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GE Energy

Transformer Monitoring:How Moving forward from Monitoring toDiagnostics can Positively Impact Indian

Business and Industry

Brian Sparling, SMIEEEGridTech 2007, DelhiFebruary 5-6, 2007


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2 /GE /

Feb 5-6, 2007GE Proprietary and Confidential

CoolingSystem

Core

Tap changer

CoilsTank

The TransformerA complex system

Oil

Control

Cabinet

Bushings


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3 /GE /


Monitoring vs. Diagnostics

Monitoring: function is to avoid unexpected transformerfailure and insure continuous normal operation

Diagnostic : Application of On-Line and Off-Linedevices & techniques to confirm and determine the

exact nature of the anomaly


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4 /GE /



No

8%

Diagnostic

2%Yes

Maintenance &Repair

Dosomething

else

Monitoring

10%NoIs itNormal ?

Transformer

Donothing

else

Yes

90%

Is it Serious ?

Broadband techniqueapplied routinely

Focused techniqueapplied as required

Maintenance &Repair Shop

Cigre Report No. 227, Life Management Techniques for Power Transformers. WG A2.18


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5 /GE /



Over the last 10 to 15 years;

Function Specific sensors, gas-in-oil, temperaturemonitoring, have evolved from dumb sensors tomicroprocessor based devices, commonly called

Intelligent Electronic Devices (IEDs)Expanded the capability of these systems to perform

more data processing at the point of measurement

Along with this, IEDs have the capability for datacommunication


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Measure multiple parameters

Support multiple calculations and modelsProvide a more complete view of the condition of aspecific type of equipment

Examples of equipment level monitors: Load Tap Changer monitors (thermal, operational,

acoustic signature)

Transformer monitors (gas content, multiplemodels, partial discharge, hot spot)

Bushing monitors


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7 /GE /


Broadband monitoring technique forMain tank

Gas-in-oil

Oil is in contact with every component in the main tank.

If a fault occurs in a component, oil will be degradedand gases will be generated

A sudden increase of dissolved gas level is the bestindicator of a developing incipient fault


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Failure Avoidance

When the insulation systemis stressed,

KEY fault gases are produced

and they will dissolve in the oilHydrogen from oil

Carbon Monoxide from paper


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9 /GE /


The HYDRAN

Technology

Detection and Monitoring of Key Fault Gases inoil

Responds Mainly to H2 and CO

Detects a deviation From the Base line

Continuously monitors the Evolution of the

gases in the Transformers, any sudden increaseis an indication of an incipient fault


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10 /GE /


HYDRAN Fault Detection

Detection of a core Hot Spot

Low Level evolution

0

5

10

15

20

25

30

35

40

45

50

1-Dec 15-Dec 29-Dec 12-Jan 26-Jan 9-Feb 23-Feb

Date

HYDRAN201iReading

PP

Transformer fault


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11 /GE /


Broadband monitoring technique forMain tank

Moisture-in-oil

Moisture degrades paper

Moisture reduces dielectric strength

Moisture ages transformer fasterMoisture is everywhere

Moisture is a key element to monitor, especially in the

solid insulation (paper)DGA testing or monitoring cannot help with moistureevaluation


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12 /GE /


Impact of moisture in Paper

The amount of water in paper is a veryimportant parameter to know, as it directlydetermines the following:

Aging rate of the winding insulation Bubbling temperature (limits the amount of

overloading of a transformer) Dielectric resistance of the barriers at the

bottom of the winding


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13 /GE /Feb 5-6, 2007GE Proprietary and Confidential

Impact of moisture in Paper

Winding

44 6456 76

Temperature (oC)

3.32.21.71.2

Moisture content (%)

Oil

Winding

insulatio

n

Thinbarrie

rs

Guided convection flow

through disk windings

Area of interest for winding insulation

Area of interest for barrier insulation


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Moisture and Bubbling Model

Water condensation temperature

Winding bubbling temperature

Bubbling temperature margin,alarm point

Absolute water content in oil


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Winding Hot SpotCooling Efficiency

Aging

Cooling Status

Gas LevelWater LevelMoisture in Paper

Load


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Moisture and Bubbling ModelIs this transformer behaving normally?


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Predictive Modeling:

Anomaly DetectionPremise:

By modeling the steady-state behavior of the transformer usingmultivariate analysis, we can deduce an impending fault in that sametransformer as its behavior changes over time.

This analysis will generate an alarm before a univariable control limitwould be exceeded

Application: Turn forced outages into planned outages

Typical Detectable Faults:

Incipient Fault Detection of Insulation System Failure

Loss of Cooling Efficiency Degradation of Tap Changer moving parts (gear slop)

Any class of fault that occurs over time (non-instantaneous)


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Compare static model against onlinedata

Compare current behavior (relationships between sensors) toprevious behavior. Determine if the current behavior is normal basedon data that has been previously sampled.

If the behavior appears abnormal, what are the contributing sensors?


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PCA

Observation #337 approaches

99th percentile Confidence Interval.

Why?


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Predictive Modeling:Model Error Contributions

Tap Changer Position & Temperature profile

Differs from what model is accustomedto seeing


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22 /GE /


Diagnostic Modeling:Bayesian Belief Networks

Premise:

The failure modes of transformer components are well known.

The relative failure probabilities are also known. It should bepossible to build a model that will link failure modes andeffects to the underlying damaged component. By adding testresults (findings) to the model, the model diagnostics can berefined until the faulted component can be inferred with a high

probability of accuracy.> Approach:

FMEA process employed to enumerate transformer failuretypes (faults) and their relative frequencies

Assist Maintenance personnel to quickly isolate transformerfaults


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23 /GE /


Diagnostic Modeling:Bayesian Belief Networks


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24 /GE /


Summary

Benefits of a combined early fault detection anddiagnostic system

Better knowledge of the operating condition of the fleetEarly warning of incipient faults on any of the 5 components ofthe transformer

Better management of capital and allocating resources toimprove the network development

Improved predictability of network performance

Early detection & correct diagnosis of a problem, saves money


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25 /GE /


Summary

Every transformer has its own normal behavior,in operation, and very different behavior when

failing.

Like in medicine, early diagnosis oftenavoids severe pain or worse

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