an expert system for condition assessment of acsr conductors · spatial thermal aging. . . . ....
TRANSCRIPT
![Page 1: An Expert System for Condition Assessment of ACSR Conductors · Spatial thermal aging. . . . . Pollution Index. . .. . . . Expert System. .. Summary Introduction Motivation Power](https://reader030.vdocuments.mx/reader030/viewer/2022040600/5e8e219b99e32d374468daf0/html5/thumbnails/1.jpg)
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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
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. .Introduction
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. . . . .Pollution Index
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Expert System. ..
Summary
Introduction
Outline
Motivation
Spatial Thermal Aging Analysis
Pollution Index
Pollutant data collectionImpact of wind direction
Proposed Expert System
Conclusion & Future Work
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. .Introduction
. . . . . . .Spatial thermal aging
. . . . .Pollution Index
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. . . .
Expert System. ..
Summary
Introduction
Outline
Motivation
Spatial Thermal Aging Analysis
Pollution Index
Pollutant data collectionImpact of wind direction
Proposed Expert System
Conclusion & Future Work
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. .Introduction
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. . . . .Pollution Index
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Expert System. ..
Summary
Introduction
Outline
Motivation
Spatial Thermal Aging Analysis
Pollution Index
Pollutant data collectionImpact of wind direction
Proposed Expert System
Conclusion & Future Work
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. .Introduction
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. . . . .Pollution Index
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. . . .
Expert System. ..
Summary
Introduction
Outline
Motivation
Spatial Thermal Aging Analysis
Pollution Index
Pollutant data collectionImpact of wind direction
Proposed Expert System
Conclusion & Future Work
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. .Introduction
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. . . . .Pollution Index
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Expert System. ..
Summary
Introduction
Outline
Motivation
Spatial Thermal Aging Analysis
Pollution Index
Pollutant data collectionImpact of wind direction
Proposed Expert System
Conclusion & Future Work
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. .Introduction
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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.
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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.
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Expert System. ..
Summary
Thermal aging analysis
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
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Thermal aging analysis
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.
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Expert System. ..
Summary
Thermal aging analysis
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.
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Summary
Thermal aging analysis
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%.
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Expert System. ..
Summary
Thermal aging analysis
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)
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Expert System. ..
Summary
Thermal aging analysis
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]
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Expert System. ..
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Thermal aging analysis
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 [%]
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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.
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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
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Summary
Pollution data
Impact of wind direction
N
2
d2
d3
Tower Span
Facility
Wind direction
d11
3
wd1
wd2
wd3
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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
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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
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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.
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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⟩
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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
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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.
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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
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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
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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
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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.
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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.
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Summary
Thanks
Thanks.
Questions ???
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