82979891-F06-DGA

Together We PowerThe World

Techniques for Interpretation of Data for DGA From Transformers

Lance Lewand, Doble Engineering

2006 IEEE Conference

Purpose of DGA

To provide a non-intrusive means to determine if a transformer incipient fault condition exists or not

Too conservative Too liberal

To have a high probability that when entering an transformer a problem is apparent

To prevent an unexpected outage

To reduce risk to the unit and the system/company


Interpretation Techniques

Incipient Fault Types, Frank M. Clark, 1933/1962

Drnenburg Ratios, E. Drnenburg, 1967, 1970

Potthoffs Scheme, K. Potthoff, 1969

Absolute limits, various sources, early 1970s

Shanks Visual Curve method, 1970s

Trilinear Plot Method, 1970s

Key Gas Method, David Pugh, 1974

Duval Triangle, Michel Duval, 1974



Rogers Ratios, R.R. Rogers, 1975

Glass Criterion, R.M Glass, 1977

Trend Analysis, various sources, early 1980s total volume per day ppm per day

Church Logarithmic Nomograph, J.O. Church, 1980s

Expert System Analysis, Richard Lowe, 1985



Expert System Monitor Program, Karen Barrett, 1989

Transformer Fingerprinting

IEEE C57.104, Limits, rates and TDCG, 1978/1991

Artificial Neural Networks (ANNs) and Fuzzy Logic X. Ding, E. Yao, Y. Liu and Paul Griffin, 1996 Vladimiro Miranda and Adriana Garcez Castro, 2004 Donald Lamontagne, 2006



IEC 60599 Ratios, Limits and gassing rates, 1999

Datamining and Log Transformation, Tony McGrail, 2000

Vector Algorithm, Nick Dominelli, Mike Lau & David Pugh, 2004


Most Commonly Used

Duval Triangle IEEE C57.104, Limits, rates and TDCG Straight Limits Key Gas Method Drnenburg Ratios Rogers Ratios IEC 60599 Ratios and Limits Trend Analysis Fingerprints Expert System Analysis


Dissolved Gas Acceptable Limits Various Sources

H2 CO CH4 C2H6 C2H4 C2H2 CO2 TCG

*IEEE 100101-700701-1800

>1800

350351-570571-1400

>1400

120121-400

401-1000>1000

6566-100

101-150>150

5051-100

101-200>200

3536-5051-80>80

25002500-4000

4001-10000>10000

720721-1920

1921-4630>4630

**Electra (CIGRE)

28.6 289 42.2 85.6 74.6 -- 3771 520

IEC 60599TypicalRange

60-150 540-900 40-110 50-90 60-280 3-50 5100-13000

Manufact. 200(250)

500(1000)

100(200)

100(200)

150(300)

15(35)

----

10651985

*IN THE PROCESS OF BEING REVISED**CORRECTED VALUES 1978( ) VALUE 6 7 YEARS


Key Gases - Arcing

0

1020

30

40

5060

70

8090

100C

o

m

b

u

s

t

i

b

l

e

s

,

%

CO H2 CH4 C2H6 C2H4 C2H2


Key Gases - Overheating, Oil

0

1020

30

40

5060

70

8090

100C

o

m

b

u

s

t

i

b

l

e

s

,

%



Key Gases - Partial Discharge

0

1020

30

40

5060

70

8090

100C

o

m

b

u

s

t

i

b

l

e

s

,

%



Key Gases - Overheating, Paper

0102030405060708090

100C

o

m

b

u

s

t

i

b

l

e

s

,

%



Drnenburg Ratio Method

Started out as only two ratios CH4/H2 C2H2/C2H4 plotted on a log-log scale. The areas corresponded to

thermal deterioration, arcing and partial discharge too many faults missed - went to 4 ratios

Ratio 1 (R1)=CH4/H2 Ratio 2 (R2)=C2H2/C2H4 Ratio 3 (R3)=C2H2/CH4 Ratio 4 (R4)=C2H6/C2H2


Drnenburg Ratio Method

Used to determine 3 general fault types Thermal faults Electrical Faults, low intensity discharges Electrical Faults, high intensity arcing


Drnenburg Ratio-Minimum Gas Levels (Drnenburg & IEEE Levels)

Hydrogen 200 100

Methane 50 120

Carbon Monoxide 1000 350

Acetylene 15 35

Ethylene 60 50

Ethane 15 65


Drnenburg Ratio

Criteria for application - a fault exists One Gas > 2 x minimum level At lest one gas > minimum level

Determine Validity, L1 norm test One gas in each ratio > minimum

Compare ratios to Fault Diagnosis Table

All fall within one condition-valid diagnosis


Drnenburg Ratio-Fault Diagnosis Table, from the oil

R1CH4/H2

R2C2H2/C2H4

R3C2H2/CH4

R4C2H6/C2H2

1-ThermalDecomp >1.0 0.3


Drnenburg Flowchart

From IEEE C57.104 - 1991


Initial Rogers Ratios

Took information from Halsteads thermal equilibrium and Drnenberg ratios along with information from faulted units

Originally developed four ratios CH4/H2 C2H6/CH4 C2H4/C2H6 C2H2/C2H4

Came up with a 4 number code that identified 11 incipient fault conditions and a normal condition


Halsteads Thermal Equilibrium


Initial Rogers Ratios

Ratio Range Code

CH4/H2 0.1>0.1


Rogers Fault Diagnosis Table

CH4/H2 C2H6/CH4 C2H4/C2H6 C2H2/C2H4 Diagnosis0 0 0 0 Normal5 0 0 0 Partial Discharge 0 0 0 Slight Overheating below 150C 1 0 0 Slight Overheating 150C to 200C0 1 0 0 Slight Overheating 200C to 300C0 0 1 0 General conductor overheating1 0 1 0 Winding circulating currents1 0 2 0 Core and tank circulating currents,

overheated joints0 0 0 1 Flashover without power follow through0 0 Arc with power follow through0 0 2 2 Continuous sparking to floating potential5 0 0 Partial discharge with tracking


Refined Rogers Ratio

Three ratios Ratio 1 (R1)=CH4/H2 Ratio 2 (R2)=C2H2/C2H4 Ratio 5 (R5)=C2H4/C2H6

No minimum levels suggested when normal levels exceeded


Refined Rogers Ratio-Fault Diagnosis

Case R2C2H2/C2H4

R1CH4/H2

R5C2H4/C2H6

Fault

0 0.1,


Rogers Ratios Flowchart

From IEEE C57.104 - 1991


IEC 60599

Identifies 6 different fault types PD: Partial Discharge D1: Discharge of low energy D2: Discharge of high energy T1: Thermal fault, t 700 C

Uses a combination of ratios (based on Rogers Ratios), gas concentrations and rates of gas increase


IEC 60599 Ratio-Fault DiagnosisR2

C2H2/C2H4R1

CH4/H2R5

C2H4/C2H6Fault

NS 1 D1 -Lowenergy

0.6-2.5 0.1-1 >2 D2 Highenergy

NS >1 (NS) 700C

NS = not significant regardless of valueConcentrations should be 10 x S (MDL)


IEC 60599 Rates of gas increase

>10% increase per month above typical levels = active fault

>50% per week or evolving faults of higher energy = serious


IEC 60599 Typical Gas Levels

H2 CO CH4 C2H6 C2H4 C2H2 CO2IEC 60599TypicalRange

60-150 540-900 40-110 50-90 60-280 3-50 5100-13000

CommunicatingOLTC

75-150 400-850 35-130 50-70 110-250 80-270 5300-12000

Note in IEC 60599: Typical values are higher in sealed transformers than free breathing transformers


Ratio Methods

Advantages quantitative independent of oil volume can be computer programmed

Disadvantages dont always yield an analysis not always correct dependence of preservation system Dornenburg has fallen out of favor because it misses

too many incipient faults


Ratio Methods

Solid insulation handled separately using carbon monoxide and carbon dioxide ratios


Trend Analysis

Historical Information

Has the percent TCG in the gas space risen suddenly?

Has the percent TCG in the oil risen suddenly?

Nameplate information

How old in the transformer?


Trend Analysis

Did a bushing fail at some point?

Did the transformer fail previously?

If the unit has been repaired and was the oil filtered or degassed?

Is the unit heavily loaded or overloaded?

Previous dissolved gas-in-oil test?


Transformer Fingerprints

GAS (PPM) Initial 3 Initial 3Hydrogen 350 260 110 210Methane 44 61 11 13Carbon Monoxide 670 650 520 630Ethane 26 25 3 4Carbon Dioxide 3000 1900 5000 3900Ethylene 9 5 8 10Acetylene -- -- -- --


GAS (PPM) Initial 3 Initial 3Hydrogen 0 1 0 0Methane 92 69 15 18Carbon Monoxide 370 400 33 57Ethane 2300 2300 560 520Carbon Dioxide 6000 6800 1800 2200Ethylene 180 180 9 6Acetylene 0 0 0 0

Transformer Fingerprints


Carbon Oxide Gases and Ratios

Cellulose Insulation Shell form > CO2 than core form - due to

mass Accidental CO2 CO2/CO : 3 -14:1 CO2/CO Avg. 7:1 Approach 1 high temperature faults High CO2 with low CO-lack of

cooling/general overheating


Pitfalls

Gases produced not as a result of incipient fault condition Leaking between tap changers and main tank lower voltage transformers having higher CO

and CO2 values as a result of non-vacuum Hitreatment

Welding producing acetylene and other gases Out-gassing of paints and gaskets, usually CO

and CO2 Stray gassing characteristics


Pitfalls

Incipient Faults not really covered production of hydrogen from overheated oil

thin films on core laminations (>140C) Oxidation and thermal heating of the oil

causing the production of CO and CO2

Gases produced not as a result of incipient fault condition Leaking between the tap changer and main

tank


Pitfalls

Galvanic reactions (steel + water + O2 = hydrogen production)

lower voltage transformers having higher CO and CO2 values as a result of non-vacuum treatment, oxygen + heat

Welding producing acetylene and other gases

Out-gassing of paints, gaskets & polymers, usually CO and CO2


Pitfalls

Stray gassing characteristics (highly refined oils H2)

Contaminants produce gases

Decomposition of additives such as passivators can produce gases as well (H2 and CO2)


In Reality - Expert Systems are Used

History Key gases Ratios Fingerprints - similar populations Trend analysis Internal databases Total combustible gas Rate of gas generation A human expert

Use the tools in the toolbox, not

just one!!!


THANK YOU FOR YOUR ATTENTION

IEEE/PES Transformer CommitteeMontreal, Canada

Tuesday, October 24, 2006

Dissolved gas analysis and the Duval Triangle

by Michel Duval

-DGA is for Dissolved Gas Analysis.

-DGA is probably the most powerful tool for detecting faults in electrical equipment in service.

-Over one million DGA analyses are performed each year by more than 400 laboratories worldwide.

-Gases in oil always result from the decomposition of electrical insulation materials (oil or paper), as a result of faults or chemical reactions in the equipment.

-for example, oil is a molecule of hydrocarbons, i.e., containing hydrogen and carbon atoms,linked by chemical bonds (C-H, C-C).

-some of these bonds may break and form H*, CH3*, CH2* and CH* radicals.

All these radicals then recombine to form the fault gases observed in oil:

-in addition to these gases, the decomposition of paper produces CO2, CO and H2O, because of the presence of oxygen atoms in the molecule of cellulose:

Hydrogen H2Methane CH4Ethane C2H6Ethylene C2H4Acetylene C2H2Carbon monoxide CO

Carbon dioxide CO2Oxygen O2Nitrogen N2

The main gases analyzed by DGA

-some of these gases will be formed in larger or smaller quantities depending on the energy content of the fault.

-for example, low energy faults such as corona partial discharges in gas bubbles, or low temperature hot spots, will form mainly H2 and CH4.

-faults of higher temperatures are necessary toform large quantities of C2H4.

-and finally, it takes faults with a very high energycontent, such as in electrical arcs, to form large amounts of C2H2.

-by looking at the relative proportion of gases in the DGA results it is possible to identify the type of fault occurring in a transformer in service.

Gas formation patterns

-are related only to the materials used and faultsinvolved.

-are the same in all equipment where these materials are used (e.g., sealed or air-breathingpower transformers, reactors, instrumenttransformers, LTCs, etc).

Standards/ Guides for the interpretation of DGA:-IEC Publication 60599 (1999).-IEEE Guide C57.104 (1991) (under revision).

Other useful information in:-IEEE EI.Mag., Apr. 2001, June 2002, Aug. 2005.-CIGRE Brochure # 296 (2006).

6 basic types of faults detectable by DGA have been defined by the IEC:

1.Partial discharges of the corona-type (PD).

-typical examples: discharges in gas bubblesor voids trapped in paper, as a result of poor drying or poor oil-impregnation.

2.Discharges of low energy (D1)

-typical examples: partial discharges of the sparking-type, inducing carbonized punctures in paper.

-or low-energy arcing, inducing surface trackingof paper and carbon particles in oil.

3.Discharges of high energy (D2)

-typical examples: high energy arcing, flashovers and short circuits with power follow-through, resulting in extensive damage to paper, large formation of carbon particles in oil, metalfusion, tripping of the equipment or gas alarms .

4.Thermal faults of temperatures < 300 C (T1)

Faults T1 are evidenced by paper turning: -brown (> 200 C). -black or carbonized (> 300 C).

Typical examples: overloading, blocked oil ducts

5.Thermal faults of temperatures between 300 and 700C (T2)

Faults T2 are evidenced by : -carbonization of paper.-formation of carbon particles in oil.

Typical examples: defective contacts, defective welds, circulating currents.

6.Thermal faults of temperatures > 700C (T3)

Faults T3 are evidenced by : -extensive formation of carbon particles in oil.-metal coloration (800 C) or metal fusion (> 1000 C).

Typical examples: large circulating currents in tank and core, short circuits in laminations.

The first one was the Dornenburg method in Switzerland in the late 1960s, then the Rogers method in UK in the mid 1970s.

Variations on these methods have later been proposed by the IEC (60599) and IEEE.

Several diagnosis methods have been proposed to identify these faults in service.

One drawback of these methods is that no diagnosis can be given in a significant number of cases, because they fall outside the defined zones.

All these methods use 3 basic gas ratios: (CH4/H2, C2H2/C2H4 and C2H6/C2H4).

Depending on the values of these gas ratios, codes or zones are defined for each type of fault.

Another method used by IEEE is the so-called key-gas method, which looks at the main gas formed for each fault, e.g, C2H2 for arcing.

One drawback of this method is that it often provides wrong diagnoses.

Finally, there is the Triangle method, which was developed empirically in the early 1970s, and which is used by the IEC.

It is based on the use of 3 gases (CH4, C2H4 and C2H2) corresponding to the increasing energy levels of gas formation.

One advantage of this method is that it always provides a diagnosis, with a low percentage of wrong diagnoses.

Comparison of diagnosis methods.

% Unresolveddiagnoses

% Wrong diagnoses

% Total

Key gases 0 58 58

Rogers 33 5 38

Dornenburg 26 3 29

IEC 15 8 23

Triangle 0 4 4

However, many people are not quite familiar with the use of triangular coordinates, so I will try to explain that in more detail today.

The triangle representation also allows to easily follow graphically and visually the evolution of faults with time.

The triangle method.

The triangle method plots the relative % of CH4, C2H4 and C2H2 on each side of the triangle, from 0% to 100%.

The 6 main zones of faults are indicated in the triangle, plus a DT zone (mixture of thermal and electrical faults).

FAQ: How fault zones have been defined in the Triangle ?

Answer: Fault zones are based on a large number of cases of faulty transformers in service which have been inspected visually.

Cases of faults PD and D1

tracking; U sparking; { small arcing.

Cases of faults D2

circulating currents ; { laminations ; U bad contacts

Cases of thermal faults in oil only

{ brownish paper ; carbonized paper ; U not mentioned

Cases of thermal faults in paper

FAQ: how corona PDs, which form a lot of H2, can be identified in the Triangle without using this gas ?

Answer: in such faults, CH4 is indeed formed in smaller amounts than H2 (typically 10 to 20 times less), but can still be measured easily by DGA.

Answer: because CH4 provides better overall diagnoses for all types of faults.

FAQ: in the Triangle, why not use H2 rather than CH4 to represent low energy faults ?

A possible explanation (?): H2 diffuses much more rapidly than hydrocarbon gases from transformer oil. This will affect gas ratios using H2 but not those using hydrocarbon gases.

First calculate: CH4 + C2H4 + C2H2 = 300 ppm.

FAQ: So, how to use the triangle ?

If for example the DGA lab results are: CH4 = 100 ppm C2H4 = 100 ppm C2H2 = 100 ppm

Then calculate the relative % of each gas: relative % of CH4 = 100 / 300 = 33,3 % relative % of C2H4 = 100 / 300 = 33,3 % relative % of C2H2 = 100 / 300 = 33,3 %

These values are the triangular coordinates to be used on each side of the triangle.

To verify that the calculation was done correctly, the sum of these 3 values should always give100%, and should correspond to only one point in the triangle.

Each DGA analysis received from the lab will always give only one point in the triangle.

The zone in which the point falls in the Triangle will identify the fault responsible for the DGA results.

The calculation of triangular coordinates can easily be done manually, or with the help of a smallalgorithm or software.

Errors are often made when developing such an algorithm, so check it first with the free algorithm available from me ([email protected]).

For those familiar with computer graphics, it is also possible to develop a software displaying the point and the fault zones graphically in the triangle.

Several software packages are available for DGA interpretation using the triangle method

.The Triangle, being a graphical method, allowsto easily follow the evolution of faults with time, for instance from a thermal fault to a potentially much more severe fault such as D2.

The most severe faults:

-faults D2 in paper and in oil (high-energy arcing)

-faults T2-T3 in paper (>300 C)

-faults D1 in paper (tracking, arcing)

-faults T3 in oil (>700 C)

The less severe faults:

-faults PD/ D1 in oil (sparking)

-faults T1 in paper (

A popular ratio used to detect paper involvement is the CO2 / CO ratio.

If the CO2 / CO ratio is < 3, this is a strong indication of a fault in paper, either a hot spot or electrical arcing.

A fault in paper is generally considered as more serious than a fault in oil only, because paper is often placed in a HV area (windings, barriers).

The CO2 / CO ratio, however, is not very accurate, because it is also affected by the background of CO2 and CO coming from oil oxidation.

The amounts of furans in oil may also be used in some cases to confirm paper involvement, however, the interpretation of results is often difficult.

.-C2H2/ H2 : a ratio > 3 in the main tank indicates contamination by the LTC compartment

Other useful gas ratios:

-O2/ N2: a decrease of this ratio indicates excessive heating (< 0.3 in breathing transformers).

.Gassing not related to faults in service:

-Catalytic reactions on metal surfaces: formation of H2 only.

-Stray gassing of oil: the unexpected gassing of oil at relatively low temperatures (80 to 200 C): gassing of the T1 or T2 type.

-first limit is related to lab accuracy.

-second limit to economic reasons.

Minimum gas formation to attempt a diagnosis:

First limit: lab accuracy

The accuracy of the average CIGRE /IEC lab is ~ 15% at medium (routine) gas concentrations (> 10 ppm for hydrocarbons).

Its accuracy decreases to ~ 30% at 6 ppm, and 100% near the lab detection limit (2 ppm).

Effect of lab accuracies of 15 and 30% on DGA diagnosis uncertainty (in red and blue).

When an area of uncertainty crosses several fault zones in the triangle, a reliable diagnosis cannot be given.

This is particularly true for lab accuracies > 30%.

This applies not only to the triangle but to all diagnosis methods.

Diagnosis uncertainty corresponding to lab inaccuracies of 15, 30, 50 and 75 %:

How inaccurate are the laboratories at medium gas concentrations ?

How inaccurate areat low gas concentrations ?

Minimum gas concentrations to attempt a diagnosis.

If for example lab accuracy is 15% at medium gas levels (>10 ppm):

If some gases are < 6 ppm, diagnoses will be uncertain, and a calculation of diagnosis uncertainty should be done.

Commercial software is available for that purpose.

If lab accuracy is between 15% and 30%, diagnoses will be uncertain at all gas concentrations, and a calculation of diagnosis uncertainty necessary.

Above 30% or 50%, diagnoses become too uncertain.

Lab and gas monitor accuracies can be obtained by using gas-in-oil standards.

Such standards are available commercially.

Second limit: typical values

A recommendation of CIGRE and the IEC is that DGA diagnosis should be attempted only if gas concentrations or rates of gas increase in oil are high enough to be considered significant.

Low gas levels may be due to contamination or aging of insulation, not necessarily to an actual fault.

Also, there is always a small level of gases in service, and it would not be economically viable to suspect all pieces of equipment.

So, it is better to concentrate on the upper percentile of the transformer population with the highest gas levels.

This is the philosophy behind the use of 90% typical concentrations and 90% typical rates of increase, in order to concentrate maintenance efforts on the 10% of the population most at risk.

A consensus has been reached at CIGRE on typical values observed in service worldwide (CIGRE Brochure # 296, 2006).

Ranges of 90 % typical concentration values for power transformers, in ppm:

C2H2 H2 CH4 C2H4 C2H6 CO CO2

All transformers 50-150

30-130

60-280

20-90

400-600

3800-14000

No OLTC 2-20

Communicating OLTC

60-280

Ranges of 90 % typical rates of gas increase for power transformers, in ppm/year:


All transformers 35-132

10-120

32-146

5-90

260-1060

1700-10,000

No OLTC 0-4

Communicating OLTC

21-37

90% typical values are within the same range on all networks, with some differences related to individual loading conditions, equipment used, manufacturers, climate, etc.

Each individual network therefore should preferably calculate its own specific typical values.

Influence of some parameters on typical values:

-Typical values are significantly higher in young equipment (suggesting there are some unstable chemical bonds in new oil and paper ?). -A bit higher in very old equipment.

-Significantly lower in instrument transformers. -Higher in shell-type and shunt reactors (operating at higher temperatures ?).

-Typical values are not affected by oil volume (suggesting that larger faults are formed in larger transformers ?).

-Typical values are very similar in air-breathing and in sealed or nitrogen blanketed equipment, contrary to a common belief in the US.

90% typical values in California vs. CIGRE values, in ppm:


CIGRE/ IEC 2-20

50-150

30-130

60-280

20-90

400-600

3800-14000

California 3 96 88 57 79 613 5991

When DGA results in service reach typical values:

-a diagnosis may be attempted to identify the fault (if lab accuracy is good enough).

-the equipment should not be considered at risk.

-however, it should be monitored more frequently by DGA.

To evaluate how much at risk a transformer may become above typical values, the probability of failure in service (PFS) has to be examined.

PFS has been defined as the number of DGA analyses followed by a failure-related event (e.g., tripping, fault gas alarm, fire, etc), divided by the total number of analyses, at a given gas concentration.

90 98 99 Norm, in %

Probability of having a failure-related event ( PFS, % )vs. the concentration of C2H2 in ppm at HQ

100 300 400 ppm

PFS, in %

The PFS remains almost constant below and above the 90% typical value, until it reaches an inflexion point on the curve (pre-failure value).

DGA monitoring should be done more and more frequently as gas concentrations increase from typical to pre-failure value.

Pre-failure concentration values were found by CIGRE to be surprisingly close on different networks:

H2 CH4 C2H4 C2H6 C2H2 CO

240-1320

270-460

700-990

750-1800

310-600

984-3000

(in ppm)

This suggests that failure occurs when a critical amount of insulation is destroyed.

In-between typical and pre-failure values, specific alarm values can be defined, depending on the tolerance to risk of the maintenance personnel, and on the maintenance budget available.

For example, higher alarm values may be used when the maintenance budget is low, and lower alarm values in the case of strategic equipment.

Pre-failure rates of gas increase (slope 3) are in preparation at CIGRE.

Concentration

Time

Pre-failure rates of gas increase in power transformers, in ppm/ day


0.5 3 5 5 11 NS NS

On-line gas monitors

-are best suited for measuring rates of gas increase (trends).

-will detect faults between regular oil samplings.

-may now also provide on-line diagnosis.

The triangle can also be used to identify faults in tap changers.

z: Normal operation; z:Severe coking; {: Light coking;U: Heating;z: strong arcing D2; {: Arcing D1

Thanks a lot for your attention.

An Artificial Neural Networks An Artificial Neural Networks Approach to Transformer Approach to Transformer

Dissolved Gas Analysis and Dissolved Gas Analysis and Problem NotificationProblem Notification

Donald LamontagneDonald LamontagneSection LeaderSection Leader

T&D Reliability Analysis and ManagementT&D Reliability Analysis and ManagementArizona Public ServiceArizona Public Service

EPRI Substation Equipment Diagnostic Conference XIVEPRI Substation Equipment Diagnostic Conference XIVMarriott Hotel and MarinaMarriott Hotel and Marina

San Diego, CASan Diego, CAJuly 17, 2006July 17, 2006

AgendaAgenda

EventsEvents OnOn--Line DGA MonitoringLine DGA Monitoring Neural NetworksNeural Networks APS TOAN SystemAPS TOAN System ConclusionsConclusions Questions?Questions?

EventsEvents

WestwingWestwing

6/14/2004 and 7/4/2004 Events6/14/2004 and 7/4/2004 Events

6/14/20046/14/2004

Sustained fault on 230kV Westwing Sustained fault on 230kV Westwing Liberty lineLiberty line

One breaker failed to openOne breaker failed to open Initial fault split between three banksInitial fault split between three banks Communication error on breaker statusCommunication error on breaker status Last fault through one bank onlyLast fault through one bank only Post event DGA and Post event DGA and thermographythermography

Damaged TransformersDamaged Transformers

Five 500MVA, Single Phase, Five 500MVA, Single Phase, 525/230/13.8kV Autotransformers w/ LTC525/230/13.8kV Autotransformers w/ LTC

Westinghouse 1973 vintageWestinghouse 1973 vintage 14,500 gals of oil in the main tank14,500 gals of oil in the main tank

Damaged Phases

7/5/2004

Deer ValleyDeer Valley

7/20/2004 7/20/2004 T928 Type U bushing failureT928 Type U bushing failure 167MVA, three phase, 230/69kV167MVA, three phase, 230/69kV FPE 1978 vintageFPE 1978 vintage Bushing was Bushing was DobleDoble tested in 2002 with no tested in 2002 with no

issuesissues

Replacement T873Replacement T873

167MVA, three phase, 230/69kV167MVA, three phase, 230/69kV Westinghouse 1979 vintageWestinghouse 1979 vintage Removed from service 5/2004 for upgrade Removed from service 5/2004 for upgrade

to 188MVAto 188MVA Returned to service 7/25/2004 to replace Returned to service 7/25/2004 to replace

failed T928failed T928

T873 DGA ResultsT873 DGA Results

002233339917172131213159261592616276273/26/20043/26/2004

2635263561761770704464463922392241418068066625266252273427343/28/20053/28/2005

0033363637371313545410151015463746377507508/18/20048/18/2004

C2H2C2H2C2H4C2H4C2H6C2H6CH4CH4H2H2COCOCO2CO2N2N2O2O2

All gases from the 8/18/2004 sample were below the All gases from the 8/18/2004 sample were below the IEEE C57.104 Condition 1 levels IEEE C57.104 Condition 1 levels indicating the indicating the transformer was behaving normally.transformer was behaving normally.

The 3/28/2004 sample has H2, C2H4, C2H2 and TDCG The 3/28/2004 sample has H2, C2H4, C2H2 and TDCG at Condition 4 and CH4 at Condition 3.at Condition 4 and CH4 at Condition 3.

OnOn--Line DGA MonitoringLine DGA Monitoring

OnOn--Line DGA MonitoringLine DGA Monitoring

Began utilizing in the summer of 2003Began utilizing in the summer of 2003 Currently using Serveron Currently using Serveron TrueGasTrueGas and TM8 and TM8

modelsmodels Continuously sample eight gases (hydrogen, Continuously sample eight gases (hydrogen,

acetylene, methane, ethane, ethylene, CO, COacetylene, methane, ethane, ethylene, CO, CO22, , OO22) and report every four hours through gas ) and report every four hours through gas chromatographychromatography

Currently installed on fiftyCurrently installed on fifty--two 230kV and above two 230kV and above transformers and shunt reactors.transformers and shunt reactors.

Source: www.serveron.com

Laboratory Grade Gas Laboratory Grade Gas ChromatographyChromatography

11--3,000 3,000 ppmppm

Artificial Neural NetworksArtificial Neural Networks

Artificial Neural NetworksArtificial Neural Networks

A network of nodes and weighted A network of nodes and weighted connections, which are loosely analogous connections, which are loosely analogous to the neurons and synapses in the brain. to the neurons and synapses in the brain.

Each node sums the inputs from several Each node sums the inputs from several incoming weighted connections and then incoming weighted connections and then applies a transfer function to the sum. applies a transfer function to the sum.

The transfer function is a smooth, nonThe transfer function is a smooth, non--linear functionlinear function logistic functionlogistic function hyperbolic tangenthyperbolic tangent

Neural NetworksNeural Networks

InputInputLayerLayer

HiddenHiddenLayer 1Layer 1

HiddenHiddenLayer 2Layer 2

OutputOutputLayerLayer

ii22

ii11

iinn

Neural Network TrainingNeural Network Training

UnderfittingUnderfitting and and OverfittingOverfitting

xx

xx

x

x

xx xx xx

xx

x

x

xx xx xx

xx

x

x

xx xx

Underfitting Correct Fit Overfitting

APS TOANAPS TOAN(Transformer Oil Analysis and (Transformer Oil Analysis and

Notification)Notification)

Traditional AnalysisTraditional Analysis

Testing accuracy of traditional methodsTesting accuracy of traditional methods

32.4%32.4%24.8%24.8%42.9%42.9%IEC 599IEC 599

62.9%62.9%12.4%12.4%24.8%24.8%Rogers RatioRogers Ratio

11.9%11.9%65.2%65.2%22.9%22.9%DornenbergDornenberg RatioRatioNot IdentifiableNot IdentifiableErrorErrorSuccessSuccessDiagnosis MethodsDiagnosis Methods

APS TOANAPS TOAN

~ 114,000 DGA samples/year~ 114,000 DGA samples/year Utilizes VTs ANNEPS engine (w/ Utilizes VTs ANNEPS engine (w/

modifications)modifications) ANN combined with Expert SystemANN combined with Expert System

Tested at ~ 93% accuracy in predicting Tested at ~ 93% accuracy in predicting fault typefault type

Exception based processing systemException based processing system

APS TOANAPS TOAN

Some Modifications to Some Modifications to VTsVTs System:System: Gassing ratesGassing rates Nine vs. eight gasesNine vs. eight gases Minimum gas levelsMinimum gas levels Added a Polling EngineAdded a Polling Engine Added a Notification EngineAdded a Notification Engine

TOAN Provides AnswersTOAN Provides Answers

Who Who Transformer IDTransformer ID When When WhenWhen the sample was taken?the sample was taken? What What WhatWhat are the gas values and what are the gas values and what

type of fault is it?type of fault is it? How How How severe is it?How severe is it? Where Where WhereWhere is the fault likely located?is the fault likely located?

SERVERONTaken By

FC3 U4 GSU SO. XFMR 345Y/199.186-22KV, 308MVA 1-PDescription

T629 (Four Corners 350KV) [ Level = 1 : IMMEDIATE ATTENTION ]Transformer

6/5/2006 2:58:09 PM6/5/2006 1:00:00 PM7410

0.1676/5/2006 5:58:25 PM6/5/2006 5:00:00 PM7412

DaysSample Received DateSample DateSample ID

Example ReportExample ReportWho and WhenWho and When

Gas in Oil

-0.0930.01.01.0TCG%

-52.800-20.33335.7++ 3315.4TDCG

-36.221-21.42797.8++ 2776.4THG

37.9241062.058284.859346.8N2

-3.017-58.210671.610613.4O2

0.26014.16681.3++ 6695.4CO2

-9.1130.6503.2++ 503.8CO

-0.896-1.2342.7++ 341.5Ethane

-21.445-13.01888.4++ 1875.4Ethylene

0.063-8.924.6++ 15.7Acetylene

-21.2591.7542.1++ 543.8Methane

-13.8060.534.735.2Hydrogen

Rate (ppm/day)Delta

Previous Sample

Current Sample

WhatWhat

Fault Analysis

0.8330.9900.833Cellulose Degradation - CD

1.0000.9901.000Overheating of Oil - OHO

1.0000.9901.000Overheating - OH

0.0000.0100.000Low Energy Discharge - LED

0.0000.0100.000High Energy Discharge - HEDA

0.0000.0100.000Normal - NR

CombinedEPSANN

WhatWhat

Duval Analysis

2434.977.00.622.3

Total Gas% C2H4% C2H2% CH4

T3 - Thermal fault > 700degCDuval Diagnosis

Diagnosis

Degradation of cellulose involved.CD Diagnosis

Overheating of oil involved.OHO Diagnosis

Possible overheating of oil or cellulose.OH Diagnosis

LED Diagnosis

HEDA Diagnosis

HEDA Severity

Est. temp is above 700 c degrees.OH Temperature

Unit is ABNORMALSimple Criteria

Sample oil daily. Consider removal of unit from service. Advise manufacturer.

Recommended Action

Overall condition needs IMMEDIATE ATTENTION.Recommended Condition

1Previous Result

1Final Recommendation

WhatWhat

LocationFault Location Confidence

0.0000.0000.0001.0000.001

OtherWindingsBushings/LeadsCore/TankLTC

1-core/tank1-core/tank

Previous Fault LocationFault Location

WhereWhere

ConclusionsConclusions

Met our goal to build an exception based Met our goal to build an exception based systemsystem

Although accuracy is good (93%) APS is Although accuracy is good (93%) APS is researching and training improved researching and training improved ANNsANNs

ANNsANNs are capable of detecting the are capable of detecting the underlying, complex patterns of DGA and underlying, complex patterns of DGA and are a good partner with onare a good partner with on--line monitoringline monitoring

Questions?Questions?

Documents

82979891-F06-DGA