Smart Transmission Grids Applications for Real-Time and Predictive Monitoring and Control of Power Systems

1. “without deviations from the norm, progress is not possible”
Frank Zappa

2. Smart Transmission Grids Applications
for Real-Time and Predictive Monitoring and Control
of Power Systems
Prof.Dr.Ing. Luigi Vanfretti
luigiv@kth.se - http://www.vanfretti.com
luigiv@kth.se
Associate Professor, Docent
SmarTS Lab
KTH
Stockholm, Sweden
Luigi.Vanfretti@statnett.no
Special Advisor in Strategy and Public Affairs
Research and Development Division
Statnett SF
Oslo, Norway
Invited Seminar – Department of Mechanical Engineering
Massachusetts Institute of Technology (MIT)
March 17, 2014 – Cambridge, MA, USA

3. Outline
• SmarTS Lab Research Group, Projects and Funding, and
“The Lab”
• Challenges brought by renewable energy sources
- Impact on system dynamics
• Large Scale Power System Operation
- Technical Needs
- Vision for Smart Operation Monitoring and Control Systems
• Concepts
- Power system dominant paths: concept and identification
- Power system dominant path feedback signal properties and time
delay impacts
• Methods
- Identification of dominant paths
• Tools
- Real-time monitoring and control of oscillations
• Looking forward – challenges for R&D
- Solutions needed for Smart Operation
- Interesting challenges

4. Research Group Members
• 19 persons: 2 staff, 3 post-docs, 9 phd students, ~< [1 - 5] MSc students at a time
• Research Group Leader: Prof. Luigi Vanfretti
• Lab coordinator: Viktor Appelgren
• Post-docs (3):
- F. Rafael Segundo-Sevilla,
- Hossein Hosshyar, Rujiroj Leelaruji
• PhD Students (9):
- Yuwa Choompoobutrgool, Vedran Peric,
- Wei Li, M. Shoaib Almas, Tetiana Bogodorova,
- Jan Lavenius, Farhan Mahmood,
- Maxime Baudette , Francisco Gomez
• Researchers (0):
- N/A
• MSc Students (4):
- Mengjia Zhang, Le Qi, Giusseppe Laera, M. Ahsan Adib Murad, Jahidul Islam Razan 4

5. International Projects and Funding
• The research group participates in several
international projects funded by the EC
within the FP7 program and Nordic projects
funded by NER, such as:
• Funding Total of 36 Million SEK
• About 12% from National funding.
5

6. SmarTS Lab: A test-bench for developing and testing
Smart Transmission Grid Technologies
Sync. Timing
Phasor Measurement Data
Controllable and Protective Device
Measurement and Status Data
Data Alignment
and
Concentration
CommunicationNetworks Data Storage
and Mining
Monitoring
Transmission System
Control Center
Advanced
Displays
Early Warning
System
Decision Support
Intelligent System
Automatic
Control and
Protective
Actions Actions
Smart Operation Decision/Control Support System
Smart-Automatic Control Actions
Smarter Operator
Decision Support
Smarter Operator Control
Actions
Grid Optimization
• Basic actions for automatic power system management:
- Measure – Communicate – Analyze the System (System health and limit assessment) – Determine Optimizing Actions and
Preventive/Corrective Actions – Communicate – Control and protect
• Smart Operation, Control and Protection solutions:
- The ultimate goal should be to attain a (nearly) automatic-feedback self-healing control system
- The functionalities of the Smart Operation Decision/Control Support System need to be developed and tested
- The harmonization and interaction of model-based and measurement-based tools needs to be developed
Physical Layer ICT Layer
Virtual Layer:
Decision Support,
Control and
Optimization
Functionalities for
Smart Operation
I
O

7. SmarTS Lab: A test-bench for developing and testing
Smart Transmission Grid Technologies
Sync. Timing
Phasor Measurement Data
Controllable and Protective Device
Measurement and Status Data
Data Alignment
and
Concentration
CommunicationNetworks Data Storage
and Mining
Monitoring
Transmission System
Control Center
Advanced
Displays
Early Warning
System
Decision Support
Intelligent System
Automatic
Control and
Protective
Actions Actions
Smart Operation Decision/Control Support System
Smart-Automatic Control Actions
Smarter Operator
Decision Support
Smarter Operator Control
Actions
Grid Optimization
• Basic actions for automatic power system management:
- Measure – Communicate – Analyze the System (System health and limit assessment) – Determine Optimizing Actions and
Preventive/Corrective Actions – Communicate – Control and protect
• Smart Operation, Control and Protection solutions:
- The ultimate goal should be to attain a (nearly) automatic-feedback self-healing control system
- The functionalities of the Smart Operation Decision/Control Support System need to be developed and tested
- The harmonization and interaction of model-based and measurement-based tools needs to be developed

8. Our Architecture

9. Our laboratory!
Design and implementation of time-synchronized measurements (i.e.
synchrophasor applications)
- Concepts: inception and design
- Methods: implementation of algorithms for monitoring, control and
protection with real-time execution constraints
- Tools: software architecture and prototype implementation
- Testing and Validation: of the implemented tools using off-line & real-time
simulation, as well as laboratory experiments.
10 15 20 25
0.985
0.99
0.995
1
1.005
1.01
1.015
1.02
Voltage(pu)
Time (sec)
SCADA: Old
Technology, Slow
PMU: New
Technology, FAST!
PMU vs SCADA
Measurements
from Wind-Farm
Interactions
(oscillations)
Applications using
PMU Data can
detect oscillations,
while those based
on SCADA can not.
SmarTS Lab Architecture
SmarTSLab
HardwareImplementation

10. Challenges:
Renewable energy sources bring more production capacity...
• But production from renewables is not enough to attain
a sustainable energy system
• For a secure, efficient, and flexible use of renewables
secure power transmission is necessary!

11. Bringing More Renewable Capacity in the Nordic Grid
Will pose a challenges on how the grid is operated and controlled
11
Old pattern: winter/summer, wet/dry –
mainly seasonal – operation based on
historical experience!

12. Bringing More Renewable Capacity in the Nordic Grid
Will pose a challenges on how the grid is operated and controlled
• With changes in generation patterns –
“system dynamics” will also change.
• This means faster power transfer
interactions –which need to be
monitored, and controlled for secure
power transmission.
• Smart Grid solutions need to be
developed to safely integrate
renewable energy sources:
- Real-time monitoring and predictive analytics
can provide real-time visibility and aid in
assessing the health of the system due to fast
dynamic phenomena – across traditional
operational boundaries
- Real-time control can help handling operation
as operation conditions become more stringent
12
Old pattern: winter/summer, wet/dry –
mainly seasonal – operation based on
historical experience!
New pattern: price differences, much
wind / less wind – daily, hourly, per
minute, per second changes!

13. Needs for Grid Operation
Monitoring, Operation, Control, Optimization and Protection Solutions
Different types of
challenges in
Scandinavia:
Picture of the Regional
Control Center at Alta,
Norway.
Operators have the
challenge of running a
power system under
severe weather
conditions and
guaranteeing supply to
critical heavy industries

14. Needs for Grid Operation
Monitoring, Operation, Control, Optimization and Protection Solutions
Different types of
challenges in
Scandinavia:
Picture of the Regional
Control Center at Alta,
Norway.
Operators have the
challenge of running a
power system under
severe weather
conditions and
guaranteeing supply to
critical heavy industries

15. Needs for Grid Operation
Monitoring, Operation, Control, Optimization and Protection Solutions
Different types of
challenges in
Scandinavia:
Picture of the Regional
Control Center at Alta,
Norway.
Operators have the
challenge of running a
power system under
severe weather
conditions and
guaranteeing supply to
critical heavy industries

16. From the X-Ray to the MRI for the Power Grid
Identifying and controlling renewable energy sources in the grid
• We need advances in monitoring and control technology, similarly of the transition
from the X-Ray to the MRI:
Wind-farm induced oscillations at @ OG&E
• Occurring during periods of high wind generation.
• 5% fluctuation at a frequency of 13-15 Hz.
• The oscillations were product of interactions between
controllers in two different wind farms.
• Countermeasures:
o Switching to electrically isolate the wind farms.
o Curtail the power output!
10 15 20 25
0.985
0.99
0.995
1
1.005
1.01
1.015
1.02
Voltage(pu)
Time (sec)
SCADA: Old
Technology,
Slow
PMU: New
Technology,
FAST! Grid
monitoring
technology of
today
Smart Grid
Monitoring
Technology
of tomorrow!

17. From the X-Ray to the MRI for the Power Grid
Identifying and controlling renewable energy sources in the grid
• We need advances in monitoring and control technology, similarly of the transition
from the X-Ray to the MRI:
• Conventional Technology (SCADA) is not capable to cope with new challenges!: too
slow, can never capture the phenomena.
• New software applications and power-electronics-based control technology can help
to:
- Detect unwanted behaviour from renewable energy sources (sub-synchronous wind-farm
oscillations)
Control unwanted/unexpected behaviour and optimize the performance of the grid.
• This in turn will allow to safely transport and utilize renewable energy sources!
Wind-farm induced oscillations at @ OG&E
• Occurring during periods of high wind generation.
• 5% fluctuation at a frequency of 13-15 Hz.
• The oscillations were product of interactions between
controllers in two different wind farms.
• Countermeasures:
o Switching to electrically isolate the wind farms.
o Curtail the power output!
10 15 20 25
0.985
0.99
0.995
1
1.005
1.01
1.015
1.02
Voltage(pu)
Time (sec)
SCADA: Old
Technology,
Slow
PMU: New
Technology,
FAST!

18. Large Scale Power System Operation
Technical Needs at Different Time Scales
• Smart Operation and operational planning solutions can help to satisfy these needs!
Severe Emergency
Conditions requiring
Automatic and Self-Healing
Control and Protective
Actions
Non-Severe Emergency
Conditions requiring
Coordinated Operator
Assited Control and
Optimized Re-Dispatch and
Balancing
Steady State Operating
Conditions requiring
Preventive Control and
Power Flow Optimization
Miliseconds, Seconds Months, Year (1) Years (2-5)
Near Real-Time Operation
Market Forces
Uncertainties in variable production and demand; External Forces; Vulnerabilities (physical and cyber)
Short-Term Mid-Term Long Term
Years (5-15)
Real-Time Operation
Minutes, Hours, Days
Regulation, Legislation
Resource Adequacy and
Grid Development Gaps
requiring Coordinated Grid
Planning Optimization
Long-term goals for
“green” resource adequacy
and grid development
optimization requiring
Coordinated Grid Planing
Operations
Operational Planning Planning
Technical Needs

19. Large Scale Power System Operation
Technical Needs at Different Time Scales
• Smart Operation and operational planning solutions can help to satisfy these needs!
Severe Emergency
Conditions requiring
Automatic and Self-Healing
Control and Protective
Actions
Non-Severe Emergency
Conditions requiring
Coordinated Operator
Assited Control and
Optimized Re-Dispatch and
Balancing
Steady State Operating
Conditions requiring
Preventive Control and
Power Flow Optimization
Miliseconds, Seconds Months, Year (1) Years (2-5)
Near Real-Time Operation
Market Forces
Uncertainties in variable production and demand; External Forces; Vulnerabilities (physical and cyber)
Short-Term Mid-Term Long Term
Years (5-15)
Real-Time Operation
Minutes, Hours, Days
Regulation, Legislation
Resource Adequacy and
Grid Development Gaps
requiring Coordinated Grid
Planning Optimization
Long-term goals for
“green” resource adequacy
and grid development
optimization requiring
Coordinated Grid Planing
Operations
Operational Planning Planning
Our focus!
Technical Needs

20. Smart Operation → Requires new functionalities
That take advantage of the supporting infrastructure (ICT) to enhance power system
operation. That is new monitoring, control, network optimization and market functions.
Transmission System Operator
Smart
Operation
Smart Operational
Planning
New
Markets
Conventional
Generation
Biomass
PV
Wind
Distributed
Loads
Large Controllable
LoadsDomestic Loads
and PV Gen.
Storage
Other Data
Sources (e.g.
weahter)
Power
Transmission
Backbone
Comms
Backbone
New functionalities
enabled by the ICT
need to be designed
to allow for Smart
Operation!

21. Smart Operation
Advanced Monitoring, Control and Optimization Functionalities
Transmission System Operator
Smart
Operation
Smart Operational
Planning
New
Markets
Power Transmission
Backbone
Comms
Backbone
Advanced ICT Solution as “backbone” to hold new functionalities.
Real-Time
Meas. Data
Control
Actions /
ActivationActions Enabling Optimization, Special Control and
Protection Schemes
Enhanced
Monitoring and
Situational
Awareness
Tools
Real-Time and
Predictive
Decision
Support Tools
Grid
Optimization
Tools
Stability
Monitoring and
Control using
Real-Time and
Predictive Tools
Automatic
Control Loops Other

22. Concepts:
Where do the main dynamics of large interconnected systems ‘flow’?
Where can we observe them? How to identify critical links?
22
p. 09.02.10 /ngt
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Lauchstädt
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3. Castelluccia
1. Alfa Avio
Sogetel
Colli Aminei
Astroni
Pozzuoli
Arenella
MA
DZ
TN
Ceuta
TR
MD
RU
LT
Malcontenta
Venezia N.
Marghera
Brunsbüttel N
Charlottenburg
Goldisthal
Putzlitz
Lahe
Atla
Benteler
Bertikow
Tarragona
Perafort
Foix
Constant
Bellicens
Castellet
Alqueva
F. Alentejo
Lavos
Maasvlakte
Clauscentren
A,B11
Hessenweg
A,B
Okroglo
TENT B
Beograd 3
Feron
B
Leskovac
Ercolano
Napoli L. AL
Napoli Dir.
Napoli C.
S. Sebastiano
Doganella
Fusina
Villabona
Azotati
Dugale
Taranto SM
ISE CET3
Argia
11
Terrer
Medinaceli
Rueda
Cillam
P.E.SIL
Gurrea
Juneda
Santarem
Smederevo
HIP
2
Arachthos
Monfalcone
AL ZI TE
Giugliano T-Gas
Wattenwil
Flumenthal
Gerlafingen
Zakhidnoukrainska
Volovets
Boryslavl
Stryj
Bursthtyn
Kalush
381
411
381
701
Pietraffita
P. Speranza
Lourizan
F.Alavesas
Segur
Adrall
B
Vallejer
Aravaca
Villanueva del Rey
Casillas
Mory
Lagisza
Grabovica
Rama
Eal
Sarajevo 20
Banja Luka
Prijedor
TPP
5 Kakanj
TPP
HPP
Dubrovnik
HPP
PHPP
Elbasan 1
Zemblak
Sharre
Zerjavinec
Plomin
Virtus
Mataporquera
Anchuelo
la Alcarria
Suido
Puerto
Mangraners
Zaragoza (AVE)
Arganda
Boadilla
Lucero
Mazarred
Casacampo
Campo de las Naciones
Canillejas
Simancas
Etzersdorf
Südburgenland
1D
Mouguerre
Biancon
Hüfingen
(7) B.-Glück
(6)
Rhede
Iven
Seixal1Trajouce
Carriche
S.Rios
Rizziconi
Giswil
Pieve Albignola
Ferrera E
(1)
Pöppings-
hausen
Leverkusen
Eller
(7)
(8) Büscherhof
Fuentes de
D. Rodrigo
Arcos
Pazos de Borben
Chantada
Olite
Franques
Franques
Rojales
Pinto
Parla
Sengraci
Puerto de la Cruz
Aparecida
Casares
Castelo de Bode
Mátra
Skopje 1
Skopje 4
Skopje 5
Podujevo
Meliti
Vetis
La Punt
Sattledt
11 12,13
Ravenna
Canale
Enipower Ravenna
Mikulowa
Neusiedl
Békéscsaba
Weilimdorf
Voghera
All'Acqua
Sedrun
CE
Mantova
Teramo
Altomonte
Enipow. BR
Candela
Vado Ligure
Cesano
E,O
Rise Sesto
Martigues-Ponteau
Lavera
Port de Bouc
Audience
Caillols
Arenc
Mazargues
Rabatau
Saumaty
Augue Septemes
Famars
ESTR621
ESTR621
Braek
Centre
Hirsingue
Volvon
Sainneville
Amfar
Gabion
Barp
6
Cuperly
Seltz
Saumade
Vezilly
Linde
Linde
YSelbeck
Rath
Mühlhausen
Kriftel
(4)
YKUS21
Siems
TIBIS
Oxylux
Esch
Schifflange
2
1
Jundiz
Mas Figueres
Abrera
Campoamor
Alvarado
Olivares
Palos
21
22
1. Basauri
5
2. Abadiano
2
3
5. Amorebieta
3. Azpeitia
4
4. Sidenorb
Escombreras Nuevo Escombreras
6. Collblanc
9
7. Urgell
7
8. Trinitat
6
Besos
Mata
9. Maragall
8
R. Caldes
Petrel
San Pedro del Pinatar
Fausita
Hoya Morena
Saladas
C. Colon
Puerto Real
Poligono
Ribatejo
C. Ribatejo
Zielona Góra
Pécs
Rijeka
Orlovac
Obrenovac
TENT A
Enthes
S. Lucia
Porto Corsini
Niederwil
Altgass
Samstagern
Fällanden
Obfelden
Gargnano
Tor-
bole
Lelystad
GKN
Crans
Mercallo
Olivone
Carpi
Termoli Energia
Rosignano
Roselectra
SET Terver.
SET Terver .
E.-Huchet
Penchard
Wessin
Schönewalde
Monceau
DZO
Muruarte
Morvedre
La Lora
Espluga
Garrafe
Cartujos
Princesa
San Miguel
Torrijos
Jordana
del Rio
Gazules
Puigpela
V.Penedés
Hospitalet
Babcock
Alcocero de Mola
Santiz
15. Aenoeste
16
12
12. Villanova
13. Z. Franca
13
14. Aebarcel
15
14
Torres del Segre
11
11. St. Cugat
Cerve
Pradil
Pinto
Parla
Vall d'Uxo
El Ingenio
Benadres
Aguacate
M. Becerr
C. Freg
Ventas
Fuenlabrada
Mediodia
Ardoz
Aljarafe
P. Serra
Bodiosa
Pontinha
Szombathely
B.B
Jagodina
Sombor
Stip
D. Mostistea
Teleajen
Stâlpu
1
1
LV
SE
DK
GBIE
Kassø
Enstedværket
Revsing
Askær
Ferslev
Vendsysselværket
Vester Hassing
Fynsværket
Kingstrup
Skærbækværk
et
ME
Weiach
Rehag
Pyhrn
Ferrara N.
Bellville
Torviscosa
Sarasdorf
EON L.Ferr
Garigliano
Sparanise
Magisano
RhoPogliano
13
13. Fibe A
Paluel
Sandoville
Nogent-Sur-Seine
Catttenom
Civaux
Montagny Lanches
Golfech
Favary
Belle de Mai
Gravelines
Penly Centrale
Penly Poste
Rele
1
10x
O
Est
Est
Ouest
Le Bugey
Flamanville
Gravelines
Pivoz-Cordier
St. Alban
Darse
Gracieuse
Terres Noires
Altbach
Val de Sevre
Espiers
Gr. Colombier
Suisse
Salles-Curan
Trois Domaines
Geithe
Geithe
Bünzwangen
Endersbach
Kühtai
Unterweser
Alfstedt
Niedervieland
11
12,13,14,15
Van
14
R,12
13
Calders
de Frontera
La Roda de Andalucia
Cabra
Brovales
Tanger
10
10. Eixample
Palmeral
San Vicente
Torre Arenillas
Torrente Quart de Polet
Catadau
F. Muestras
Gaussa
17. S. Fost
18. Codony
Castelo Branco
Penamacor
Pedralva
Biskupice
Bohunice
6
Bgd5
Beograd 17
Pristina
Siebnen
AuwiesenSeebach
A,B
NK
7
22
21
29
29
21
29
22
21
4
Bošáca
Križovany
Gabcíkovo
Šal'a
Vel'ký Dur
Bystricany
Považská
Sucany
Horná Ždana
Široká
Cierny Váh Spišská Nová Ves
Lemešany
Kapušany
Kerzers
Galmiz
Kocin
Zakucac
Bihac
Meduric
Divaca
Klece
Bericevo Krško
Šabac
Pancevo
Cacak
U.Požega
Kruševac
Niš
Bajina Bašta
Gradacac
Capljina
Perucica
Magadino
Manno Belviso
Venina
Cedegolo
Taio
Stazione 4
S.Fiorano
Bovisio
Lanzada
Sud
Villa Tirano
Camuno
Sondrio
22
Bexbach
Herne
Knepper
Irsingen
Westfalen
Voerde
Dülken
Bertrange
Berchem
Plattling
Herrenwyk
Kruseberg
Bjæverskov
Rostock
Interconnected network
Situation January 2011
Neurath
GKW Ost
Farbwerke
Sud
YHAN
KW
Eyck
11
12
Oostzaan
Totana
Hueneja
Beverwijk
Bleiswijk
Albatarrec
La Selva
Vandellos
Palafox
Melancólicos
Puigpela
V.Penedés
Subira
Subira
Castello
Daganzo
La Cartuja
Falagueira
Penela
Carvoeira
Carregado
Fanhoes
Paraimo
Vermoim
Cutoias
Canelas
C.Gas
3
Sines
Pombal
Macedo
Cavaleiros
5
8
7
1
6
Lesniów
21,22
421
Slupsk
Nadab
^
N. Santa
Babaeski
Otel
Koplik
Lindenholz
Bulciago
Mendrisio
Modugno
Gissi
Simeri
S. B. Querce
Cassano
Ganda
1,All
Rossano TE
PE Severin
Terranova
Avold
411
Ostrów
Certosa
Brindisi Città
Palo D.C.
Glorenza
Castelbello
Lasa
Ferrara
Cize
Cycofos
Julien
Hyères
Soullans
Yvelines
Le-Comte
Brailly-Cornehotte
1
Le Soler
Berge
Epizon
Lagaf
Oststeiermark
Vörden
HR8
Walsum
Vill
Maials
Requena
Anoia
La Estrella
Mirasierra
3
Salteras
Cartama
Motors
Beniferri
V. Pouca
de Aguiar
Tábua
NK
E.Coto
Azca
Villameca
Telled
Las Arroyadas
Pujalt
16. Sagrera
17
Visp1
Visp2
Continental Europe
Massstab Format
Zust.St.
Bl.-Nr. Anz.Bl.
Gezeichn.
Gepr.
Änderungen
= +
Geprüft
AutoCADENTSO-E750/380/220kV
/emm
/ngt
07.01.2011
27.09.1996
AM-AN-DA
1SPEZ
PA050024
Situation January 2011
Interconnected network
Continental Europe
r. 07.01.11 /ngt
Cyprus
IL
SY
JO
SA
IQ
IR
AM
KZ
AZ
EE
GE
RU
NO
Lacu
G. lalomitei
Cernavoda
Smirdan
Braila
LB
Tepeören
Atisalani
Osmanca
Eregli
Cayirhan
Seyitömer
Seydisehir
Afyon
Oymapinar
Varsak
Aksa
Konya
Sincan
Temelli
Habipler
Ikitelli
Ambarli
Kücükbakkalköy
Hamitabat Alibeykoy
Izmir DGKC
Uzundere
Germencik
Yenikoy
Kemerkoy
Denizli
Soma B
Can
Balikesir II
Bursa Sanayi
Bursa
Tuncbitek
Adapazari
Adapazari 2DGKC
Erzin
Adana
Isdemir
Andirin
Goksun
Kapasitör
Agacoren
Ataturk
Diyarbakir 3
Karakaya
Kangal
Keban
Özlüce
Erzurum
Batman
5
Birecik
Kiziltepe
Elgun
Elbistan B
Ürgüp
Elbistan A
Carsamba
Tirebdu
Altinkaya Sant.
Hasan Ugurlu
Gaziantep 2
Borçka
Deçeko
Gokçekaya
^
Aliaga
^
Yatagan
Isiklar
Pasaköy
Gölbasi
Yesilhisar
Atakas
Kayabasi
Manisa
Karadeniz Eregli
Hilvan
W O
ALL
Abbadia
21
Morbihan
10x Grillon
Richier
Provence
Self
G. Riv
H
Malchow H
H
H
H
Stendal West
Osterrath
Osburg
Berneau
Navagne
Lagoaça
Aldeadavila
Bescano
Vva. Escuderos
Minglanilla
Belinchón
Carmona
6
6. Zamudio
Els Aubais
Albal
Bechi
Casaquemada Almodovar
El Cereal
18
Ebora
Carroyosa
Portimão
C. Lares
Armanar
2
PBRS
PADR
4
9
Košice
Malženice
Gönyü
PT
PEC
Heron
Maritsa 1
Tariv
Sarat
Calea Aradului
Babic
Titan
Elkurum
Arvedi
Mincio
P
P
P
P
P
P
P
P P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
C
Amigne
Mayrhofen
SMH
Sint Laurens
MSEC
Göynük
Sanliurfa
Yildiztepe
Adapazari 1DGKC
Baglum
Karabiga
Bandirma
Elbistan TES
T Bara
Toscelik
Zekeriyaköy
Beykoz
Kursunlu
Keban HES
Soma RES
Sugözü
Agri
Kaptan Celik
Unimar
Davutpasa Ümraniye
Yolbulan

23. Dynamics in the Continental European System
February 19th 2011, Frequencies
23
49.85
49.9
49.95
50
50.05
50.1
50.15
08:08:00 08:08:10 08:08:20 08:08:30 08:08:40 08:08:50 08:09:00 08:09:10 08:09:20 08:09:30 08:09:40 08:09:50 08:10:00
f[Hz]
20110219_0755-0825
Freq. Mettlen Freq. Brindisi Freq. Wien Freq. Kassoe
Courtesy of Dr. Walter Sattinger

24. Dynamics in the Continental European System
February 19th 2011, Frequencies
24
49.85
49.9
49.95
50
50.05
50.1
50.15
08:08:00 08:08:10 08:08:20 08:08:30 08:08:40 08:08:50 08:09:00 08:09:10 08:09:20 08:09:30 08:09:40 08:09:50 08:10:00
f[Hz]
20110219_0755-0825
Freq. Mettlen Freq. Brindisi Freq. Wien Freq. Kassoe
Courtesy of Dr. Walter Sattinger
0
200
400
600
800
1000
1200
08:08:00 08:08:10 08:08:20 08:08:30 08:08:40 08:08:50 08:09:00 08:09:10 08:09:20 08:09:30 08:09:40 08:09:50 08:10:00
P[MW]
20110219_0755-0825
Sils -Soazza Robbia-Gorlago Robbia-San Fiorano Mettlen-Lavorgo

25. Dominant Inter-Area Oscillation Paths
Interaction Paths
The group of transmission
lines, buses, and controllers
which the generators in a
system use for exchanging
energy during swings.
[J. Hauer]
25

26. Definition
The passageway containing
the highest content of the
inter-area oscillations
Dominant Inter-Area Oscillation
Paths can be characterized by
using Network Modeshape
26
Dominant Inter-Area Oscillation Paths

27. Observability of System Dynamics
Projecting the eigenvector to measurement output variables
27
Linearizing around an
equilibrium point
Right Eigenvector/
Modeshape
Network
sensitivities

28. Network Modeshape
Network sensitivities
Modeshape

29. Illustration: System with 1 Dominant Path
Important Features
The largest SV or the smallest S
element(s) indicates the center of
the path.
The difference between S elements
of two edges of the path is largest
among any other pair within the
same path.
SV elements of the edges are the
smallest or one of the smallest
within the path
29
1 234 5
1
G 2
G

30. 30
Feedback Properties and Time Delay Impacts
1 234 5
1
G 2
G
Time delay ≈ phase lag
Inter-area frequency
Δθ45

31. 31
Feedback Properties and Time Delay Impacts
1 234 5
1
G 2
G
Time delay ≈ phase lag
Inter-area frequency
PSS Δθ45

32. Dominant Path Signals: Delay Margin
32
1 234 5
1
G 2
G
Voltage Angle
Difference |θij|
PSS
Delay Margin (DM) is defined as the
smallest time (for Td > 0) required to
destabilize the closed-loop system.
0
200
400
600
800
1000
1200
θ14 θ43 θ45 θ42
Delay Margin (ms)
494 ms
- Delay margin = upper bound for the design of
WAPOD
- Although it’s attractive to use the signals with
the largest observability, we found that these
signals result in a smaller delay margin.

33. Methods:
Identification of Dominant Paths
• Current magnitude modeshapes indicate how much of the
contents of the inter-area modes are distributed among the
transfer corridors.
• Thus, for particular inter-area oscillations, corridors having the
highest content are the paths where the inter-area modes travel to
the most.
• We propose an ambient-data based algorithm:
Pre-processing
PMU
measurements
Filter the meas. so
that only inter-area
modal content is
preserved.
Compute PSD
Select appropriate
window
Sort the contents
of current meas.
in descending
order
Dominant
Paths
Verify the characteristics of the
dominant paths

34. Study System: KTH-Nordic32
• Nordic grid ‘conceptual’
model
• 20 generators 52 buses
• Small-signal stability
analysis using eigenanalysis
• Eigenanalysis: 2 poorly
damped inter-area
oscillations:
• 0.5 Hz (Mode 1) and
• 0.73 Hz (Mode 2)

35. Algorithm: Ambient Data
Pre-processing PMU
measurements
Compute PSD
Sort in descending
order Dominant Paths
35

36. 36
0.50-Hz Mode0.73-Hz Mode
October 22,
2012
Dominant Paths

37. Known
System
(ABCD)
Transient
measurements
Ambient
measurements
Model-
based
Algorithm
Transient-
based
Algorithm
Ambient-
based
Algorithm
Dominant
Paths (for
individual
inter-area
oscillation)
PMU
placement
Controller
placement
Feedbackinputsignals
Wide-Area
Damping
Control
Overview of the Algorithms

38. Discussion
Algorithms Comparison
 Synchrophasor measurements should be used as the primary
source of information while model-based algorithm can be used
for planning studies since it is difficult in practice to know the
exact model of the system.
0.73-Hz Mode0.50-Hz Mode

39. Tools:
Need for real-time monitoring of system dynamics.
(Data from the 1996 WECC Break-up)
0.27 Hz
7% Damping
(Ambient)
0.264 Hz
3.46%
Damping
(Transient)
0.252 Hz
1.2%
Damping
(Ambient)
350 400 450 500 550 600 650 700 750 800
1100
1200
1300
1400
Time (sec.)
PowerTransfer(MW)
Alarm
System
Unsable
15:42:03 – K-A Line
Trip
15:47:36 – R-L Line
Trip
McNary Generation
Trip
15:48:51
Out-of-step
Separation

40. • Infrastructure (external to the software development kit):
- PMU: phasor measurement unit.
- PDC: phasor data concentrator. A software running in a dedicated server .
- Communication Network: composed by routers/switches, fiber optic links (or other medium)
• Software Development Kit (SDK): a set of different computer software that allows
a user to develop other derived software applications.
Computer/Server
Statnett’s Synchrophasor
Software Development Kit (SDK)
Real-Time
Data
Mediator
(RTDM)
“DLL”
LabView
PMU Recorder Light
(PRL)
PMU 1
PMU 2
PMU n
PDCCommunication
Network
Infrastructure
Data in IEEE C37.118 Protocol
Tools:
Synchrophasor Software Development Kit

41. SDK is composed by two main parts:
• Data Collector
- DLL reads data from PDC/PMU filling the
Live Buffer
- Access Buffer keeps the data
• Data Extractor
- Queue Handler enables reading the Live
buffer
- Buffer Handler enables reading the Access
Buffer
- Selector is a user interface for signal
selection
PDC
DLL
Live Buffer
Access Buffer
Remote
Buffer Handler
Selector
C37.118
Data
Data
Collector
Data extractor (LabView PMU control)
· Time step
· Voltage Phasor
· Current Phasor
· Frequency
PRL Library
Queue Handler
Software architecture
Program
interface
SDK Software Architecture and Implementation

42. SDK Monitoring and Options
PRL Main GUI Connection Settings Buffers and Queue
Bad Data Advanced

43. Ambient Data-Based Mode Estimation – Principle
• Objective: To estimate modes frequency and damping ratio
• Assumptions:
- System is excited only by small load changes
- Load changes are modeled as white noise (no forced oscillations)
- Behavior of the system is modeled by a linear model
Power System
H(f)
Load Changes Measurements
43

44. Mathematical Model
Yule-Walker’s Method
44
• Model of the measured stochastic process
• Yule-Walker Equations (assuming that e(k) is white noise process)
0
1
( )
( ) ( ) ( )
( )
1
q
i
i
i
p
i
i
i
b z
B z
y k e t e k
A z
a z1 0
( ) ( ) ( )
p q
i i
i i
y k a y k i be k i
       
     
      
       
     
1
( ) ( 1) ( 1)
( 1) ( ) ( )p
r q r q p a r q
r q p r q a r q p
1
0
1
( ) ( ) ( )
N n
k
r n y k n y k
N
 

 

45. Implementation of the
Real-Time Mode Estimation Application
•State machine architecture
•Program interface structure
•Interface example – Time
domain signal
•Interface example – Options
Data Acquisition
Initialization
Read real-time
data
Read
simulated data
ARMA model
coefficients
Wait Reports
Preprocessing
Signal
generator
PDC/SDK

46. Experimental Testing
•Synthetic measurement data
- KTH Nordic 32 Test system
- Software (only) simulation
- Hardware in the loop experiment using
real time simulator (KTH Nordic 32 Test
system implemented in the simulator)
- Frequency signal obtained using National
Instruments cRIO PMU
•Real field measurement data
- Real-time measurements from the actual
Nordic Grid

47. Results - Synthetic Data
Real
Mode
Hardware
in the loop
Software
simulation
Mean {f}
[Hz]
0.4987 0.5073 0.4985
Mean {}
[%]
3.5223 4.0910 3.6905
Var {f} - 1.1037e-04 7.5797e-05
Var {} - 1.9088 1.7184
Stochastic properties of the mode estimator
• Spectrogram and spectrum
• Complex plane of the system
• Damping ratio evolution
• The 0.5 Hz mode is analyzed
• Statistical properties are
analyzed by 120 independent
estimations

48. Results -
Real-time measurements from the Nordic Grid
• The critical mode is at 0.39 Hz
with damping ratio 9 % (in
average)
• Other modes are observable
at 0.2 Hz, 1 Hz and 1.4 Hz
• 0.5 Hz mode sporadically
appears as a poorly damped
mode

49. • Development of a phasor-
based damping control
• Algorithm for excecution on
FPGA
• Implementation and
performance assessment of
Power Oscillation Damping
Controller deployed in NI-
cRIO
• Major Issues encountered:
Signal-to-Noise Ratio of
Analog Output Modules of
Opal-RT, SNR of Analog
output modules of NI-cRIO
OPAL-RT
2
Real-Time Digital simulation is converted
to Analog / Digital Signals through I/O s
1
RealTime simulations are accessed from the
console generated by OPAL-RT Lab software
Ethernet Switch
SEL-421 (PMU)
SEL-5073 (PDC)
Concenterates all the PMU
streams, time-alligns them and
creates a single PDC stream
BabelFish
Unwraps the PDC stream and provides
Raw Data in Labview to develop
visualization and control applications
based on PMU measurements
TCP
UDP
Shared Variables
Analog Outputs of the RTS are wired to
the CT/VT inputs of the PMUs
3
4
The PMU streams are fed to the
Network switch and these
streams are made accessible to
the Phasor Data Concentrator
56
7 NI-cRIO executes control algorithms
based on PMU measurements and
sends the control actions to the RTS
SEL-487E (PMU)
Generator
1000 MVA
13.8 kV
Step Up
Transformer
13.8:500 kV
1000 MVA
Transmission
Line 350 km
Transmission
Line 350 km
Load
500kV
5000 MW
Bus 1 Bus 2 Bus 3
Generator
Step Up
Transformer
13.8:500 kV
5000 MVA
5000
MVA
13.8 kV
SVC
Bus 2_SVC
SVC Controller for Power
Oscillation Damping
SEL-421 (PMU)
Phasor POD
Algorithm
deployed in
NI-cRIO
Tools:
Real-Time Control of Oscillations

50. Result from
Simulink
software model
Result from RT-HIL with NI-cRIO
Power oscillation
damping (Active
Power Transfer
From Area 1 to
Area 2
Software Model vs. Hardware
Implementation
Time-delay
impact

51. Methods and SW for
balancing, re-distpach
and power flow control
considering variable
sources
Methods, SW and HW to
Improve Stability,
Security and Control
Power System with Hybrid AC/DC
Transmission System
and variable generation sources and
flexible loads
Miliseconds, Seconds,
Minutes
Minutes, Hours, Days Days, Months, Years
Time Scales
Methods and SW Tools
considering variable sources
and transmission
resource adequacy, power
transfer optimization, and
optimized grid development
ICT System for Messaging, Data Management, etc.
Communication and Information Technologies
Electricity Market
Legislation & Regulation
Looking forward – Smart Operation Solutions Needed!
Smart Operation Solutions
Operation,
control, and
protection actions
Coordinated control
execution cycles
Data
Flow
Market effects
transmitted through
ICT network
Market events
transmitted in
physical process
dynamics

52. Methods and SW for
balancing, re-distpach
and power flow control
considering variable
sources
Methods, SW and HW to
Improve Stability,
Security and Control
Power System with Hybrid AC/DC
Transmission System
and variable generation sources and
flexible loads
Miliseconds, Seconds,
Minutes
Minutes, Hours, Days Days, Months, Years
Time Scales
Methods and SW Tools
considering variable sources
and transmission
resource adequacy, power
transfer optimization, and
optimized grid development
ICT System for Messaging, Data Management, etc.
Communication and Information Technologies
Electricity Market
Legislation & Regulation
Looking forward – Smart Operation Solutions Needed!
Operation,
control, and
protection actions
Coordinated control
execution cycles
Data
Flow
Market effects
transmitted through
ICT network
Market events
transmitted in
physical process
dynamics

53. A future vision for a Smart Operation Control System
Blending Measurement/Data-Based and Model-Based Analysis Methods and Tools
Model-Based Dynamic
Analysis and Control Tools
Measurement-Based Dynamic
Analysis and Control Tools
Conventional Static Analysis Tools
SCADA/EMS
Preventive Protection and
Control Actions
Emergency Protection and
Control Actions
PMU & IED
Data
SCADA/EMS,
PMU & IED
Data
Real-Time Protection
and Control
Dynamic
Models
OptimizationAnalysisTools
Other Data
Other
Data

54. Challenges - Scientific work to Support the Smart Operation Vision
Hybrid Measurement-and-Model-Based Tools for Smart Operation
• There is a need to coherently combine real-time operation and control tools for:
- Traditional static analysis tools and SCADA/EMS functions:
• Provide steady state limits and control
– Efficient computation techniques are needed for equilibrium and boundary calculations (near-real-time).
- Dynamic analysis and control tools based on models
• Provide corrective and preventive actions based on results predicted from models
– Uncertainty needs to be modeled and computation approaches considering uncertainty need to be developed.
– Simulation and control design methods & tools, considering also ICT, need to be developed.
– Techniques for model identification, reduction and validation are needed.
- Dynamic analysis and control tools based on measurements
• Identify in real-time problems in the system, provide immediate relief for real-time protection and
feeback control
– Signal processing methods coping with real-time constraints.
– Algorithms for feature extraction and pattern recognition (“Data Analytics”)
– Near-real-time methods for model identification and stability computations (“Data Analytics”)
– Partly-distributed / partly-centralized control systems and laws need to be
- Optimization methods and tools that can exploit both models and measurements
• Determine preventive and corrective actions to provide higher exploitation and stability of the grid
– Solution of NP hard problems involving large interconnected systems with continuous and discrete variables.
– Computational techniques exploiting HPC and cloud.

55. References – Smart Operation, Concepts, and Lab
• About Smart Operation state of the art, challenges and vision:
- L. Vanfretti, D. Van Hertem and J. O. Gjerde, “Smart Transmission Grids vision for Europe”, Book Chapter, in Smart Grids and
Sustainable Energy Transformation, Editors: P. Hills, Daphne Mah, Victor O.K. Li, Richard Balme, Springer, 2014.
http://www.springer.com/energy/policy,+economics,+management+%26+transport/book/978-1-4471-6280-3
- L. Vanfretti, M. Baudette, and A. White, “Monitoring and control of renewable energy sources using synchronized phasor
measurements,” Book Chapter, in Renewable Energy Integration: Practical Management of Variability, Uncertainty and Flexibility in
Power Grids, Editor: Lawrence E. Jones, Elsevier, 2014. http://www.barnesandnoble.com/w/renewable-energy-integration-lawrence-
e-jones/1117605149?ean=9780124079106
• About some of the concepts presented:
- L. Vanfretti, Y. Chompoobutrgool, and J.H. Chow, “Chapter 10: Inter-Area Mode Analysis for Large Power Systems using
Synchrophasor Data”, Book Chapter, in Coherency and Model Reduction of Large Power Systems, Joe H. Chow (Ed.), Springer, 2013.
http://www.springer.com/energy/systems%2C+storage+and+harvesting/book/978-1-4614-1802-3
- Y. Chompoobutrgool and L. Vanfretti, “Analysis of Time Delay Effects for Wide-Area Damping Control Design using Dominant Path
Signals,” IEEE PES General Meeting, 2014, National Harbor, MD (Washington, DC Metro Area).
• About our “SmarTS Lab”, Testing and Validation Methods:
- L. Vanfretti, et al, “SmarTS Lab: A Laboratory for Developing Applications for WAMPAC Systems,” IEEE PES General Meeting 2012,
San Diego, CA, USA.
- L. Vanfretti, M. Baudette, I. Al-Khatib, M. S. Almas, and J. O. Gjerde, “Testing and Validation of a Fast Real-Time Oscillation Detection
PMU-Based Application for Wind-Farm Monitoring,” Invited Paper, Technical Session, Track 5: Communication and Control in Smart
Grids, in Proceedings of the First International Black Sea Conference on Communications and Networking 2013 (BlackSeaCom 2013),
Batumi, Georgia.
- M. Baudette, L. Vanfretti, G. Del-Rosario, A. Ruiz-Alvarez, J.L. Dominguez-Garcia, I. Al-Khatib, M. Shoaib Almas, I. Cairo, and J.O.
Gjerde, “Validating a Real-Time PMU-Based Application for Monitoring Sub-Synchronous Wind Farm Oscillations,” IEEE ISGT 2014,
Washington, DC, Feb. 19-22, 2014

56. References – Methods and Tools
• About some of the methods developed:
- V. Peric and L. Vanfretti, “Power System Ambient Mode Estimation Considering Spectral Load Properties,” IEEE Transactions on
Power Systems, in press. Accepted November 2013. Available for early access: DOI: 10.1109/TPWRS.2013.2292331
- Yuwa Chompoobutrgool and L. Vanfretti, “Identification of Power System Dominant Inter-Area Oscillation Paths,” IEEE Transactions
on Power Systems, vol. 28, no. 3, pp. 2798 – 2807, Aug. 2013. DOI: http://dx.doi.org/10.1109/TPWRS.2012.2227840
- C. Sturk, L. Vanfretti, Y. Chompoobutrgool, and H. Sandberg, “Non-coherency based structure model reduction of power systems,”
IEEE Transactions on Power Systems. August 2013. Revision, Dec. 2013. Accepted, January 2014. Availabe for early acces:
http://dx.doi.org/10.1109/TPWRS.2014.2302871
- N.T. Anh, L. Vanfretti, D. Van Hertem, and J. Driesen, “A Quantitative Method to Determine ICT Delay Requirements for Wide-Area
Power System Damping Controllers,” submitted, IEEE Transactions on Power Systems, March 2014.
• About some of the tools implemented:
- L. Vanfretti, M. Baudette, J.L. Dominguez, A. White, I. Al-Khatib, M.S. Almas, and J.O. Gjerde, “A PMU-Based Fast Real-Time
Oscillation Detection Application for Monitoring of Wind Farm Dynamics,” submitted, Electric Power System Research (Elsevier),
February 2014.
- M.S. Almas, M. Baudette, L. Vanfretti, S. Løvlund and J.O. Gjerde, “Synchrophasor Network, Laboratory and Software Applications
developed in the STRONg2rid project”, IEEE PES General Meeting, 2014.
- L. Vanfretti, T. Bogodorova, and M. Baudette, “A Modelica Power System Component Library for Model Validation and Parameter
Identification,” 10th International Modelica Conference 2014, Lund, Sweden, Mar. 10 – 12, 2014.
- V.S. Peric, L. Vanfretti, M. Baudette, J.O. Gjerde and S. Lovlund, “Implementation of a Real-Time Mode Estimation Algorithm using
Ambient Synchrophasor Data,” Power Systems Conference, Clemson University, Clemson SC, 2014.

57. Thank you!
Questions?
http://www.vanfretti.com
luigiv@kth.se
luigi.vanfretti@statnett.no