Dissolved Gas Analysis Of Transformers: Examining Faults And Making Recommendations

Performance Evaluation of Transformer using DGA

Introduction:

Transformers are the most important component in the field of electrical engineering which could have a long life and said to be a cost intensive component. These transformers constitute an electrical supply networks. A transformer works on the principle of electromagnetism to modify an AC voltage to another. The oil sample analysis is said to be a maintenance method which is carried out to monitor the transformer health.  There are various types of analysis test such as standard oil test, Dissolved Gas Analysis (DGA) and furan analysis through which detailed information regarding the operation of the transformer is collected. Normally the Dissolved Gas Analysis (DGA) test is carried out to find the electrical (Hjartason, 2006) abnormalities. Through this method we can able to determine the electrical fault as well as the thermal faults. Each fault can be categorized into three different division based on the international standard IEC 60599. Though there are different techniques, the Dissolved Gas Analysis became most accepted technique in the last decade. In this paper, the discussion is done regarding the DGA and also brief information is given about the decision making of an asset manager with the data received from the analysis.

Performance which is carried out by DGA (Hjartason& Otal, 2006)  in the insulating oil with the oil sampling analysis test is used as an evaluation of the transformer health. Any malfunction that happens inside a transformer and its required equipment could generate some gases inside it. Therefore, the identification of these gases and the information obtained from that could be very useful for some maintenance and prevention. There are many methods to estimate these gases but the Dissolved Gas Analysis (DGA) is said to be the most efficient. In order to measure the concentration of the dissolved gases, there are two process carried out: 1) Sampling the oil obtained 2) Testing the samples. This DGA analysis should be carried out at least a year and the details should be compared with the previous analysis data. There are several standards such as ASTM D3613, ASTM D3612, and ANSI/IEEE C57.104(Rowland & Bahadoorsingh, 2008), respectively to evaluate the result.

The main causes of the formation of the gases are due to the electrical strife and thermal putrefaction. At some point, each and every transformer could produce gases in usual working temperature. The transformer insulation process is done through several mineral oils which is said to be the composition of several hydrocarbons. The decomposition process in these hydrocarbons is said to be tedious due to the thermal and the electrical fault (Abu-Elanien & Salama, 2009). The basic reaction occurs due to the breakage of C-H bonds and C-C bonds. Hence we could get the fragments of hydrocarbon and some hydrogen atoms.  This leftover mingle with each other and leads to the formation of gases such as hydrogen (H₂), methane (CH₄), acetylene (C₂H₂), ethylene (C₂H₄), and ethane (C₂H₆). Moreover, due to the cellulose insulation, thermal decomposition or electrical problem generates methane (CH₄), hydrogen (H₂), carbon monoxide (CO), and carbon dioxide (CO₂). These gases are considered to be the key gases and their property is said to be combustible (here the exceptional gas is CO₂ which is non-combustible).

Causes of Gas Formation

This key gas depends highly on their temperature (John, 2006) which is based on their volume of material at that circumstantial temperature. The small volume at high temperature could produce the same quantity of gases as produced by the huge volume at restrained temperature. This is mainly caused due to the effect on volume. For this reason, the gases which are formed due to the transformer’s insulating oil is used for the evaluation process by comparing with the past history of these transformers (Ridwan, Talib & Ghazali, 2014) in order to find out any faults that could happen potentially or thermally.

Later the appropriate samples is examined and evaluated, the foremost step of the DGA analysis is to find the concentration levels of each and every key gases samples. This could be expressed in parts per million (ppm). It is endorsed that the concentration of the key gases change in time and therefore the rate of change of the concentration is calculated (Jongen, Gulski, Morshuis, Smith, Janseen, 2007). Fundamentally, the probable fault in the transformer could be indicated by the sharp rise in the value of key gas concentration. Therefore it could be said that the result of the DGA analysis gives a sharp rise in the value of the concentration level of the gases. If the normal value limit is surmounted, then supplementary analysis of the sample should be taken and once again we have to confirm where the key gas concentration level is accumulating. When the level reach the action level point then the transformer (Ledwich and Islam, 2000) should be considered and that particular transformer should be removed. Therefore care must be taken while taking this sample analysis test. This particular test involves in the calculation of the key gas ratio and then correlating it with certain limit range.

Table 1: The description of the gas with their limit range and their fault type

Gas Description	Normal Limit(<)	Actual Limit(>)	Potential fault type
H2(Hydrogen)	150	1000	Corona, Arcing
CH4(Methane)	25	80	Sparking
C2H2(Acetylene)	15	70	Arcing
C2H4(Ethylene)	20	150	Severe Overheating
C2H6(Ethane)	10	35	Local Overheating
CO(Carbon Monoxide)	500	1000	Severe Overheating
CO2(Carbon dioxide)	10000	15000	Severe Overheating
TDCG(TotalCombustibles)	720	4630

The Gas Description with their respective key gas concentration is given in the above table. When the value exceeds the normal limit then the sample frequency should be increased with the consideration given to planned outage in near term for the further evaluation. When the value exceeds the Action limits then the particular transformer should be removed immediately from the service.

At the table given below, consists of the data composed as part of a random sampling test from the Buchholz relay of transformer C₂. This subsequent test of DGA analysis (Paul, Barringer  &Associates, 2003) was carried out in all the transformers and the result is given in the form of a bar graph shown in figure 1. The transformer is an autotransformer which is of core-type with 132/66kV and 90MVA.

Understanding DGA Results

Table 2: Estimated result from the DGA analysis test

Install Date	Sub	Transformer	H2	CH4	C2H6	C2H4	C2H2	CO	C02
06/2013	A	A1	11.7	9.98	6.40	6.19	0.05	323.8	3001.16
06/2013	A	A2	19.68	92.93	362.06	3.57	0.05	243.94	3002.27
06/2013	B	B1	18.04	75.02	306.76	7.54	0.05	204.15	2589.93
06/2013	C	C1	46.23	366.55	391.87	464.15	0.05	247.41	3733.99
06/2013	C	C2	150.81	365.87	183.97	899.42	6.48	35.72	1583.54
06/2013	D	D1	12.86	55.84	233.14	5.47	0.79	192.49	2150.23
06/2013	D	D2	25.04	134.39	637.14	23.67	0.05	218.5	1695.52
06/2013	D	D3	17.86	123.22	582.37	25.27	0.59	199.96	1769.85
06/2013	E	E1	13.75	86.17	350.73	5.35	0.05	127.26	2485.08
06/2013	E	E2	15.95	94.17	223.02	8.85	0.05	201.2	2015.39

The foremost step in the elucidation of DGA data is to examine the glitches with the rectitude of the data. Any problems with the perceived data must be handled before any culmination is declared regarding the transformer. This interpretation (Andress, Endrenyi, and Yung(2010) of the DGA analysis test should be concluded once it is compared with the previous result of the analyzed data from the old samples. If the result of the data goes well with the previous one, then the data could be considered and based on that the transformers could be stated. If it does not agree with the earlier sample data, then the particular data should be inspected before the fault outcomes are made.

1. Concentration of the gas can alter based on the sample:

The best sampling could be acquired from the transformer with is rich in the insulation oil and therefore best concentration measurement can also be obtained.  The uncertainty of this sampling process could be 10-15% (Bertling, 2002). Deprived repeatability measurement of gas concentration can hide minute fluctuations that could be an indication of problem in the early stage and make it very arduous to detect and evaluate any fault that is identified.

1. Different sample values obtained:

Human error is common and these errors could be:  interchanging the sample values between two gases; changing the sample from a distant oil compartment or from a distant transformer; replicating or withdrawing digits in a numbers (Li, and Pai, 2002). This could also be possible due to the inappropriate oil sample and due to the aging and improper maintenance of the equipments.

1. Environmental factors could affect the concentration:

If the concentration of the hydrogen gas decreases it means that the gas is exposed by the oxygen content which could be caused due to some air bubbles from the sample syringe. If other gases concentration is decreased or increased except hydrogen then there could be some possibilities of transcription error and sometimes it could be due to the stray gassing.

1. Concentration of H₂gas is low in every sample:

 H₂ gas has a property to escape very quickly at any instant, (Li , Jonas, Yan, Corns, Choudhury, and Vaahedi, 2007) and the transformer could lose other gases due to their design or there could be an outflow through an erupted conservator diaphragm. When this happens then care should be taken since this could lead to the missing of certain fault or their asperity could be underrated. The decrease in the H₂ gas could be due the problem in the sensors or the gas chromatography. If this is the case then there could be some possibility of the inaccurate measurement of the other gases.

1. Replacement of oil:

Interpreting DGA Data

Oil that is kept inside the layers of paper winding separator (Werle, 2003) is generally unsophisticated by degassing or substituting the oil from the main tank. Later, the gas from the winding cover oil undergo diffusion over weeks or months into the clean oil until the gas concentrations interior and exterior the paper separator gets balanced. More often than not (accepting that no novice gas is formed in the mean time) the ultimate impact is to reestablish approximately 10% to 15% of the gas level decline (Li & Korczynski, 2004). The increase in the concentration of the gas, taken after degassing or oil substitution must not be mixed up with the dynamic gas generation.

The Resource Chief must choose on a consolidation of various activities that imitate plant stacking and stretch levels, support plans, and substitution timetabling. These things are for the most part forbid additionally depend upon the framework (Perkins, Pettersson, Fantana, Oommen, Jordan, 1999) necessities of the gear. It is conceivable that one course to resource cultivation includes changing the working environment of the thing to expand life or reduce prompt disappointment probability. These things are by and large forbid additionally depend upon the framework necessities of the gear (Aubin, Bourgault, Rajotte,  Gervais, 2002). It is conceivable that one course to resource cultivation includes changing the working environment of the thing to amplify life or decrease quick disappointment probability.

Conclusion:

Transformers are the most important equipment and essential asset in a substation and the failure of the transformers could result in the heavy damage both electrically and economically. Hence regular monitoring of the transformer is necessary and the details should be maintained properly. The sampling methods so far we discussed above is the best method and this could be done with the various equipment. The test carrying equipment should also be maintained properly. An asset manager should play a major role in these cases and the decisions made by an asset manager should be viable.

References:

Hjartason, T. and Otal, S. (2006) Predicting Future Asset Condition Based on Current Health Index and Maintenance Level, Albuquerque, NM, USA:IEEE 11th International Conference on Transmission & Distribution Construction, Operation and Live-Line 75 Maintenance.

Rowland, S. M. and Bahadoorsingh, S. (2008) A Framework Linking Insulation Ageing and Power Network Asset Management. Vancouver: IEEE International Symposium on Electrical Insulation.

Abu-Elanien E.B.  Ahmed and Salama, M.M.A(2009)Asset Management Techniques for Transformers. University of Waterloo, Waterloo, ON, Canada: Electric Power Systems Research

John, W.(2006)PAS 55 – Asset Management: Concept and Practise. Daytona Beach Florida: International Maintenance Conference.

Jongen R., Gulski, E., Morshuis, P., Smith, J., Janseen, A.(2007) Statistical Analysis of Power Transformer Component Life Time Data. Singapore: IEEE. 3-6, December. pp 16-18

Ledwich, G., Wu, T. and Islam, S.M(2000) A Novel Fuzzy Logic Technique for Power Transformer Asset Management . IEEE, pp. 177 – 186

Barringer H. Paul, Barringer P.E., &Associates (2003) A Life Cycle Cost Summary, Perth, Australia: International Conference of Maintenance Societes (ICOMS-2003).

Andress, G.J., Endrenyi, J. and Yung, C.(2010) Risk- based Planner for asset management , New York: IEEE Computer Application in Power. pp. 20-26.

Bertling, L. (2002) Reliability Centered Maintenance for Electric Power Distribution Systems. Ph.D. thesis. Stockholm: Royal Institute of Technology (KTH).

Li, W. and Pai, S. (2002) Evaluating Unavailability of Equipment Aging failures. IEEE Power Engineering Review.  February.  pp52-54

Li W., Jonas, H. C., Yan, S., Corns, B., Choudhury, P. and Vaahedi, E. (2007) Reliability Decision Management System: Experience at BCTC. Vancouver: IEEE CCECE 2007 conference.

Werle, P. et al.(2003) An Enhanced System for Partial Discharge Diagnosis on Power Transformers. Niederlande: 13th International Symposium on High Voltage Engineering (ISH).

British Columbia Transmission Corporation, 17 technical reports on reliability assessment [online]. Available at: https://www.bctc.com/the_transmission_system/reliability_assessment/.

Li, W.  and Korczynski, J. K. (2004) A Reliability Based Approach to Transmission Maintenance Planning and Its Application in BCTC System.  IEEE Trans. on Power Delivery. Vol. 19, No. 1, pp303-308

Perkins, M.  Pettersson, L. Fantana, N.L. Oommen, T.V. Jordan, S. (1999) Transformer Life Assessment Tools with Special Application to Nuclear Power Station Generator Transformers. Monterrey Mexico: IEEE Transformer Committee Meeting.

Aubin, J. Bourgault, A. Rajotte, C.  Gervais, P. (2002) Profitability Assessment of Transformer On-Line Monitoring and Periodic Monitoring. Framingham: EPRI Substation Equipment Diagnostics Conference.

Ridwan, M.I. Talib, M.A. Ghazali, Y.Z.Y.(2014) Application of weibull-bayesian for the reliability analysis of distribution transformers. Langkawi, Malaysia: In Proceedings of the IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014).