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TRANSCRIPT
Line Impedance Estimation
Using SCADA Data
Presenter: Ramiro Da Corte - Power System Engineer
Prepared by: James Shen - Principal Engineer, AESO
Nov. 5, 2014
2
Background
• AESO is responsible for grid reliability using real-time
power flow analysis.
• Lines and transformers parameters are important for the
power flow results. Impedances are from TFO to the
AESO EMS system. Not like transformers, it is difficult to
test and validate a line impedance.
• The parameters of a line are calculated based on
construction information. It will be helpful to actually
verify the calculated parameters.
• All existing line impedance estimation are phasor based.
This project collaborates with U of A to use EMS
SCADA measurements for estimating the impedances.
Methodology of Impedance Estimation
• U of A researchers discovered a way to estimate a line
impedance using only the SCADA measurements at both
ends of a line, plus line length data
• SCADA measurements:
• Voltage magnitude (Vrms) without angle
• Active power (P)
• Reactive power (Q)
• Line impedance results: R, X, G and B
• To reduce the noise impact from SCADA measurement,
multiple points of time are used to average the calculated
impedance results
3
Project Data
• Five lines have been selected for the impedance estimation
• Two days’ SCADA measurements with 5 sec. interval were
used
– Winter peak date in 2013
– Summer peak date in 2014
– SCADA data at two ends of the line are
• Voltage magnitude (Vrms)
• Active power (P)
• Reactive power (Q)
• Line lengths are also required
4
Line Impedance Model
5
G, the shunt conductance representing corona loss,
can also be estimated using the U of A algorithm
Impedance Estimation
• R = (Ps + Pr – (Vs2 + Vr
2) x G/2) / (Irms)2
• X = (Qs + Qr + (Vs2 + Vr
2) x B/2) / (Irms)2
Where G is shunt conductance and B is shunt susceptance
• Above two equations are for R and X estimation
• Considering SCADA measurements random errors for P, Q
and V, the estimation results can vary.
• Irms is the denominator, which means the impact of error
decreases with increased Irms. Therefore, larger load current
can be used to filter results.
6
Estimation Results
Line 1 data: summer and winter MW, MVar and kV
7
0 2000 4000 6000 8000 10000 12000 14000 16000 1800050
100
150
MW
0 2000 4000 6000 8000 10000 12000 14000 16000 1800020
40
60
MV
ar
0 2000 4000 6000 8000 10000 12000 14000 16000 18000252
254
256
258
kV
Sending end-summer
Receiving end-summer
Sending end-winter
Receiving end-winter
Line 1 Estimated Results
• Original estimated results
• Sorted by Current
8
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
5
10
15
p.u
.
summer
R-est
X-est
R-ref
X-ref
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
5
10
15
p.u
.
winter
R-est
X-est
R-ref
X-ref
0.32 0.34 0.36 0.38 0.4 0.42 0.44 0.46 0.48 0.50
5
10
15
Irms
(kA)
p.u
.
summer
R-est
X-est
R-ref
X-ref
0.32 0.34 0.36 0.38 0.4 0.42 0.44 0.46 0.48 0.50
5
10
15
Irms
(kA)
p.u
.winter
R-est
X-est
R-ref
X-ref
Final Estimated Impedance
Given Impedance Summer Est. Winter Est.
Line 1 - R 1.31 2.56 ±0.37 1.42 ± 0.23
Line 1 – X 5.13 9.15 ± 0.06 8.63 ± 0.13
Line 2 – R 0.73 1.44 ± 0.11 1.60 ± 0.08
Line 2 - X 3.69 3.87 ± 0.11 3.89 ± 0.09
Line 3 - R 0.78 / 0.59 ± 0.09
Line 3 – X 4.80 / 3.55 ± 0.08
Line 4 – R 0.78 / 0.74 ± 0.07
Line 4 - X 4.80 / 3.85 ± 0.08
Line 5 – R 4.0 / 3.84 ± 0.50
Line 5 – X 9.61 / 10.56 ± 1.69
9
Impedance Estimation by Fault Recorder
• The impedance estimation by SCADA also has been
crosschecked by impedance estimation using measurements
from Fault Recorder.
• The fault recorder data contain waveforms so phasor
information can be extracted to calculate line impedance.
10
0.6 0.7 0.8 0.9 1
-100
0
100
Waveform of Phase C Voltage at 207s
t [s]
Vo
ltag
e [k
V]
0.6 0.7 0.8 0.9 1
-100
0
100
Waveform of Phase C Voltage at 235s
t [s]
Volt
age
[kV
]
0.6 0.7 0.8 0.9 1-5
0
5Waveform of Phase C Current at 207s
t [s]
Curr
ent
[kA
]
0.6 0.7 0.8 0.9 1-5
0
5Waveform of Phase C Current at 235s
t [s]
Cu
rren
t [k
A]
Estimation Comparison
- SCADA and Fault Recorder
Line 903L R X
Value used for load flow case 1.45 9.08
SCADA (Summer peak) 1.48 ± 0.05 9.03 ± 0.08
SCADA (Winter peak) 1.17 ± 0.05 9.09 ± 0.10
SCADA (Fault day) 1.45 ± 0.06 9.03 ± 0.21
Fault Recorder 1.41 8.94
11
Line 925L R X
Value used for load flow case 1.68 9.70
SCADA (Summer peak) 2.14 ± 0.27 9.61 ± 0.38
SCADA (Winter peak) 1.74 ± 0.14 9.78 ± 0.31
SCADA (Fault day) 1.63 ± 0.17 9.57 ± 0.30
Fault Recorder 1.70 9.68
Application of Line Impedance Estimation
• In the AESO, EMS system has real-time State Estimation to
check if the power flow will have a valid solution.
• Big line impedance error will cause big delta between
SCADA measurements and State Estimation solution.
• State Estimation can screen out suspected parameter or
SCADA measurements errors.
• Impedance estimation using SCADA data can verify
suspected parameter errors from model, to improve the
accuracy of power flow solution.
• Potentially this impedance estimation can be built as an
application in real-time EMS system for State Estimation
tuning.
12
Collaborative Experiences
• The AESO EMS is in a project to integrate power system model between
EMS and Planning by mapping facilities, including facility impedances
checking.
• APIC seminar at the AESO in 2013 provided trigger for this project, when
presenter from U of A talked about the innovated way to estimate
impedance using SCADA data.
• At a short meeting by both sides, it was agreed that this initiative is a
good match. The AESO then reviewed U of A’s estimation methodology,
and decided to co-operate with U of A, by providing project source data
and reviewing the results.
• In future, the AESO will consider to use this program from U of A to check
suspected line impedance in EMS model. U of A will develop the software
for use by the AESO (and other APIC companies).
• U of A plans to investigate the change of line impedance with
temperature and the variation G-loss with time. The findings might be
useful for condition monitoring of lines. 13
Summary
• In the study cases, estimated line parameters are very close
to the given value.
• Winter peak data is more suitable for line parameter
estimation, as its load is larger. This conclusion is consistent
with the finding that the larger load indicates better
estimation.
• SCADA measurements used for line parameters estimation
is crosschecked by fault recorder data and it is found that
they are close. Therefore proposed algorithm using SCADA
data for line impedance estimation is valid.
• SCADA measurement errors in the estimation is acceptable.
• This estimation approach will help to verify suspected lines
impedances in data model.
14
Thank you