Challenges in Evaluating Smart Grid Solutions:...

Breakout Session: Synchrophasor Systems, PMUs & PDCs, GPS

Glauco Taranto, COPPE/UFRJ, Brazil, Session Chair

Le Xie, TAMU, Session Co-Chair

Challenges in Evaluating Smart Grid Solutions: Metric, Testing,

Certification, Modeling and Simulation, Testbeds

Topics:

Evaluation of Synchrophasor Applications

Evaluation of Data Analytics

Evaluation of Synchrophasor Infrastructure

Breakout Session: SynchrophasorSystems, PMUs & PDCs, GPS

Outline

• Evaluation of Synchrophasor Applications- General issues - Example: collaboration between institutions in Brazil, and COPPE& A&M on new applications

• Evaluation of Data Analytics- General issues- Example: Collaboration between A&M researchers on

PMU data handling• Evaluation of Synchrophasor Infrastructure

- General issues- Example: Collaboration between A&M, WSU, UIUC

and GaTech on lifecycle management tools

Phasor Measurement Units(Synchrophasors)

• PMU

– First experiments back to the seventies

• The pioneers: Profs. Phadke and Thorp

– In Brazil: MedFasee Project UFSC (2003)

• Technology for synchronization

– Loran-C (in the past – 100 kHz)

– GPS (presently – 2 MHz)

• Applications

– Monitoring (low sampling rate) M-Class – IEEE Std C37.118.1-2011

– Protection and Control (high sampling rate) P-Class

Outline• Evaluation of Synchrophasor Applications:

- General issues

- Example: collaboration between institutions in

Brazil, and COPPE& A&M on new applications

• Evaluation of Data Analytics

- General issues

- Example: Collaboration between A&M researchers on

PMU data handling

• Evaluation of Synchrophasor Infrastructure

- General issues

- Example: Collaboration between A&M, WSU, UIUC


COPPE/UFRJ Background with PMU

Sweden - Royal Institute of Technology

Brazil – Analysis of Steady-State CurrentTransformer Errors in the Accuracy of PMUs

Italy – Application on Voltage Instability Detection

Uruguay – Application on Out-of-Step Protection

The MedFasee Project

22 Low-voltage PMUs at University Sites

The Voltage InstabilityAwareness Application

• Developed in collaboration with CESI (Italy)

• Being tested with actual data

8

Hasle

SwedenNorway

BRAZIL NORWAY


- General issues




- General issues


PMU data handling


- General issues



Project Information

10

Research Topics for Collaborations

11

Streamlined collaboration will provideenhanced laboratory features (marked in dots)

12


- General issues



• Evaluation of PMU Data Analytics

- General issues


PMU data handling


- General issues



Challenges in Real-time PMU Processing

14

1. Tennessee Valley Authority (TVA) 120 PMUs produces 36GB

data per day [4].

2. One phasor data concentrator (PDC) collecting data from 100 PMUs of 20 measurements each at 30 Hz requires over 50

GB/day storage [5].

3. State-of-the-art: primarily post-event analysis

4. Online application for oscillation monitoring: 10+ seconds delay

[4] N. Dahal, R. King, and V. Madani, “Online dimension reduction of synchrophasor data,” 2012.

[5] M. Patel, S. Aivaliotis, E. Ellen et al., “Real-time application of synchrophasors for improving reliability,” 2010.

Cumulative variance for bus frequency and voltage magnitude data.15

Dimensionality Reduction -- PCA

ERCOT1 2 3 4 5 6 7

99.7

99.75

99.8

99.85

99.9

99.95

100

Number of PCs

Perc

enta

ge (

%)

(a) Cumulative Variance for Bus Frequency in Texas Data

1 2 3 4 5 6 7

30

40

50

60

70

80

90

100

Number of PCs

Perc

enta

ge (

%)

(b) Cumulative Variance for Voltage Magnitude Vm

in Texas Data

1 2 3 4 5 6 799.7

99.75

99.8

99.85

99.9

99.95

100

Number of PCs

Perc

enta

ge (

%)

(a) Cumulative Variance for Bus Frequency in Texas Data

1 2 3 4 5 6 7

30

40

50

60

70

80

90

100

Number of PCs

Perc

enta

ge (

%)


in Texas Data PJM2 4 6 8 10 12 14

99.4

99.5

99.6

99.7

99.8

99.9

100

Number of PCs

Perc

enta

ge (

%)

(a) Cumulative Variance for Bus Frequency in PJM

1 2 3 4 5 6 7 855

60

65

70

75

80

85

90

95

100

Number of PCs

Perc

enta

ge (

%)

(b) Cumulative Variance for Voltage Magnitude in PJM

2 4 6 8 10 12 1499.4

99.5

99.6

99.7

99.8

99.9

100

Number of PCs

Perc

enta

ge (

%)

(a) Cumulative Variance for Bus Frequency in PJM

1 2 3 4 5 6 7 855

60

65

70

75

80

85

90

95

100

Number of PCs

Perc

enta

ge (

%)

(b) Cumulative Variance for Voltage Magnitude in PJM

BPA0 5 10 15 20 25 30 35 40 45

99.5

99.6

99.7

99.8

99.9

100

Number of PCs

Perc

enta

ge (

%)

(a) Cumulative Variance for Bus Frequency in 20130728 Oscillation Data

0 5 10 15 20 25 30 35 40 4560

65

70

75

80

85

90

95

100

Number of PCs

Perc

enta

ge (

%)


in 20130728 Oscillation Data

0 5 10 15 20 25 30 35 40 4599.5

99.6

99.7

99.8

99.9

100

Number of PCs

Perc

enta

ge (

%)

(a) Cumulative Variance for Bus Frequency in 20130728 Oscillation Data

0 5 10 15 20 25 30 35 40 4560

65

70

75

80

85

90

95

100

Number of PCs

Perc

enta

ge (

%)


in 20130728 Oscillation Data PSS/E 0 5 10 15 20 2599.975

99.98

99.985

99.99

99.995

100

100.005

Number of PCs

Perc

enta

ge (

%)

(a) Cumulative Variance for Bus Frequency in PSS/E Data

0 5 10 15 20 2550

60

70

80

90

100

Number of PCs

Perc

enta

ge (

%)


in PSS/E Data

0 5 10 15 20 2599.975

99.98

99.985

99.99

99.995

100

100.005

Number of PCs

Perc

enta

ge (

%)

(a) Cumulative Variance for Bus Frequency in PSS/E Data

0 5 10 15 20 2550

60

70

80

90

100

Number of PCs

Perc

enta

ge (

%)


in PSS/E Data

© 2015 Le Xie

1616

Scatter Plot of PMU

© 2015 Le Xie

Normal Condition

Abnormal Condition

Back to Normal Condition

ERCOT BPA

17

Corporate PDCData

Storage

Synchrophasor Data

Dimensionality Reduction

Data

Storage

Early Event Detection

: Phasor measurement unit

PDC: Phasor data concentrator

: Raw measured PMU data

: Preprocessed PMU data

Local PDC Local PDCLocal PDC

Early Event Detection Algorithm

Adaptive Training

PCA-basedDimensionality Reduction

Robust Online Monitoring

Online Detection

PMU Measurement sfdfffa

Covariance Matrix ga

Reorder va Eigenvalues

Select fa PCs, ggagga

Project jfj in m-D Space

Define Base Matrix ags

Calculate sh

Approximate grffs

Approximation error gfsgf

Event indicator grss

Alert to

System

Operators

0n NY t

YC

BY

iv

N

m m N

ˆi

y t

i

e t

i

t

YES

1t t

NO

Y

Early Event Detection Algorithm

?i

t

Event

Detected!0

?t t upT T

NO

YES

Update

Theoretically justified using linear dynamical system theory [6].

[6] L. Xie, Y. Chen, P. R. Kumar, “Dimensionality reduction of synchrophasor data: linearized analysis

and online applications,” IEEE Trans. Power Systems, Nov 2014. © 2015 Le Xie


- General issues




- General issues


PMU data handling


- General issues



Synchrophasor lifecycle

19

New Tools are needed

Synchrophasors• Certification• Field commissioning• Periodic maintenance

testing• Detecting

abnormalities• Troubleshooting


- General issues




- General issues


PMU data handling


- General issues



PMU CertificationLead: Mladen Kezunovic, Texas A&M University

End-to-end testsLead: Mladen Kezunovic, Texas A&M University

Application testIntegration of gold “PMU for In-service testsLead: Mladen Kezunovic, Texas A&M University

Discussion• What kind of applications that will enhance the

resiliency of the system should be addressed?

• Do we have a killer application of synchrophasor systems for smart grid solution?

• Do we have adequate tools for lifecycle management?

• What are they and why we need them?

• How do we evaluate synchrophasor solutions using the proposed evaluation tools?

THANKS!

[email protected]

http://smartgridcenter.tamu.edu/sgc/

[email protected]

http://smartgridcenter.tamu.edu/sgc/

Challenges in Evaluating Smart Grid Solutions:...

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