manuel avendaño j. v. milanović
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School of Electrical & Electronic Engineering. Methodology for Flexible, Cost -Effective Monitoring of Voltage Sags. Manuel Avendaño J. V. Milanović. Manchester, UK. Manuel Avendaño – UK – Session 2 – Paper 0529. What did we do?. - PowerPoint PPT PresentationTRANSCRIPT
Frankfurt (Germany), 6-9 June 2011
Manuel Avendaño J. V. Milanović
Manuel Avendaño – UK – Session 2 – Paper 0529
METHODOLOGY FOR FLEXIBLE, COST-EFFECTIVE MONITORING OF
VOLTAGE SAGS
School of Electrical & Electronic Engineering
Manchester, UK
Frankfurt (Germany), 6-9 June 2011
What did we do?
Proposed methodology for determining a range of best monitoring programmes for estimating the performance of sags with different characteristics.
Incorporated user-defined voltage sag characteristics and a measure of the overall accuracy of sag estimation.
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Presentation Outline
Why did we do it? (Importance and motivation) How did we do it? (Methodology) What did we get? (Results) What did we learn? (Conclusions)
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Why did we do it?
Knowledge of voltage sag incidence in the network can help in tailoring solutions to mitigate the consequences of sags.
Estimation of sag characteristics is required when measurements are not available.
Fault location method utilized directly influences the number of monitors.
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Why did we do it?
Sag monitoring programs (SMPs) should be focused on quantifying most critical sags− (E.g. SARFI-90%, SARFI-70%, SEMI F47, etc)
To provide a measure for assessing the sag estimation derived from a SMP− (Diff. between real and estimated events)
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
How did we do it?
Selection of monitor locations based on minimization of overall sag estimation error.
Utilization of existing fault location method.
Application in a generic distribution system (GDS) and comparison with an optimal placement method.
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Sag estimation error (SEE)
= total number of buses
= real number of sags below i.c. SEMI F47 at bus i
= estimated number of sags below i.c. SEMI F47 at bus i
, , 47 47
1( 47)
- N
i real i estimatedSEMI F SEMI F
iSEMI F
X XSEE
N
, 47
i realSEMI FX,
47i estimatedSEMI FX
N
SEMI F47 can be substituted by any other voltage-tolerance curve (CBEMA), performance index (SARFI), etc.
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
1. Set target value for SEE or number of monitors (stop criteria).
2. Simulate faults to obtain sag performance.
3. Perform fault location using voltage measurements of all buses.
4. Calculate SEE incurred by all buses.
5. Monitor location = min(SEE)
6. Repeat steps 3-5 until a stop criterion is fulfilled.
Monitor placement
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
What did we get?
An iterative search algorithm that is:
Flexible. One or multiple monitoring programmes can be determined for any kind of user-defined voltage sag characteristics.
Cost-effective. If technical and/or economic constraints limit the number of monitors to be deployed, a series of SMPs can be provided accordingly.
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Application
295-bus GDS, 278 lines, 37 transformers.
GDS equipment shut-down region below SEMI F47
5 10 15 20 25 30 35 40 45 500
20
40
60
80
100
Duration (cycles)
Pe
rce
nt
of
no
min
al v
olt
ag
e
GDSsag characteristics
Voltage level (kV)
Fault clearing time (cycles)
11 18
33 9
132 4.8
Bus faults 3.6
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Sag Monitoring Programmes
Number of monitors
SEE (number of sags)
SARFI-90 SARFI-80 SARFI-70 SEMI F47
1 876 873 876 541
2 459 459 459 218
3 255 255 313 117
4 118 118 119 62
5 22 23 24 16
6 11 12 13 5
7 5 10 12 3
8 3 8 8 1
9 1 6 6 0
10 0 0 0 0
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Reduction of sag estimation error
0
200
400
600
800
1000
1200
1 2 3 4
Sa
g E
sti
ma
tio
n E
rro
r
1 2 3 4
Manuel Avendaño – UK – Session 2 – Paper 0529
0
5
10
15
20
25
30
5 6 7 8 9 10
Number of monitors5 6 7 8 9 10
Frankfurt (Germany), 6-9 June 2011
Location of monitorsSMP – SARFI-90 Optimal monitoring
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Effects of robustness in fault location method on sag magnitude estimation
0 50 100 150 200 250 300
0.7
0.8
0.9
1
Buses
Vo
lta
ge
sa
g m
ag
nit
ud
e (
p.u
.)
Est. 5 monRealEst. 12 mon
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Comparison with optimal monitoring
0 50 100 150 200 250 3000
5
10
15
Buses
SA
RF
I-9
0 in
de
x
RealEst. 5 monEst. 12 mon
12 monitors optimally placed vs. 5 monitors placed with proposed approach.
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Comparison with optimal monitoring
0
5
10
15
20
25
30
1 2
Sa
g E
sti
ma
tio
n E
rro
r
OSMP (12 monitors)SMP (5 monitors)
Distribution of SEE for Monte Carlo simulations representing 100 years of system performance
Manuel Avendaño – UK – Session 2 – Paper 0529
Frankfurt (Germany), 6-9 June 2011
Conclusions
A methodology for determining a range of best voltage sag monitoring programmes is proposed.
DNOs can choose a sag monitoring programme specifically designed to estimate the performance of the sags more relevant to its customers.
Due to the fault location technique employed it is more robust than previous approaches.
Manuel Avendaño – UK – Session 2 – Paper 0529