oil.
Oil samples can be easily collected from operating transformers, however, it is impractical to acquire paper samples from servicing transformers; then the concentration of 2-furfuraldehyde in the oil can be used as a good indicator of paper deterioration. It has been estimated that new paper, under normal running conditions will generate furfural at the rate of 1.7 ng/g of paper/h. The rate of production increases with increasing degree of degradation and a total yield of 0.5 mg of furfural/g of paper is expected in 100000 h of running (15-20 years)[5, 7-9]. The correlation between 2-furaldehyde and DP with respect to the solid insulation extent of damage is given in table I depicting insulation paper dielectric and mechanical properties[9-11].
TABLE I DP AND 2-FURFURALDEHYDE (2-FAL) CORRELATION
2-FAL (ppm) DP Value Significance
0-0.1 1200-700 Healthy Insulation
0.1-1.0 700-450 Moderate Deterioration
1-10 450-250 Extensive Deterioration
> 10 < 250 End of Life Criteria
When DP test reveals a value of 250 or less, the paper is considered to have lost all its mechanical strength, and the transformer has reached its end of life. Furans concentration is measured by High-Performance Liquid Chromatography (HPLC) or Gas Chromatography-Mass Spectrometry (GC/MS) based on American Society for Testing and Materials (ASTM D5837, Standard Specifications for Mineral Insulating Oil in Electrical Apparatus)[12]. Both a methods provide accurate and reliable results in detecting the concentration of all furan derivatives; 2- furfural (2-FAL), 2-Furfurol (2-FOL), 5-Hyroxy methyl-2-furfural (5-HMF), 5-Methyl-2-furfural (5-MEF), and 2-Acetylfuran (2-ACF). However, these two methods need very
expensive equipments and take long time to get the results for one oil sample which should be chemically treated before conducting the test in addition it requires an expert person to perform the test and to interpret its results. This paper presents a novel approach for estimating the remnant life of paper insulation and hence power transformer using the spectral response of transformer oil. Results show that there is a good correlation between furan concentration level in oil transformer and its spectral response, hence spectral response of transformer oil can be used as a good parameter in estimating transformer life. The Ultraviolet-to-Visible (UV-Vis) spectral response of transformer oil can be measured instantly with relatively cheap equipment and does not need an expert person to conduct the test. Moreover, no chemical treatment is required for the oil samples.
II. LABORATORY AGED OIL
The study has been performed on in-service as well as laboratory aged transformer oil. Laboratory aged insulating oil is prepared by utilizing the heating process available in IEC 61125 (International Electro-technical Commission)[13, 14] . Section of new craft paper (20mmx280mm) was cut and wrapped around copper strips (3mmx10mm) then it was impregnated in 25ml of new transformer oil (shell Diala B). All samples were heated up to 100°C in a thermostatically-controlled aluminum alloy block heater for 7 days. Oxygen flow at a rate of 1 l/hr was supplied into each dry tube to further accelerate the aging process.
III. FURAN ANALYSIS
All samples were prepared in accordance to standard ASTM D 5837 and tested using GC/MS system for furan derivatives identification and quantification. Table II shows furan derivative concentration in particle per million (ppm) for different oil samples using GC/MS. Results show that the 2-furaldehyde (2-FAL) is the most prominent component of paper decomposition. Therefore, the level of 2-furaldehyde in transformer oil can be used as an indicator for paper deterioration.
TABLE II
FURAN CONCENTRATION RESULT BY GC/MS
Test Sample 2-FAL 2-FOL 2-ACF 5-MEF 5-HMF
New Oil <0.01 <0.01 <0.01 <0.01 <0.01
Sample 1 3.1 <0.01 0.01 0.01 <0.01
Sample 2 5.1 <0.01 0.02 0.01 <0.01
Sample 3 10.0 <0.01 0.03 0.03 <0.01
Sample 4 15.0 0.01 0.05 0.05 <0.01
IV. UV-VIS SPECTRAL RESPONSE
UV-Spectrophotometry is a non-intrusive test used to determine the transformer’s integrity. UV-Spectrophotometry is an accurate and sensitive method to analyze impurities in the transformer oil using light absorbing properties of a sample. Light transmitted through the oil sample containing various contaminations is decreased by that fraction being absorbed and is detected as a function of wavelength. A spectrophotometer measures the transmission, absorption or reflection of the light spectrum for a given wavelength. Absorption spectroscopy
provides a measure of how much light is absorbed by the oil sample which can be calculated as[15, 16]
⎟⎟⎠
⎞⎜⎜⎝
⎛
−−
−=λλ
λλλ DR
DSA log (2)
Where λA is the absorbance, S is the sample intensity at wavelengthλ , D is the dark intensity at wavelengthλ , R is the reference intensity at wavelengthλ .
Same oil samples used for furan concentration measurement using GC/MS were tested using a laboratory grade spectrophotometer for absorbance spectrophotometry at room temperature 20ºC. The experiment procedure was set up in reference to ASTM E275[17]. Figure 1 shows the lab set up for measuring spectral response for one oil sample.
Fig. 1. Lab set up for measuring the spectral response of transformer oil
Figure 2 shows the spectral response (absorbance) for different oil samples with different furan concentration.
Fig. 2. UV/Vis Spectrum (Absorbance) for different oil samples with
different furan concentration
It can be shown from Figure 2 that the new oil exhibits its characteristics between 200 and 350nm uv-spectrum with maximum absorbance at 250nm wavelength. However, in-service and laboratory aged oil samples exhibit their respective characteristics in the range of 200 and 470nm wavelength uv-spectrum. Results show that absorbance as well as bandwidth for maximum absorbance increases by a significant and easily observable margin with oil deterioration and contaminations which are reflected by the furan concentration level in the oil. UV spectrum shows considerable noise for contaminated oil which can be attributed to the variety of contaminations
including very high carbon and water content in the oil. Figure 2 shows a good correlation between the furan concentration in transformer oil and its spectral response. To prove this correlation in more details, Figure 3 shows the relationship between spectral response parameters (maximum absorbance and bandwidth wavelength) of transformer oil and its furan concentration level. Figure 3 shows that the more furan concentration the more absorbance and more wavelength. Consequently, the spectral response parameters can be used as an alternative method to GC/MS to determine the furan concentration in transformer oil.
Fig. 3. Correlation between furan concentration in transformer oil and its
spectrum response parameters (wavelength and peak absorbance) Figure 3 shows that either the two parameters of the
transformer oil spectral response (wavelength and peak absorbance) or one of them is sufficient to determine the furan concentration level in the oil. However, due to the high noise in the contaminated oil, peak absorbance for different oil samples may overlap. The zero crossing point (wavelength) for each sample is unique and there is no overlapping of this parameter for different oil samples. Consequently, zero crossing points of spectral response can be used to identify the furan concentration level in transformer oil. Table III shows the spectral response parameters and its corresponding furan concentration level for each oil sample.
TABLE III
SPECTRAL RESPONSE PARAMETERS FOR DIFFERENT FURAN LEVELS
Comparing the results obtained in table III by those limits in
table I, the transformer life estimation can be obtained from the bandwidth of the transformer oil spectral response as shown in table IV.
TABLE IV
DP AND SPECTRAL RESPONSE CORRELATION
Bandwidth (nm) DP Value Significance
300-350 1200-700 Healthy Insulation
350-365 700-450 Moderate Deterioration
365-445 450-250 Extensive Deterioration
> 445 < 250 End of Life Criteria
V. FUZZY LOGIC MODEL
In this section, a novel fuzzy logic model is developed to estimate the remnant life of power transformer using oil UV spectral response. Due to the fact that spectral response of transformer oil depends on ambient temperature, UV-Vis spectral response was conducted for the same samples at different room temperature (25ºC). The maximum variation in wavelength/maximum absorbance was found to be less than ±3%. Based on these results, a fuzzy model is developed to estimate the remnant transformer life using its spectral response parameters. Figure 4 shows the fuzzy model to estimate the transformer remnant life where variables a and b represent the spectral response bandwidth and peak absorbance respectively, as an input data to the fuzzy model and t is the step time for the fuzzy model simulation. Membership functions (MF) for input variables are established based on the variation of spectral response results at different room temperatures as shown in Figures 5 and 6. The membership functions for the output variables (remnant life) are considered on the scale 50 to 0 (new to end of life) as shown in Figure 7.
Fig. 4. Fuzzy Model for Transformer Remnant life using Spectral Response
Fig. 5. Input Variable MF – Band Width
Fig. 6. Input Variable MF – Maximum Absorbance
Fig. 7. Output Variable MF – Remnant Life
Based on the experimental results, a set of fuzzy rules relates the input variables to the output are developed as shown in Figure 8. The expected remnant life using fuzzy logic model is tested with bandwidth (383 nm) and maximum absorbance (1.8). The model result is 1.27 years which is end of life criteria. The expected remnant life estimation assessment suggests immediate removal of the transformer for detailed investigation and asset management decision. The transformer expected remnant life estimation for any set of spectral response parameters (bandwidth and maximum absorbance) can also evaluated from the model’s surface graph shown in Figure 9.
Fig. 8. Fuzzy Rules - Spectrum-Remnant Life Model
Fig. 9. Surface Graph- Spectrum-Remnant Life
VI. CONCLUSION
The paper introduces a novel fuzzy logic approach to estimate the transformer expected remnant life via measuring oil spectral response. Experimental results show a good correlation between oil spectral response and its furan contents; consequently correlation between transformer aging and UV trend can be easily established. The current method to measure furan concentration in transformer oil is using very expensive and time consuming equipments which need a trained expert to conduct the test and to interpret the results. The paper introduces a novel technique for detecting furan concentration in transformer oil through measuring its spectral response. The Ultraviolet-to-Visible spectral response of transformer oil can be measured instantly using relatively cheap equipment with no need to an expert person to perform the test. A fuzzy logic model has been developed to estimate the transformer expected remnant life using the spectral response parameters (bandwidth and maximum absorbance) of transformer oil. Results show that oil spectral response can be used to estimate the furan concentration effectively and hence the transformer expected remnant life.
VII. REFERENCES [1] T. K. Saha, "Review of Modern Diagnostic techniques for Assessing
Insulation Condition Aged Transformers," IEEE Transaction on Dielectrics and Electrical Insulation, vol. 10, pp. 903-917, 2003.
[2] M. Arshad, "Remnant Life Estimation Model Using Fuzzy Logic for Power Transformer Asset Management," PhD thesis, Curtin University of Technology, 2005.
[3] A. M. Emsley, X. Xiao, R. J. Heywood, and M. Ali, "Degradation of cellulosic insulation in power transformers. Part 2: formation of furan products in insulating oil," Science, Measurement and Technology, IEE Proceedings -, vol. 147, pp. 110-114, 2000.
[4] P. Verma, M. Roy, R. K. Tiwari, and S. Chandra, "Generation of furanic compounds in transformer oil under accelerated thermal and electrical stress," in Electrical Insulation Conference and Electrical Manufacturing Expo, 2005. Proceedings, 2005, pp. 112-116.
[5] R. D. Stebbins, D. S. Myers, and A. B. Shkolnik, "Furanic compounds in dielectric liquid samples: review and update of diagnostic interpretation and estimation of insulation ageing," in Properties and Applications of Dielectric Materials, 2003. Proceedings of the 7th International Conference on, 2003, pp. 921-926 vol.3.
[6] I. L. Hosier, A. S. Vaughan, S. J. Sutton, and F. J. Davis, "Chemical, Physical and Electrical Properties of Aged Dodecylbenzene: Thermal Ageing of Mixed Isomers in Air," Dielectrics and Electrical Insulation, IEEE Transactions on [see also Electrical Insulation, IEEE Transactions on], vol. 14, pp. 1113-1124, 2007.
[7] A. J. Kachler and I. Hohlein, "Aging of cellulose at transformer service temperatures. Part 1: Influence of type of oil and air on the degree of polymerization of pressboard, dissolved gases, and furanic compounds in oil," Electrical Insulation Magazine, IEEE, vol. 21, pp. 15-21, 2005.
[8] J. M. K. MacAlpine and C. H. Zhang, "Observations from measurements of the furfural content of oil samples from transmission transformers," in Advances in Power System Control, Operation and Management, 2000. APSCOM-00. 2000 International Conference on, 2000, pp. 317-321 vol.2.
[9] Y. Shang, L. Yang, Z. J. Guo, and Z. Yan, "Assessing aging of large transformers by furfural investigation," in Solid Dielectrics, 2001. ICSD '01. Proceedings of the 2001 IEEE 7th International Conference on, 2001, pp. 272-274.
[10] M. Wang, A. J. Vandermaar, and K. D. Srivastava, "Review of condition assessment of power transformers in service," Electrical Insulation Magazine, IEEE, vol. 18, pp. 12-25, 2002.
[11] C. H. Zhang and J. M. K. Macalpine, "Furfural Concentration in Transformer Oil as an Indicator of Paper Ageing, Part 1: A Review," in Power Systems Conference and Exposition, 2006. PSCE '06. 2006 IEEE PES, 2006, pp. 1088-1091.
[12] ASTM, "Standard Test Method for Furanic Compounds in Electrical Insulating Liquids by High-Performance Liquid Chromatography (HPLC)," D5837-05, 2005.
[14] ITC, "Unused Hydrocarbon-Based Insulating Liquids-Test Methods for Evaluating The Oxidation Stability " IEC61125, 1992.
[15] J. A. Palmer, W. Xianghui, R. A. Shoureshi, A. Mander, D. Torgerson, and C. Rich, "Effect of aging on the spectral response of transformer oil," in Electrical Insulation, 2000. Conference Record of the 2000 IEEE International Symposium on, 2000, pp. 460-464.
[16] S. P. Lai, A. A. Siada, S. M. Islam, and G. Lenco, "Correlation between UV-Vis spectral response and furan measurement of transformer oil," in Condition Monitoring and Diagnosis, 2008. CMD 2008. International Conference on, 2008, pp. 659-662.
[17] ASTM, "Standard Practice for Describing and Measuring Performance of Ultraviolet, Visible, and Near-Infrared Spectrophotometers,," ASTM E275-01, vol. 03.06, pp. 72-81, 2001.
VIII. BIOGRAPHIES
Ahmed Abu-Siada received the B.Sc. and M.Sc. degree from Ain Shams University, Egypt and the PhD degree from Curtin University of Technology, Perth, Australia, All in Electrical Engineering. Currently, he is a lecturer in the Department of Electrical and Computer Engineering at Curtin University of Technology. His research interests include power System Stability and Control, Power Electronics, Power Quality, Condition Monitoring, Energy Technology and System Simulation. He is a
member of IEEE.
Sin P. Lai was born Malaysia. He received the B.Sc. degree in Electrical Engineering with Honor from Curtin University of Technology, Perth, Australia in 2006. He is currently a Master of Electrical Engineering by research student at Curtin University. His research interests are in Power Systems with particular emphasis on power transformer insulation degradation analysis. He is a
graduate student member of IEEE.
Syed Islam (S’81, M, 83, SM’93) received the B.Sc., MSc, and PhD degree all in electrical power engineering in 1979, 1983, and 1988 respectively. He is currently the Chair Professor in Electrical Power Engineering and Head of Department of Electrical and Computer Engineering at Curtin University of Technology, Perth, Australia. He received the IEEE T Burke Haye’s Faculty Recognition award in 2000. He has published over 140 technical papers in his area of expertise. His research interests are in Condition Monitoring of
Transformers, Wind Energy Conversion, and Power Systems. He has been a keynote speaker and invited speaker at many international workshops and conferences. He is the current Vice-Chair of the Australasian Committee for Power Engineering (ACPE) and a member of the steering committee of the Australian Power Institute. He is a Fellow of the Engineers Australia, a senior member of the IEEE IAS, PES and DEIS, a Fellow of the IET and a chartered engineer in the United Kingdom. He is regular reviewer for the IEEE Trans. on Energy Conversion, Power Systems and Power Delivery.
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