Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in
Review of Various Method of Fault Analysis Based On Dissolved Gas Analysis in Power Transformer Ravi.U.Magre1, Prof. K. Chandra Obula Reddy2 1
Student, ME (EPS), Matsyodari Shikshan Sansthas College of Engineering and Technology, Jalna (MS) 431203 2 Assistant Professor, Dept. Electrical Engineering, Matsyodari Shikshan Sansthas College of Engineering and Technology, Jalna (MS) 431203
Abstract: Power transformer is one of the fundamental equipment in the power system. Transformer breakdown or damage may interrupt power distribution and transmission operation, as well as incur high repair cost. Thus, detection of incipient faults in power transformer is essential and it has become an interesting topic to study. DGA is a reliable technique to detect incipient faults as it provides wealth of information in analyzing transformer condition. Power transformers being the
(CO) and carbon dioxide (CO2) can be detected and the concentrations of the gases, total concentrations of the combustible gases, the relative proportions of gases and gassing rates used estimate the condition of the transformer and the incipient faults presented. Hence, qualitative and quantitative determination of dissolved gases in transformer oil may be of great importance in order to assess fault condition and further operating reliability of power transformers.
major apparatus in a power system, thus the assessment of transformer operating condition and lifespan have obtained crucial significance in latest years. Dissolved gas analysis (DGA) is a sensitive and reliable technique for the detection of incipient fault condition within oil-immersed transformers, which provides the basis of diagnostic evaluation of equipment health. These expert system
2. Dissolved gas transformer oil fault
also consider other information of transformer such as type, voltage level, maintenance history, with or without tap changer etc. These proposed approaches provide the user a more accurate result and better condition awareness of the transformer.
1. Introduction Power transformers play an important role in both the transmission and distribution of electrical power and its correct functioning is essential to the operation system. In service, transformers are subject to electrical and thermal stresses, causing the degradation of the insulating materials which degradation then leading to the formation of several gases. These gases tend to stay dissolved. According to the temperature reached in the area, the product of the oil decomposition change. There is a correlation between type of the gases found and these temperatures. Thus, based on the temperature on which the oil decomposition occur and as a function of the formation of the gases for that temperature, it is assumed that faults may be present. Based on dissolved gas analysis (DGA) gases, such as hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon-monoxide
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2.1. Transformer fault types Dissolved gas analysis (DGA) is a sensitive and reliable technique to identify the power transformers faults. By using this technique, it is possible to discriminate fault in a great variety of oil-filled equipment. IEC Publication 60599provides a coded list of faults detectable by DGA. Table 1 tabulates the fault types and the codes addressed in this paper. Table 1 Fault Type used in Analysis. Fault Type Code Partial discharge PD Low energy discharge D1 High energy discharge D2 Thermal faults T <300 ° C T1 Thermal faults 300 <T< 700 ºC T2 Thermal faults T > 700 ºC T3
2.2 DGA interpretation methods Many interpretative methods based on DGA to detect the incipient fault nature have been reported. In this paper, three of the DGA methods were studied: - Gas key method; - IEC Ratios method; - The graphical representation method. - key gas method
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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in In this key gas method, we need five key gas concentrations: hydrogen (H2), methane (CH4), acetylene (C2H2), ethylene (C2H4) and ethane (C2H6) available for consistent interpretation of the fault. IEC 60599 standard establishes an interpretation by which five gases H2, CH4, C2H2, C2H4 and C2H6 can be used to detect different types of faults.
3. Discharge of High Energy 4. Thermal Fault, t < 3000C 5. Thermal Fault , 3000C < t < 7000C 6. Thermal Fault, t > 7000C 7. Normal
Table 2 shows the diagnostic interpretations applying various key gas concentrations. Interpretation gas dissolved in the oil. Gas Detected Interpretation Oxygen (O2) Transformer seal fault Oxide and Dioxide Carbon (CO and CO2) Cellulose decomposition Hydrogen (H2) Electric discharge (corona effect, low partial Acetylene (C2H2) Electric fault (arc), spark) Ethylene (C2H4) Thermal fault (overheating local) Ethane (C2H6) Secondary indicator of thermal fault Methane (CH4) Secondary indicator of an arc or serious overheating
2.3 Data collection and preparation Data for combustible gas generated from transformer oil are obtained from the main utility company in Malaysia, Tenaga National Berhad (TNB). The data consist of the values of combustible gas generated in every sample of transformer oil taken. The oil sample had undergone laboratories testing to determine types and values of combustible gases generated. Three ratios of combustible gases values are used as the input, while seven transformer conditions are used as the targeted output. Table 2 shows the properties of input and the targeted output of the developed network. The data interpretation is based on IEC 60599(2007) standard. The IEC threeratio is widely used as the guideline and standards in diagnosis stage because it was found one of the effective and convenient guidelines and standards available. Table 2 tabulates the IEC standard used throughout the research to interpret fault types in power preprocessing steps is performed to ensure network efficiency. Preprocessing input involves normalization and de-normalization of data where the inputs and targeted output values are scaled to a specific range transformer. It consists of three keygas ratios corresponding to the suggested fault diagnosis. When key gas ratios exceed specific limits, incipient faults can be expected in the transformer Table 2 Properties of input and output developed network Input Output targeted 1. C2H2/C2H4 1. Partial Discharge 2. CH4/H2 2. Discharge of Low 3. C2H4/C2H6 Energy
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3. DGA Methods Various interpretative techniques have been reported in the literature to predict development of faults, such as IEC60599 Standard’s ratio codes, IEEE Standard’s Roger’s and Doernenburg’s ratio codes, the Key gas method, CIGRE guidelines, MSZ-09-00.0352 National Standard’s ratio codes and graphical techniques such as Duval Triangle method. All these methods have been based on years of experience in fault diagnosis using DGA. None of these methods are based on mathematical formulation and interpretations are heuristic in nature and vary from utility to utility. However, in recent years more consistent method shave been developed in DGA interpretation based on large number of expert system, data and failure history of transformers.
3.1 Key gas method Decomposition of gases in oil and paper insulation of transformers caused by faults depends on temperature of faults. Various faults produce certain gases and the percent of some gases have been found to mention fault types, such as overheated oil and cellulose, corona in oil and arcing in oil.
3.1.1Roger’s Ratio methods The ratio methods are the most widely used technique Rogers’, Doernenburg’s and IEC ratios are all used by utilities. Typically, three or four ratios are used for sufficient accuracy, such as the original Roger’s ratio method uses four ratios (CH4/H2, C2H6/CH4, C2H2/C2H4, and C2H4/C2H6) to diagnose eleven incipient fault conditions and a normal condition. This method took information from the Halstead’s thermal equilibrium and Doernenburg’s ratios along with information from faulted units. The Roger’s method utilizes four ratios, CH4/H2, C2H6/CH4, C2H4/C2H6 and C2H2/C2H4. Diagnosis of faults is accomplished via a simple coding scheme based on ranges of ratios. Four conditions are detectable, i.e. normal ageing, partial discharge with or without tracking, thermal fault and electrical fault
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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in of various degrees of severity. Table 3 shows codes for gas ratios used in this method.
0
0
12
1-2
Arc with power follow Through
w
0
0
2
2
x
5
0
0
1-2
Continuous sparking To floating potential Partial discharge with Tracking(note CO)
Table 3 Gas ratio codes
Gas ratios CH4/H2 C2H6/CH4 C2H4/C2H6 C2H2/C2H4
Ratio codes i j k l
y
Table 4 Rogers’ ration code Ratio code
range
code
i
<=0.1
5
>0.1,<1.0
0
>=1.0,<3.0
1
>=3.0
2
<1.0 >=1.0 <1.0 >=1.0,<3.0 >=3.0 <0.5 >=0.5,<3.0 >=3.0
0 1 0 1 2 0 1 2
j j
l
3.1.2
For diagnosis scheme recommended by IEC originated from Rogers’ method, except that the ratio C2H6/CH4 was dropped since it only indicated a limited temperature range of decomposition. Four conditions are detectable, i.e. normal ageing, partial discharge of low and high energy density, thermal faults and electrical faults of various degrees of severity. In this method three gas ratios are used to interpret the faults.
Table 5 Rogers’ fault diagnosis table i
j
k
l
Diagnosis
code
0
0
0
0
n
5
0
0
0
Normal deterioration Partial discharge
1-2
0
0
0
Slight overheating <1500C
p
1-2
1
0
0
Overheating 1500C-2000C
q
o
0
1
0
0
Overheating 2000C-3000C
r
0
0
1
0
General conductor Overheating
s
1
0
1
0
Winding circulating currents
t
1
0
2
0
Core and tank Circulating currents, Overheated joints Flashover without Power follow through
u
0
0
0
1
IEC Method
v
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Table 6 IEC Ratio code Ratio code range l <0.1 0.1-1.0 1.0-3.0 >3.0 i <0.1 0.1-1.0 1.0-3.0 >3.0 k <0.1 0.1-1.0 1.0-3.0 >3.0 Table 7 IEC fault diagnosis table l i k characteristic fault 0 0 0 Normal ageing * 1 0 Partial discharge Of low energy density 1 1 0 Partial discharge Of high energy density 1-2 0 1-2 Discharge of low energy (Continuous sparking) 1 0 2 discharge of high energy (arc with power flow through)
code 0 1 1 2 1 0 2 2 0 0 1 2
code a b c
d
e
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Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in 0
0
1
Thermal fault<1500C
f
0
2
0
Thermal fault 1500C3000C
g
4. Park Negative Sequence Method 0
0
2
1
Thermal fault 300 C7000C
h
0
2
2
Thermal fault>7000C
m
.
3.1. Doernenburgâ&#x20AC;&#x2122;s Ratio Method This method utilizes the gas concentration from ratio of CH4 /H2, C2H2/CH4, C2H4/C2H6 and C2H2/C2H4.This method is used to detect three types of faults (1) Thermal fault (2) Corona fault t(low energy partial discharge) (3) Arcing (high intensity partial discharge). The value of the gases at first must exceed the concentration A1 to indicate whether there is really a problem with the unit or not and then we check the ratio of generated gases for indication of type of fault. Table 1(a) shows the key gases and their concentration A1 and 1(b) shows fault according to gases ratio. The limitation of this method is, it can detect only three types of faults. Table 8 Concentration Level for Doernenburgâ&#x20AC;&#x2122;s Ratio Main gases Concentration level (ppm) A1 Hydrogen (H2) 100 Carbon monoxide(CO) 350 Methane (CH4) Acetylene(C2H2) Ethylene (C2H4) Ethane(C2H6)
Table 9 Fault diagnosis Differe Ratio1 Ratio2 nt fault (CH4/ (C2H2/C2 diagno H2) H4 ) sis 1. >1.0 <0.75 therma >0.1 <1.0 l fault 2.coro na fault 3.arcin g fault
<0.1 <0.01 >0.1 >0.01 <1.0 <0.1
Not significa nt >0.75 >1.0
Protective devices are a crucial part for detecting fault conditions in a power system. The appropriate protection scheme must be selected to ensure the safety of power apparatus and reliability of the system. One that type of protecting scheme is the differential protection scheme which is aimed at detecting internal winding faults in transformer. Differential protection is one of the most used methods for protecting transformers against internal faults. The technique is based on comparison of currents both primary and secondary side of transformer. Taking into account voltage ratio and vector group adjustment, the related relay trips whenever the difference of currents magnitude in both sides crosses the limit. Although, this protection scheme most accurate one, is subjected to false operation in some special cases. Such as problems related to a mismatching between the transformer ratio and the CTs ratios, magnetizing inrush current To overcome this problem park negative sequence method is used. It is one of the effective techniques for diagnosing the occurrence of internal winding faults in the windings of operating transformer. In this technique primary and secondary phase currents are measured and negative sequence differential current is calculated. The magnitude of this negativesequence current signifies whether the fault is internal or external. However, with this approach, it is difficult to discriminate between unbalanced loads and winding faults. Also this method is unable to detect the fault when the transformer is unloaded.
5. Conclusion
120 35 50 65
Ratio3 (C2H2/C H4)
Ratio4 (C2H6/C2 H2)
<0.3 <0.1
>0.4 >0.2
DGA has been recognized as an important tool in condition monitoring of power transformer. The main advantage of using ratio methods is that, volume of oil involved in the dissolution of gas is not required as only ratios of gases are involved. But the drawback is that they fail to cover all ranges of data. This paper presents different diagnostics methods for internal winding fault analysis in transformers, which are employed in practice. This paper presents an ample review of all these methods that can help the diagnosis of deferent type of internal faults.
6. Acknowledgement <0.3 <0.1
>0.4 >0.2
>0.3 >0.1
<0.4 <0.2
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I express my sincere gratitude to my guide Prof. K. Chandra Obula Reddy for providing me opportunity to complete my project. I sincerely thank Prof. Ms. Alfiya A. Mahat, HOD, and department of electrical engineering for her continuous guidance. I am also thankful to Prof. Dr. S.K. Biradar, Principal, Matsyodari Shikshan Sansthas College of Page 173
Imperial Journal of Interdisciplinary Research (IJIR) Vol-3, Issue-2, 2017 ISSN: 2454-1362, http://www.onlinejournal.in Engineering and Technology, Jalna (MS) who rendered his help to complete my project within time.
Electric Power Systems Research, Vol. 79, pp. 89– 97, 2009.
7. Reference. [1] IEEE Std C37.91-2000, “IEEE Guide for Protective Relay Applications to Power Transformers.” [2] J. Webster (ed.), “Wiley Encyclopedia of Electrical and Electronics Engineering,” Transformer Protection, John Wiley& Sons, Inc1999. [3] M.R. Barzegaran and M. Mirzaie, “Detecting the position of winding short circuit faults in transformer using high frequency analysis,” European Journal of Scientific Research, Vol. 23, 2008, pp. 644-658. [4] A. Shintemirov, W.J. Tang, W.H. Tang, and Q.H. Wu, “Improved modeling of power transformer winding using bacterial warming algorithm and frequency response analysis,” Electric Power Systems Research, Vol. 80, 2010, pp. 1111–1120. [5] M.A.Abdul Rahman, H.Hashim and P.S.Ghosh, “Frequency response analysis of power transformer,” Electrical Engineering Department, College of Engineering, University Tenaga National. [6] M. Faridi, M. Kharezi, E. Rahimpour, H.R. Mirzaei and A.Akbari, “Localization of turn-to-turn fault in transformersusing artificial neural networks and winding transfer function,” International Conference on Solid Dielectrics, Potsdam, Germany, July 4-9, 2010. [7] H.Wang and K.L. Butler, “Finite element analysis of internal winding faults in distribution transformers,” IEEE Transactions on Power Delivery, Vol. 16, July 2001. [8] L.M.R. Oliveira and A.J. Marques Cardoso, “Online diagnostics of transformer winding insulation failures by Park’s vector approach,” Proceedings of the 9th International Electrical Insulation Conference, pp. 16-21, Berlin, Germany, June 18-20, 2002. [9] A. Nagopitakkul and A. Kunakorn, “Internal fault classification in transformer windings using combination of discrete wavelet transforms and back-propagation neural networks,” International Journal of Control, Automation, and Systems,Vol. 4, No. 3, pp. 365-371, June 2006. [10] L. Satish and Subrat K. Sahoo, “Locating faults in a transformer winding: an experimental study,”
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