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