An Expert System for Condition Assessment of ACSR Conductors · Spatial thermal aging. . . . . Pollution Index. . .. . . . Expert System. .. Summary Introduction Motivation Power

. . . . . .

. .Introduction

. . . . . . .Spatial thermal aging

. . . . .Pollution Index

. . .

. . . .

Expert System. ..

Summary

An Expert System for Condition Assessment ofACSR Conductors

Md. Mafijul Islam BhuiyanDr. Petr Musilek

Jana Heckenbergerova

University of Alberta

October 05, 2011

1 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Introduction

Outline

Motivation

Spatial Thermal Aging Analysis

Pollution Index

Pollutant data collectionImpact of wind direction

Proposed Expert System

Conclusion & Future Work

2 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Introduction

Outline

Motivation


Pollution Index




3 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Introduction

Outline

Motivation


Pollution Index




4 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Introduction

Outline

Motivation


Pollution Index




5 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Introduction

Outline

Motivation


Pollution Index




6 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Introduction

Motivation

Power transmission companies are instigated to elevate thetransmitted load to meet increasing power needs ofindustrialized and urbanized consumers.

Increased loads impose thermal stress, causing risks oftransmission reliability and human safety.

Pollutants emitted from different facilities are responsible forchemical aging of transmission lines.

Conventional mathematical tools are not suitable for makingrelationships between these uncertain, ill-defined, andnon-linear parameters.

7 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Thermal aging analysis

Conductor thermal loading

Steady-state heat balance equation of a transmission line

.

...... qc +qr = qs + I2 ·R(Tc)

Required information

Physical characteristics such as type and size of the conductors.

Elevation and geospatial location.

Load profiles based on current or historical dataset.

Weather information that includes historical records of temperature,wind direction and speed, solar radiation, precipitation rate, etc.

8 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary


Point analysis method

Weather Parameters (NARR Data)

IEEE 738 standard Line

Temperature

Quantizing Line

Temperature

CumulativeLoS [%] for Al strand

(LAl)

Total LoS [%] for ACSR conductor

(LC)

Conductor Physical

Parameters

Geospatial Data

Step I Step II Step III & IV Step V

9 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary


Cumulative loss of strengthLoss of strength for a single aluminum strand

.

...... LAl = 100− (−0.24Tc + 134) · t−1.6

0.63·d (0.001·Tc−0.095)

Graphical method

100

101

102

103

0

5

10

15

Time t, [Hrs]

Loss

of s

tren

gth

[%]

Line Current: 1332 ALoss of Strength (L

Al): 10.048

%

128C

124C

121C

117C114 C

110 C

107 C

104 C

100 C

97 C

Temp [°C] Time [hrs]97 144100 177104 153107 141110 129114 102117 75121 63124 45128 30

Harvey, J. R., Effect of elevated temperature operation on the strength of aluminum conductors,IEEE Trans. Power apparatus and systems, Vol. PAS-91, PP. 1769-1772, Sep/Oct 1972.

10 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary


Cumulative loss of strengthLoss of strength for a single aluminum strand

.

...... LAl = 100− (−0.24Tc + 134) · t−1.6

0.63·d (0.001·Tc−0.095)

Graphical method

100

101

102

103

0

5

10

15

Time t, [Hrs]

Loss

of s

tren

gth

[%]

Line Current: 1332 ALoss of Strength (L

Al): 10.048

%

128C

124C

121C

117C114 C

110 C

107 C

104 C

100 C

97 C

Temp [°C] Time [hrs]97 144100 177104 153107 141110 129114 102117 75121 63124 45128 30

Harvey, J. R., Effect of elevated temperature operation on the strength of aluminum conductors,IEEE Trans. Power apparatus and systems, Vol. PAS-91, PP. 1769-1772, Sep/Oct 1972.

11 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary


Total loss of strengthTotal strength before annealing

S =π4(r2

al ·Sal ·nal + r2st ·Sst ·nst

)Total strength after annealing

S′=

π4

[(1− Lal

100

)·r2

al ·Sal ·nal + r2st ·Sst ·nst

]Total percentage loss of strength of compound conductor

Lcond =

(S−S

′

S

)·100%.

12 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary


Case Study

Sample power transmission line,5L011

Location: British ColumbiaProvinceLength: 330 kmNorth-end: Prince GeorgeSouth-end: Kelly LakeConductor: Finch (ACSR)

Weather data

North American RegionalReanalysis (NARR)Duration: 5 years (1/2005 -12/2009)

13 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary


Weekly load profile

Assumed weekly load profile with mean equals to nominalcurrent.

0 MO 24 TU 48 WE 72 TH 96 FR 120 SA 144 SU 168800

900

1000

1100

1200

1300

1400

1500

Time [hrs]

Line

cur

rent

[A]

14 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary


Spatial thermal agingSpatial distribution of cumulative thermal aging over 5 yearsperiod.

0 100 200 300 400 500 600 700 8000

0.2

0.4

0.6

0.8

1

Tower Number

Loss

of S

tren

gth

L c [%]

15 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Pollution data

Pollutant data collection

Pollution data were collected from National Pollutant ReleaseInventory (NPRI), 2008.

Data were rolled over five times assuming that the pollutionprofiles are constant.

Total amount of pollutants were estimated for each towerspan after processing pollutant data.

16 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Pollution data

Pollutant data processing

Step I Step II Step III Step IV

Identification of facilities emitting Sulpher, Amonia,

Chloride

Computation of total amount of

pollutant

Determining geospatial location of facility

Applying Sugeno fuzzy model

Impact of wind direction

Total amount of pollutant at each

tower span

Pollution index applying

normalization

Step V

17 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Pollution data

Impact of wind direction

N

2

d2

d3

Tower Span

Facility

Wind direction

d11

3

wd1

wd2

wd3

18 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Pollution data

Sugeno fuzzy model

Sugeno fuzzy model was applied to estimate the amount ofpollutant at each tower span

The threshold distances were set to 5, 10, and 13 Km basedon expert opinion.

Proposed Sugeno fuzzy system:

IF x is near THEN z = a · yIF x is medium THEN z =

( xb −1

)· c · y

IF x is far THEN z = (1−µ(x)) · d · y

19 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Pollution data

Spatial pollution index

0 100 200 300 400 500 600 700 8000

2

4

6

8

10

Tower Number

Pol

lutio

n In

dex

20 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Expert System

Schematic diagram of the expert system

Defuzzified output Deterioration grade

Fuzzy Inference system

Input Parameters 1. LoS (%) 2. Environmental pollution 3. Conductor configuration

Fuzzy rules Database

Fuzzy database contains the membership functions anduniverse of discourses of input and output parameters.

21 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Expert System

Classification of deterioration levels

Level Interpretation Membership function

No Deterioration Almost new conductor µ ⟨0,0,1.5,4⟩Minor Deterioration Normal condition µ ⟨2,3,5.5⟩Partial Deterioration Regular line inspection and maintenance µ ⟨3,5.5,7⟩Severe Deterioration Immediate reconductoring µ ⟨6,8,10,10⟩

22 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Expert System

Fuzzy inference system

Min Operation (antecedents) Larsen implication (consequents)

LoS (%)

Environment Pollution

Conductor configuration

Rule 1

Rule 2

Rule 3

Rule 4

Rule 60

ΣMax (Agg)

Defuzzification (COG/MOM)

Deterioration Grade

23 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

System validation

Fuzzy system validation

Initially, 60% of the available data points was chosen randomlyto develop the systemSeveral iteration processes were implemented (COG, MOM)for setting the parameters of membership functionsComapred the data with desired values estimated by a domainexpert using nonlinear regression model.

24 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

System validation

Deterioration grades of training dataset

Applying center of gravity (COG) defuzzification method

0 50 100 150 200 250 300 350 400 450 5002

3

4

5

6

7

Training Data set

Det

erio

ratio

n G

rade

Predicted OutputDesired OutputApplying COG defuzzification method

25 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

System validation

Deterioration grades of training dataset

Applying mean of maxima (MOM) defuzzification method

0 50 100 150 200 250 300 350 400 450 5002

3

4

5

6

7

Training Data set

Det

erio

ratio

n G

rade

Predicted OutputDesired OutputApplying MOM defuzzification method

26 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

System validation

Deterioration grade of entire transmission line

Applying center of gravity (COG) defuzzification method

0 100 200 300 400 500 600 700 8002

3

4

5

6

7

Tower Number

Det

erio

ratio

n G

rade

Predicted OutputDesired OutputApplying COG defuzzification

method

27 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Conclusion & Future work

Conclusion

A novel paradigm of assessing current condition of powerconductor has been introduced.

The proposed expert system can compute the deteriorationgrade of conductor based on thermal and chemical aging.

The proposed model was validated using a sample overheadtransmission line in the interior of British Columbia, Canada.

28 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Conclusion & Future work

Future work

Further validation of the model will be performed by acquiringspatial field data to optimize the expert system.

Aging behavior of other electric components due to industrialpollution will be considered.

Impact of wind speed and stack height of a facility will betaken into consideration to compute pollution index.

29 / 30

. . . . . .

. .Introduction



. . .

. . . .

Expert System. ..

Summary

Thanks

Thanks.

Questions ???

30 / 30

Documents

An Expert System for Condition Assessment of ACSR Conductors · Spatial thermal aging. . . . . Pollution Index. . .. . . . Expert System. .. Summary Introduction Motivation Power