Wide-Area Transmission System Data Analysis and Visualization

Tom Overbye Texas A&M University overbye@tamu.edu

July 18, 2017

1

Acknowledgments • Work presented here has been supported by a variety

of sources including PSERC, DOE, ARPA-E, NSF, EPRI, BPA, llinois Center for a Smarter Electric Grid and PowerWorld. Their support is gratefully acknowledged!

• Slides also include contributions from TAMU and UIUC graduate student and engineers including Komal Shetye, Sudipta Dutta, Saurav Mohapatra, Trevor Hutchins, Adam Birchfield, Ti Xu, Kathleen Gegner, and Iyke Idehen

• Thanks for human factor aspects from Prof. Esa Rantanen, Rochester Institute of Technology

2

Overview • Power system operations and planning are generating

more data than ever – In operations thousands of PMUs are now deployed – In planning many thousand of studies are now routinely run,

with a single transient stability run creating millions of values

• How data is transformed into actionable information is a crucial, yet often unemphasized, part of the software design process

• Presentation addresses some issues associated with dealing with this data

3

Visualization Software Design • Key question: what are the desired tasks that need to

be accomplished? – Needs for real-time operations might be quite different than

what is needed in planning

• Understanding the entire processes in which the visualizations are embedded is key

• Software should help humans make the more complex decisions, i.e., those requiring information and knowledge – Enhance human capabilities – Alleviate their limitations (like adding up bus flows)

4

Power System Operating States • Effective data analysis and visualization for operations

requires considering the different operating states

• Effective visualization is most needed for the more rare situations and for planning

5

Image Derived From L.H. Fink and K. Carlsen, Operating under stress and strain, IEEE Spectrum, March 1978, pp. 48-53

Synthetic Models and Visualization • Access to actual power grid models is often restricted,

and this can be a particular concern with data analysis and visualization since its purpose is provide insight into the model, including weaknesses – Models cannot be freely shared with other researchers, and

even presenting results can be difficult

• Solution is to create entirely synthetic (fictitious) models the mimic the characteristics of actual models – We are doing this on an ARPA-E project, with all models

containing geographic coordinates

6

Synthetic Texas Model with 2000 Buses • ERCOT geographic

footprint • Four voltage levels:

500, 230, 161, 115 kV • 1250 substations,

2000 buses, 545 gens 2350 branches, 67 GW

• Geographic coordinates are available for all substations; model is now fully public at https://electricgrids.engr.tamu.edu

7

This is a synthetic power system model that does NOT represent the actual grid. It was developed as part of the US ARPA-E Grid Data research project and contains no CEII. To reference the model development approach, use:

For more information, contact abirchfield@tamu.edu.

A.B. Birchfield, T. Xu, K.M. Gegner, K.S. Shetye, and T.J. Overbye, "Grid Structural Characteristics as Validation Criteria for Synthetic Networks," to appear, IEEE Transactions on Power Systems, 2017.

Next Up: Ten Thousand Bus Case 8

• Model has 10K buses, 4700 substations, 16 areas, six nominal transmission voltages (765, 500, 345, 161, 138 and 115kV); total peak load is 150GW

• Green arrows show initial MW flows

• Model should be publicly available soon!

Background: Preattentive Processing • Good reference book: Colin Ware,

Information Visualization: Perception for Design, Third Edition, 2013

• When displaying large amounts of data, take advantage of preattentive cognitive processing – With preattentive processing the time spent to find a

“target” is independent of the number of distractors

• Graphical features that are preattentively processed include the general categories of form, color, motion, spatial position

9

Preattentive Processing Example 10

All are preattentively processed except for juncture and parallelism

Source: Information Visualization by Colin Ware, Fig 5.5

Preattentive Processing with Color & Size 11

Illini 42 Bus CaseUnserved Load: 0.00 MW

417 MW

515 MW

2378 MW

1750 MW

234 MW 55 Mvar

234 MW 45 Mvar

92 MW 29 Mvar

265 MW -48 Mvar

265 MW -48 Mvar

265 MW -48 Mvar

267 MW 127 Mvar

267 MW 127 Mvar

236 MW 108 Mvar

199 MW 82 Mvar

149 MW 30 Mvar

205 MW 54 Mvar

203 MW 64 Mvar

198 MW 45 Mvar 198 MW

45 Mvar

155 MW 42 Mvar

155 MW 42 Mvar

240 MW 0 Mvar

240 MW 0 Mvar

157 MW 32 Mvar

157 MW 27 Mvar

183 MW 55 Mvar

199 MW 32 Mvar

187 MW 41 Mvar

199 MW 51 Mvar

199 MW 61 Mvar

173 MW 32 Mvar 154 MW

23 Mvar 174 MW 15 Mvar

208 MW 29 Mvar 137 MW

32 Mvar 208 MW 29 Mvar

130 MW 15 Mvar

93 MW 35 Mvar

265 MW 1 Mvar

265 MW 1 Mvar

265 MW 1 Mvar

207 MW 45 Mvar

182 MW 33 Mvar

110 MW 39 Mvar

296 MW 59 Mvar

94 MW 23 Mvar 74 MW

15 Mvar 196 MW 35 Mvar

190 MW 30 Mvar

159 MW 21 Mvar

134 MW 20 Mvar

140 MW 20 Mvar

87 MW -47 Mvar

129 MW 45 Mvar

127 MW 27 Mvar

67%

59%

29%

45%

85%

37%

61%

20%

60% 21%

25%

33%

38%

59% 65%

27%

70%

66%

45%

57%

39%

75%

49%

47%

59%

75%

35%

62%

21%

80%

54%

45%

87%

49%

1162 MW

184 MW 168 MW 183 MW

91 MW

60%

1570 MW

246 MW 49 Mvar

Hickory138

Elm138 Lark138

Monarch138

Willow138

Savoy138Homer138

Owl138

Walnut138

Parkway138 Spruce138

Ash138Peach138

Rose138

Steel138 130 Mvar

70 Mvar

100 Mvar

130 Mvar

Metric: Unserved MWh: 0.00 120 Mvar

120 Mvar

88%

31%

70%

57% 78%

64%

47%

65%

Badger

DolphinViking

Bear

SidneyValley

Hawk

46%

Illini

Prairie

Tiger

Lake

Ram

Apple

Grafton

Oak

Lion

55%

85%

1570 MW

52%

197 MW 39 Mvar

198 MW 45 Mvar

34%

75%

189 MW 63 Mvar

200 MW

515 MW

82%

60 Mvar

63%

Eagle

26%

75%

0 MW

96%

90%

105%

121%

103%

114%

Use of Color • Some use of color can be quite helpful

– 10% of male population has some degree of color blindness (1% for females)

• Do not use more than about ten colors for coding if reliable identification is required

• Color sequences can be used effectively for data maps (like contours) – Grayscale is useful for showing forms – Multi-color scales (like a spectrum) have advantages (more

steps) but also disadvantages (effectively comparing values) compared to bi-color sequences

12

Blue/Red Discrete Color Sequence 13

Illini 42 Bus CaseUnserved Load: 0.00 MW

417 MW

515 MW

2378 MW

1750 MW

234 MW 55 Mvar

234 MW 45 Mvar

92 MW 29 Mvar

265 MW -48 Mvar

265 MW -48 Mvar

265 MW -48 Mvar

267 MW 127 Mvar

267 MW 127 Mvar

236 MW 108 Mvar

199 MW 82 Mvar

149 MW 30 Mvar

205 MW 54 Mvar

203 MW 64 Mvar

198 MW 45 Mvar 198 MW

45 Mvar

155 MW 42 Mvar

155 MW 42 Mvar

240 MW 0 Mvar

240 MW 0 Mvar

157 MW 32 Mvar

157 MW 27 Mvar

183 MW 55 Mvar

199 MW 32 Mvar

187 MW 41 Mvar

199 MW 51 Mvar

199 MW 61 Mvar

173 MW 32 Mvar 154 MW

23 Mvar 174 MW 15 Mvar

208 MW 29 Mvar 137 MW

32 Mvar 208 MW 29 Mvar

130 MW 15 Mvar

93 MW 35 Mvar

265 MW 1 Mvar

265 MW 1 Mvar

265 MW 1 Mvar

207 MW 45 Mvar

182 MW 33 Mvar

110 MW 39 Mvar

296 MW 59 Mvar

94 MW 23 Mvar 74 MW

15 Mvar 196 MW 35 Mvar

190 MW 30 Mvar

159 MW 21 Mvar

134 MW 20 Mvar

140 MW 20 Mvar

87 MW -47 Mvar

129 MW 45 Mvar

127 MW 27 Mvar

67%

59%

29%

45%

85%

37%

61%

20%

60% 21%

25%

33%

38%

59% 65%

27%

70%

66%

45%

57%

39%

75%

49%

47%

59%

75%

35%

62%

21%

80%

54%

45%

87%

49%

1162 MW

184 MW 168 MW 183 MW

91 MW

60%

1570 MW

246 MW 49 Mvar

Hickory138

Elm138 Lark138

Monarch138

Willow138

Savoy138Homer138

Owl138

Walnut138

Parkway138 Spruce138

Ash138Peach138

Rose138

Steel138 130 Mvar

70 Mvar

100 Mvar

130 Mvar

Metric: Unserved MWh: 0.00 120 Mvar

120 Mvar

88%

31%

70%

57% 78%

64%

90%

47%

65%

Badger

DolphinViking

Bear

SidneyValley

Hawk

46%

Illini

Prairie

Tiger

Lake

Ram

Apple

Grafton

Oak

Lion

55%

85%

1570 MW

52%

197 MW 39 Mvar

198 MW 45 Mvar

34%

75%

189 MW 63 Mvar

200 MW

515 MW

82%

60 Mvar

63%

Eagle

26%

75%

0 MW

96%

105%

121%

103%

114%

Continuous Color Sequence 14

Illini 42 Bus CaseUnserved Load: 0.00 MW

417 MW

515 MW

2378 MW

1750 MW

234 MW 55 Mvar

234 MW 45 Mvar

92 MW 29 Mvar

265 MW -48 Mvar

265 MW -48 Mvar

265 MW -48 Mvar

267 MW 127 Mvar

267 MW 127 Mvar

236 MW 108 Mvar

199 MW 82 Mvar

149 MW 30 Mvar

205 MW 54 Mvar

203 MW 64 Mvar

198 MW 45 Mvar 198 MW

45 Mvar

155 MW 42 Mvar

155 MW 42 Mvar

240 MW 0 Mvar

240 MW 0 Mvar

157 MW 32 Mvar

157 MW 27 Mvar

183 MW 55 Mvar

199 MW 32 Mvar

187 MW 41 Mvar

199 MW 51 Mvar

199 MW 61 Mvar

173 MW 32 Mvar 154 MW

23 Mvar 174 MW 15 Mvar

208 MW 29 Mvar 137 MW

32 Mvar 208 MW 29 Mvar

130 MW 15 Mvar

93 MW 35 Mvar

265 MW 1 Mvar

265 MW 1 Mvar

265 MW 1 Mvar

207 MW 45 Mvar

182 MW 33 Mvar

110 MW 39 Mvar

296 MW 59 Mvar

94 MW 23 Mvar 74 MW

15 Mvar 196 MW 35 Mvar

190 MW 30 Mvar

159 MW 21 Mvar

134 MW 20 Mvar

140 MW 20 Mvar

87 MW -47 Mvar

129 MW 45 Mvar

127 MW 27 Mvar

67%

59%

29%

45%

85%

37%

61%

20%

60% 21%

25%

33%

38%

59% 65%

27%

70%

66%

45%

57%

39%

75%

49%

47%

59%

75%

35%

62%

21%

80%

54%

45%

87%

49%

1162 MW

184 MW 168 MW 183 MW

91 MW

60%

1570 MW

246 MW 49 Mvar

Hickory138

Elm138 Lark138

Monarch138

Willow138

Savoy138Homer138

Owl138

Walnut138

Parkway138 Spruce138

Ash138Peach138

Rose138

Steel138 130 Mvar

70 Mvar

100 Mvar

130 Mvar

Metric: Unserved MWh: 0.00 120 Mvar

120 Mvar

88%

31%

70%

57% 78%

64%

90%

47%

65%

Badger

DolphinViking

Bear

SidneyValley

Hawk

46%

Illini

Prairie

Tiger

Lake

Ram

Apple

Grafton

Oak

Lion

55%

85%

1570 MW

52%

197 MW 39 Mvar

198 MW 45 Mvar

34%

75%

189 MW 63 Mvar

200 MW

515 MW

82%

60 Mvar

63%

Eagle

26%

75%

0 MW

96%

105%

121%

103%

114%

Techniques for Time-Varying Data • Need to keep in mind the desired task! • Tabular displays • Time-based graphs (strip-charts for real-time) • Animation loops

– Can be quite effective with contours, but can be used with other types of data as well

• Data analysis algorithms, such as clustering, to detect unknown properties in the data – There is often too much data to make sense without some

pre-processing analysis!

15

Time-based graphs • Graphs can be quite

helpful for showing exact values if no more than about ten individual signals are shown – In larger sets outliers may

be missed

• Showing more values can be helpful in identifying response envelope – Graph at left shows 2400

signals

16

Animation Example 17

GVDs for Interactive Visualization • One issue with visualization is deciding a priori on the

information to show • A solution is to use what we’re calling geographic

information views (GDVs) in which embedded substation latitude and longitude allows displays to be auto-created – The previous animation loop was an example in which the

substations, used to contour the frequency, had been automatically inserted

• Now essentially any power system field can be shown

18

GDV Showing Substation GMD Losses 19

New Scotl

Rot t erdam

H u r

R

R o c k T a

Sugar loaf

Roselan

M e

Sm

Shoem aker

B le n h e im - G ilb o a

Brunswick

Deans

CPV Valley

East Windsor

Card

T a b e r n a c le

Branchburg

Lawrence

Coopers Corners

I n g h a m s

D a c o s t a

L u m b e r t o n

Ford

S w a in t o n

R o llin g M illEmilie

At co

Dennisville

B e r lin

B u r lin g t o n ( P S E G F )

T a n s b o r o

New FreedomP in e H ill

Fr aser

Byber r y

M onroe

T a p

C u m b e r la n d ( A T E L C O )

S h e r m a n A v e n u e

B e t h a n y

M ar t ins Creek

Waneet a

Heat on

S o u t h w a r k

G la s s b o r o

Lom brd

N o r t h P h ila d e lp h ia

W a v e r ly

P it m a n

G r a y s F e r r y C o g e n e r a t io n P a r t n e r s h ip

M ast er

Passyunk

P a c k e r

R o w a n

Orchard

W h it e m a r s h

Edic

I n d ia n R iv e r ( N R G )

L a m b

I s la n d R o a d

Center Point

P ly m o u t h M e e t in g

DelcoTap

A n g o r a

Whitpain

E d d y s t o n e

Elr oy

P a p e r

Pedr ickt own

Milford

P r in t z

Cham ber s

Allent ownSteel City

C h ic h e s t e r

C la y m o n t

S a le m

W e s t e r lo o

C h u r c h t o w n

P in e y G r o v e

Edgem oorHay Road

Siegf r ied

Cromby

Salem (PSEGN)Hope Creek (PSEGN)

C h r is t ia n a

Penny Hill

Wescosville

Brei

O a k H ill

Blue Ball

Planebrook

Limerick (EXGEN)

L a c k a w a n n a

Harm ony

M o s e r

K ia m e n s i & W e s t

Eagle

Red Lion

Kitty Hawk

Bradf ord

Keeney

L y o n s

Steele

Vienna

Cecil

Newlinville

K e lla m

Oakdale

D a le v ille

Vir ginia Beach

C la r k C o r n e r

Dewitt

L y n c h

L a u s c h t o w n R d .

N . M e s h o p p e n

T o d d v ille

H ill R o a d

Landstown

Susquehanna

R o c k S p r in g s G e n e r a t in g

Clay

Greenwich

Conowingo

Fentress

Perryman

Peach Bot t omM u d d y R u n

E liz a b e t h R iv e r N u g

S t e v e n s v ille

C h e s a p e a k e E n e r g y C e n t e r

S e w e lls P o in t

Yadkin

C. P. Crane

Shellbank

Gracet on

Elbridge

Peninsula

S a f e H a r b o r

R a p h a e l R d .

Whealt on

Calvert Cliffs

Yorktown

Harm ony Village

H a y e s

River side (CPS)

H e r b e r t A W a g n e r

B r a n d o n S h o r e s

Conast one

Skiff Creek

Suffolk

Yorkana

Hackeys

West por t

AES Cayuga

P u m p h r e y

T o ln a

Montour

Waugh Chapel

Chalk Point

P P L B r u n n e r I s la n d

Gravel NeckSurry

M id d le t o w n J c t n .

N o r t h e r n N e c k

Three Mile Island

Wat er cure

B o w ie

Hillside

CoronaSunbury

M ilt o n

Nor t hwest

Granite

Jackson

Tipt on

Burches Hill

High Ridge

Lanexa

Benning

M o r g a n t o w n

M ainesburg

Buzzard Point

Br ight on

P e n t a g o n

O St

L a u r e l H ill

Everetts

Bells M ill

Ogden

Chickahom iny

Hunt er st own

Juniata

Carroll

M o u n t a in

Possum Point

Ox

Ginna

O r r t a n n a

P o e

N o r t h e a s t

Pannell

Chesterfield

D e f e n c e S u p p ly C e n t e r

Clifton

Carson

St . Johns

Locks

B u ll R u n

S t a t io n 1 2 4

D ic k e r s o n S t a t io n " D "Dicker son

P la z a

F r e d e r ic k s b u r g

Elmont

Doubs

Pleasant View

G u ilf o r d

NW

C a n a d ia s

Ladysmith

C h a n c e llo r

Loudon

Lewist own

B r is t e r s ( P r o p o s e d )

New Rd

Carolina

Goldale

Thelm a

S t a t io n 8 0

Midlothian

Morrisville

M eyer

R e m in g t o n M a r s h R u nRem m ingt on

North Anna

Wilson

Bedington

M eadow Brook

S o u t h A n n a N U G

B r e m o B lu f f

Selm a

Zebulon

S h a w v ille

F a r m v ille

Pierce Brook

H e n d e r s o n

Wake

O le a n E n e r g y C e n t e r

C a r b o n C e n t e r

M ill R u n

M ilb u r n ie

Falls

S t o lle R o a d

Elko

AES Som er set

S q u a b H o llo w

Method

E n d le s s C a v e r n s

H ig h la n d W in d

Clover

R o b in s o n R o a d

Forest

Durham

Gardenville

Ridgeley

Grot t oes

Dooms

E . D u r h a m

Crest St .

P ly w o o d

M a y o

B la c k O a k

Hoover sville

Halifax

Parkwood

H a r r is ( C P L C )

Valley

Dupont

St aunt on

S e n e c a - C E I

Robert Moses Niagara

Eno

Seward (RRI)

Sir Adam Beck 1

Person

Cape Fear

Conemaugh

Roxboro (CPLC)

Joshua FallsE a s t L y n c h b u r g

Allanburg

Homer City

Thorold

Greenland Gap

Peaksview

B la ir s v ille

Decew Falls

E a s t M o n u m e n t

Mount Storm (VIEP)

Concord

Vansickle

Sheloct a

M ebane

Keyst one (RRI)

D u n k ir k ( N R G )

E. Danville

H a ll B r

Sout h Bend

R ig is

R iv e r s id e

M o t le yDanville

Henry

Hurt

W illia m

Siler Cit y

Mosely

C e n t e r v ille

L o r n e P a r k

Sadler

E llio t t s

Oakville

R o c k in g h a m P o w e r P la n t

D a n R iv e r

Yukon

Trafalgar

P le a s a n t G a r d e n

Asheboro

H a m ilt o n - L a k e

N. Greensboro

Beach

Axton

CheswickLogan's Fer r y

Cabot

Bath County

G a g e

C u m b e r la n d

C lif t o n F o r g e

D o f a s c o

G a n s

Kenilwor t h

Halton

B u r lin g t o n

H a m ilt o n N e b o

Low M oor

Cloverdale

B ir m in g h a m H ill

4 M ile

Fielddale

F o r t M a r t in ( M O N G )

Hat f ields Fer r y Power St at ionRonco

S t ir t o n H ill

M adison

C a le d o n ia

Carson

W a y n e ( R R I )

M iddlepor t

5 0 2 J T a p

ArsenalBrunot I sland

Er ie Sout h

Nanticoke (OPG)

Belews Creek

Pruntytown

C a m p b e ll

Collier

M a p le

Hancock

Horning

B e c k e r d it e

Cranberry

Crescent

B r a n t f o r d

L e x in g t o n

Cedar

Galt

Cat awba

C lin t o n

R u r a l H a ll

Harrison

Hoytdale

Erie West

New Cast le

B u c k ( D U P C )

M a n s f ie ld ( F I R G E N )

Shenango

Beaver Valley

Kit chener

S c h e if e ld

M ocksville

Det weiler

P in n a c le

Wylie Ridge

C la y t o r

B r u e s

Toront o

R o w a n C o u n t y E n e r g y C o m p le x

Sam m is

Tidd

M it chell River

Karn

W . B e lla ir e

Kammer

Jacksons Ferry

H ig h la n d / H u b b a r d

I nger soll

S t a m e y

Antioch

N o r t h W ilk e s b o r o

Holloway

Leroy Cent er

P e r r y ( F I N U O P )

E d g e w a r e

Br adley

Shaler sville

Hanna

Lookout

Harmon

K in c a id

L o n d o n - T a lb o t

D a m e r o n

C a r b o n d a le

PleasantsBelmont

Wagenhals

C a n t o n C e n t r a l

H ic k o r y

S. E. Cant on

Seaforth

Bolt

Tor r ey

South Canton

Kanawha River

D a r r o w

R h o d h is s

Eastlake

Oak Grove

Sam sung

C h a m b e r la in

C a b in C r e e k

S u n d ia l

C lo v e r d a le

Tazewell

Valdese

Juniper

Parkhill

Longwood

Inland

Capitol

J im B r a n c h

C h e s t e r

B e a r w a l

Beverly

Harding

W y o m in g

Bim

Broadford

H o r iz o n

M uskingum River

Ham ilt on

Star

W a t e r f o r d E n e r g y C e n t e r

C a r s w e ll

Fox

Duart

Hum m el

Amos

H o p k in s

S e v ille

Conesville

Sporn

Mountaineer

Woost er

H a le s B r .

D r e s d e n E n e r g y C e n t e rOhio Central

Avon Lake

S t r o u d s R u n

Car lisle

Sout h Holst on

L o o n e y C r e e k

Gavin

Elliot

L a k e A v e .

Kent

POSTON

C lin c h f ie ld

N o r t h B r is t o l

Spr igg

Wolf Hills

Beaver

Sullivan

Cane River

H a t f ie ld

Green E.

Flat Lick

S t o n e

D a r a h

S a r n ia - S c o t t

N . P r o c t o r v ille

M odeland

B o o n e ( T V A )

F ir e

B u n c e C r e e k

Lambton

H e a t h

Inez

St. Clair

C a r g ill S a lt I n c

Belle River

W e s t H e b r o n

KenovaTristate

Baker

R e e d C r e e k

D e a n

W . M ille r s p o r t

S o u t h P o in t

Nagel

V ir g in ia C it y

R iv e r s id e ( D Y N O P E )F o o t h ills G e n e r a t in g P r o j e c t

Hill

D o r c h e s t e r

Kirk

Greenwood

B e lle f o n t e

G r e e n f ie ld

Gr angst on

Beaver Creek

Jug St

Biers

Banner

Lenox

DeweyThelna

I m b o d e n

CorridorVassel

Bixby

B e x le y

Lauzon

Genoa

C ir c le v ille

John Sevier

C o n n e r s C r e e k

Ross

Grassmere

M a r io n

W in d s o r - E s s e x

M aliszewski

B is m a r c k

S t e p h e n s

Jewell

Don Marquis

Red Run

Nor t heast

Sterling

Caniff

Pocket

J . C . K e it h

Tangy

Davis-Besse

Sawm ill

B r ig h t o n B e a c h

Wilson

Hyatt

O t t o w a

Roberts

Beatty

Spokane

KHC (Kelsey Hayes)

Fr em ont

L o v e

W . F r e m o n t

Atlanta

W a r r e n

Hazard

Stein

T r e n t o n C h a n n e l

Brownstown

Fermi

B lo o m f ie ld

P o n t ia c

L e s lie

Darby

Monroe (DETED)

R o w a n

Marysville

Bay Shore

H a n c o c k

J . R . W h it in g ( C E C )

Lemoyne

Placid

Wayne

J u d d

C h e r o k e e ( T V A )

Quaker

Wixom

Crosswind

D o w lin g

V u lc a n

Covent r y

Cody

D e la n e y

Thet f ord

Super ior

H a ls e y

A t la n t a

Dor t

M orocco

Pasadena

Milan

Pineville

Allen Jct .

F a ll R o c k

U r b a n a

K e n t o n

Spurlock

Volunteer

Dan E. KarnJ . C . W e a d o c k

C la r k

C lin t o n

Midway

R a is in

Goss

Clark

Bath

Majestic

Fulton

Greene

B e e c h e r

Madrid

E. Lima

M onit or

Murphy

H ill C o u r t

Alpha

P o w e ll C o .

Shelby

N o r r is

C o lf a x ( D E T E D )

S u g a r C r e e k

J.K. Smith

F a r le y

L a k e R e b a

S h a w n e e R o a d

Bull Run (TVA)

M ia m i

O w o s s o

W a r r e n

Tit t abawassee

S.W. Lima

W a c k e r ly

G le a n e r

Shaker

M id la n d C o g e n e r a t io n V e n t u r e ( M C V )

W . H . Z im m e r

Fost er

Bullock

Slate

F a r f a x

D a le ( E K P C )

L a u r e l ( U S C E N D )

C r o w n

Hut chings

P ie r c e

S u m m e r s id e

Beckj ord

Tobasco

C a r lis le

L in w o o d

D e e r C r e e k

AvonLoudon

F a w k e s

Plym out h

W. M ilt on

P a g e A v e .

Silver Grove

Todhunt er

Redbank

R e n a k e r

R

B la c k s t o n e

Oakley

C la r y v ille

St r yker

H a e f lin g

Por t Union

K e n t o n

W o o d s d a le

V r o o m a n

Term inal

Tom pkins

C e n t r a l

J a c k s o n b u r g

M it c h e ll A v e

R e o

Delhi

Wilder

Fair f ield

AlcadeC o o p e r

Haviland

W a r r e n

Gr eenville

W. Lexingt on

M o o r e R d

P is g a h

E r ic k s o n

Pr ice Hill

Dixie

B r o w n ( K U C )

Buf f ingt on

B e a v e r

B o o n e

A d a m s

S u m m e r t o n

D a v id

Ebenezer

C o n v o y

S t illw e ll

Oneida

V e r n o n

Miami Fort

F r a n k f o r t E

East Bend

L a w r e n c e b u r g

W . F r a n k f o r t

Allen

Ghent

NAS

J a y C o u n t y L a n d f ill

Robison Par k

R u s s e ll

Verona

H e a d w a t e r

Bat t le Creek

H ill C r e s t

E u r e k a

M o d o c

B a t e s v ille

K e y s t o n e

N . B e ld in g

I llin o is

T h o m p s o n

I n d u s t r y

Buckner

M cKinley

Sorenson

N e w C a s t le

H e r s e y

Vergennes

Jefferson

S im p s o n

Greensboro

M iddlet own

C o llin s

V a n B u r e n

Dut t on Gaines

Fall Creek

Argenta-1

D e e r C r e e k

H a r d y

W a y la n d

G w y n n e v ille

Weed Lake

Hiple

A lg o m a

E t h e l

C r o t o n

Buck Creek

Corey

S u m m e r S h a d e

K e n tWealt hy

Blue Lick

Colum bia

Four M ile

Nor t hside

P r e s c o t t

Speed

M ot t ville

Beals Rd

C a n a l

Tallmadge

Cent er Hill

D u r b in

E . E lk h a r t

Ransom

C a n e R u n

Geist

C o lu m b u s ( L S P )

M ill Cr eek (LGEC)

Gr eent own

Valley

Leesburg

N o b le s v ille ( P S E G P )

B u r n ip s

F iv e P o in t s

Sunnyside

Pingree

D u n la p

M a n liu s

R o g e r s v ille

P o r t S h e ld o n

S c o t t L a k e

I n d u s t r ia l

Hardin Co.

C a r m e l

Twin Branch

Kokomo

R ile y S t .

K e n z ie C r e e k

J. H. Cam pbell (CEC)

St ought ons

Ram sey

B r ic k y a r d

B. C. Cobb

G e o r g e t o w n ( I P & L )

Guion

Walton

Pritch

Covert

Palisades (NMC)

L e it c h f ie ld

Rockville

W h it e s t o w n

J a c k s o n R o a d

Thom pson

B e n t o n H a r b o r

Per e M arquet t e

Dumont

A m b e r

R e d w o o d

Burr Oak

M ic h ig a n P o w e r , L . P .

River side

Ludington

G a lla t in ( P R I )

P le t c h

Wilson

M it c h e ll L o s t R iv e r

H ic k o r y C r .

H a r d in s b u r g S t a t io n

Olive

Qualit y

Bedford

Donald C. Cook

Meadow

T a s w e ll

St illwell

Bloomington

S o u t h N a s h v ille

Cannelt on

T r o y

C o le m a n ( W K E C )

Newt onville

W e s t N a s h v ille

Reynolds

Michigan City

Duff

West wood

Paradise (TVA)

Schahfer

Rockport (INMI)

W o r t h in g t o n P la n t

Sm it h (OM U)

D B W ils o n ( W K E C )

G r e e n R iv e r ( K U C )

Bailly

S t a u n t o n

Dubois

A t t ic a

E d w a r d s p o r t

Pet e 1 ( IP&L)Rat t s

M ont gom ery (DOM ENE)

Fowler Ridge

Lake George

CulleyW a r r ic k

C la r k s v ille

W a b a s h R iv e r

R iv e r Q

G a r y A v e n u e

CayugaC a y u g a S u b

Green Acres

Francisco

St. John

Reid

Sugar Creek

Sheffield

St at eline (DOM ENE)

E a r lin g t o n

Harbor

P ig e o n

Burnham

Sullivan

R iv e r

O a k H ill

Bloom

S c o t

C r e t e

TaylorFisk

Tilton

Cum ber land (TVA)

Blue I sland

B u n s o n v ille

Nor t hwest

Edgewater (WPL)

Brown (SIGE)

C r a w f o r d ( M I D G E N )

U n iv e r s it y P a r k E n e r g y

Skokie

Gibson (PSI)

Bedf ord Par k

W. Loop

T a pRidgeland

Nor t hbrook

ZionW a u k e g a n ( M I D G E N )

McCook

E Frankf or t

O a k C r e e k N o r t h

Golf Mill

W a lk e r

Racine

P o r t W a s h in g t o n ( W E P )

P r in c e t o n

F r a n k lin P a r k

Des Plaines

Davis Creek

P r o s p e c t H e ig h t s

Elmhurst

V a lle y ( W E P )

G o o d in g s G r o v e

M o r g a n f ie ld

Wilton Ctr.

S a u k v ille

Kansas

Liber t yville

Itasca

G r a n v ille

Par is

B lu e m o u n d

Sidney

Lisle

W ill C o u n t y

Albion

Joliet 29

Arcadian

Cypress

Braidwood

Electric Junction

Wolf

Wayne

Tollway

E liz a b e t h t o w n

Silver Lake

K e n d a ll C o u n t y P r o j e c t

K e n t u c k y ( T V A )

Katy

Dresden

Norris City

Newt on

Elgin

Rising

Collins (MIDGEN)

Equis

P le a s a n t V a lle y ( I N D O P E )

N e o g a

S o u t h F o n d D u L a c

Plano

G ib s o n C it y

Car r ier M ills

Fit zgerald

G o o s e C r e e k E n e r g y C e n t e r

La Salle

Concord

R e n s h a w

C o lle y R d .

Shawnee (TVA)

Tur

S h e lb y v ille

C lin t o n ( A M E R G E N )

Weakley

E . W e s t F r a n k f o r t

Joppa St eam

Mt. Zion

M cCue

B r o k a w

A v e n a

W e s t F r a n k f o r t

B lo o m in g t o n

W. M t . Vernon

C h e r r y V a lle y

C h a r lie B lu f f

A lp in e

Kinm undy

R a m s e y

R o c k R iv e r

Rockdale

M in o n k

North Pana

N R G R o c k f o r d I

Paddock

Blue Mound

Wem plet own

Lat ham

Byron (EXGEN)

Hennepin

Lanesville

Jackson

Colum bia (WPL)

Coffeen

N. M adison

P o r t a g e

Kincaid

W. Middleton

N . C o u lt e r v ille

Tazewell

C a m p b e ll H ill

Kelso

E 'd a le

New Madrid - ASEC

D a llm a n

Austin

N e ls o n

Sikest on

Car

P r a ir ie S t a t e E n e r g y C a m p u s

Edwards

Power t on Generat ing St at ion

Fargo

Heritage

B a ld w in E n e r g y C o m p le x

E s s e x P o w e r P la n t

T u r k e y H ill

D u c k C r e e k

Pet enwell

S t a llin g s

K e n n e t t

Havana

V e n ic e ( U N I E L )

S t F r a n c is

Cahokia

Bevr Chn

Campbell

R u s h I s la n d

Sioux

C o r d o v a E n e r g y

S a n d b u r g

Q u a d C it ie s ( E X G E N )

Ipava

M eram ec

H ills b o r o

E d e n

G a le n a

Oak Grove

R iv e r s id e ( M I D A M )

E. Moline

Saint Francois

Mason

C o u n c il C r e e k

Joachim

Tyson

Sub 91

M eredosia

S a le

8 t h S t r e e t

Sub 18

G o b K n o b

Salem

Wildwood

Davenport

Belleau

L e e p e r

T a u m S a u k

Labadie

Ned

Gray Summit

C la r k

E J S t o n e m a n

Enon

H ic k o r y C r e e k

S . S u b

Flet cher

L o u is a ( M I D A M )

B u r lin g t o n ( I P L )

Lansing

C o u le e

S u lliv a n

NiotaW e v e r

P e n o C r e e k

Genoa

Nor t h La Crosse

H e r le m a n

V ie le

M ont gom ery

S a le m

B la n d

nicipal

Circle size is proportional to substation GMD-induced reactive power losses; color indicates neutral flow direction

Other Examples: Clustering 20

Other Examples: Modal Analysis 21

Conclusion • We've reached the point in which there is too much

data to handle most of it directly – Certainly the case with much time-varying data

• How data is transformed into actionable information is a crucial, yet often unemphasized, part of the software design process

• There is a need for continued research and development in this area – Synthetic dynamics cases are needed to help provide input

for such research

22

Thank You! 23

Questions?

Synthetic models are available at https://electricgrids.engr.tamu.edu