Synchrophasor Applications

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Synchrophasor Applications Facilitating Interactions between Transmission and Distribution Operations

Luigi Vanfretti Associate Professor, Docent

KTH Royal Institute of Technology, Stockholm, Sweden

https://www.kth.se/profile/luigiv

Thursday February 9th 2017


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Acknowledgements

• The work in this presentation is result of the research carried @KTH SmarTS_Lab in the FP7 IDE4L project (2014-2016) and other projects form 2011-2016.

• Special thanks to Dr. Hossein Hooshyar, who undertook the day-to-day project management of this project at KTH SmarTS Lab. with great dedication.

• All of the following students contributed to this presentation with their hard work!

29/11/2016 WWW.IDE4L.EU SLIDE 2

Hossein Reza Ali Farhan Ravi Narender Maxime Jan

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Outline • Introduction: PMUs in distribution networks?

• PMU Application Use Case Definition using SGAM

• Methodology and Experimental Lab Testing • Development, Implementation and Testing

using RT-HIL Simulation

• The Reference Distribution Grid Model

• Handling PMU Data: • Getting the data: The Khorjin Gateway and The S3DK Toolkit

• Cleaning the data: PMU Data Feature Extraction

• Using the Data: Applications • Steady State Model Synthesis

• Dynamic Line Rating for Distribution Feeders

• Small-Signal Dynamic Analysis

• Conclusions


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Introduction (1/2) • It is becoming necessary to increase observability between T&D

grids, emerging dynamics active distribution networks due to renewables will lead to

• Fast changing conditions in the network

• Fast behavior of components

• Traditional monitoring technology not capable of satisfying the technical requirements to meet these conditions: types of signals, time-synchonization and speed of data acquisition

• There is great potential of utilizing real-time Synchrophasor data from PMUs (Phasor Measurement Unit(s))

• From different actors, voltage levels and across conventional operation boundaries.

• to extract key information related to fast changing conditions & dynamic behavior,

• To use such information adequately, a properly designed and implemented architecture needs to be considered.

SLIDE 4 29/11/2016 WWW.IDE4L.EU

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Introduction (2/2)

• The IDE4L architecture is built upon the 5-layer Smart Grid Architecture Model (SGAM) framework.

• The main inputs to the architecture

design are the use cases.

• The IDE4L use case, including

the PMU-based functions for

dynamic information

extraction and exchange, is presented in

this presentation.


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DistributionTrans.

Pro

cessField

Station

Op

eration

Enterp

rise

PMU(distributed)

Transducer(PS/SS/distributed)

Electricalconversion

(PS/SS/distributed)

Synchrophasorcalculation

(distributed)

SynchrophasorCalculation

(PS/SS)PMU

(PS/SS)Measurement

acquisition(PS/SS)

Communication interface

(PS/SS)

Data concentration

(PS/SS)PDC

(PS/SS)

DMSat

DSO

Data curation and extraction of components

Partial derivation of

key information

Data transfer

TSO

Computation unit(PS/SS)

Measurement acquisition

Communication interface

Data curation and extraction of components

Derivation of key information

Data export

Gen. DER Cost.Prem.

• Data flows from PMUs => PDC => Super PDC => DMS computers (where dynamic information is extracted) and exchanged with the TSO (possible also w. DSOs).

• Data processing and information extraction can occur at both the station (partially) and the operation (i.e. DMS computers) zones.

• Actors: Instrumentations, PMU, PDC, communication interface, DMS, and TSO

• Functions: electrical conversion, synchrophasor calculation, data acquisition, data concentration and time-alignment, data exporting, data curation, and derivation of key information out of the data (aka PMU applications).


Use Case Definition

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• The development of the applications has been carried out using real-time hardware-in-the-loop simulation consisting of:

• A real-time simulation model of active distribution networks.

• The real-time simulation model is interfaced with PMUs in HIL.

• PMU data is streamed into a computer workstation through PDC.


Development, Implementation and Testing using

RT-HIL Simulation

• The computer makes available to specialized software development tools within the LabVIEW environment.

• All data acquisition chain is carried out using the corresponding PMU standards.

ideal grid for all SLIDE 8 29/11/2016 WWW.IDE4L.EU

The

Ref

eren

ce D

istr

ibu

tio

n

Gri

d M

od

el (

1/2

)

101

100

102 103

104

105

106 800 802

806 808

810

812

814 850 816

818

820

822

824 826

828830 854

856

852

832

858

864

834

842

844

846848

860 836 840

862

838888

890/799

701

+

-

+

-

702 713 704

714

718 725

706

720

707722

724

705

712

742

703

727744

728

729

730

709 731708732

775

733

734

736

710

735 737

738 711 741

740

0.4

kV

220 kV

36 kV

36 k

V

36 kV

6.6 kV

6.6 kV

6.6

kV

Electric power system

ZIP load

Motor load

Voltage regulator

Wind farm

PV farm

Residential PV system

Battery storage+

-

Capacitor bank

Circuit breaker

Overcurrent protection

Recloser

Legend

Component Number of units

1 three-phase unit

11 three-phase units, 56 single-phase

units

2 three-phase units, 3 single-phase units

3 sets each consisting of 3 single-phase

units

3 three-phase units

2 three-phase units

3 single-phase units

2 three-phase units

11 single-phase units

1 three-phase units

1 set consisting of 3 phase relays and 1

ground relay1 three-phase unit, 2 sets each consisting

of 3 single-phase units

Rated Voltage Number of branches

220 kV 7 three-phase

36 kV 28 three-phase, 8 single-phase

6.6 kV 39 three-phase

HV

MV

LV

0.4 kV 1 three-phaseRLV

No. of buses

6

34

37

2

Roy Billinton

Transmission

Test System IEEE 34

bus test

feeder

IEEE 37

bus test

feeder

RLV DG

and Load

Steam turbine

Governor

ωm

PSS

Excitation System

V

dω

System load

Output feeder

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The Reference Distribution Grid Model (2/2)

• Runs on 4 cores using the State-Space-Nodal (SSN) solver with time-step of 100μs.

• The two different implementations of the grid model can be downloaded from the KTH SmarTS Lab GitHub repository at

• The SSN implementation is also available in the ARTEMiS 7.0.5 demo section of Opal-RT.


https://github.com/SmarTS-Lab/FP7-IDE4L-KTHSmarTSLab-ADN-RTModel

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• Methodology and Experimental Lab Testing • Development, Implementation and Testing using RT-HIL

Simulation







• Conclusions


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IEEE Std C37.118.2

Today’s Architecture

Deployment Time Guesstimate: ~15-20 Years

Transition

Likely Future Scenario

Security Not Addressed

IEEE Std C37.118.2

Application System

PDC

PMU 1

PMU 2

PMU n

PDC PDC PDC

Communication Network

PMU 1

PMU 2

PMU n

PDC PDC PDC

IEC 61850-90-5

Application System

PDC

Fulfills the Gaps Not addressed in the

IEEE Std C37.118

e.g. Security Enhancement

Harmonization with IEC 61850

Security Not Addressed

Two Segregated Systems Two Protocols (Even in the same substation) It will be a huge CHALLENGE to adopt IEC 61850-90-5 Standard

Need of Interfaces @PMUs, @PDCs, @App Sys,… How to maintain this ?

Getting the Data… in the Advent of IEC TR 61850-90-5 Standard Specification

Application System

PDC

PMU 1

PMU 2

PMU n

PDC PDC PDC


ideal grid for all One possible approach and our contribution:

Khorjin • A Gateway:

• Can act as the IEEE C37.118.2 to IEC 61850-90-5 protocol converter.

• Providing future compatibility for legacy devices already in the field

• Capable of being used at various levels:

• @PMU Level

• @PDC Level

• @Application Level

• …

• Gateway Application and Implementation Test:

Wide-Area Controller

Application System

Application System

PDC

PMU

PDC


IEEE C37.118.2 Compatible IEDs

and APPs

IEC 61850-90-5 Gateway

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Seyed Reza Firouzi [email protected]

Receiver

Gateway

National Instrument

CompactRIO PMU

Wireshark captures

IEC 61850-90-5

R-SV / R-GOOSE

Khorjin Gateway Testing

• Test of the gateway implementation was tested using real-time HIL simulation • Data is transformed from IEEE C37.118.2 from actual PMUs, and published using the IEC

61850-90-5 protocol

ideal grid for all Khorjin Gateway Architecture

• The Gateway functionality of “Khorjin” library is getting use of its modular architecture:

Easier for future development

• The Khorjin Gateway is designed and implemented in three main components of:

1) IEEE C37.118.2 Module,

2) IEC 61850 Mapping Module, and

3) IEC 61850-90-5 R-SV / R-GOOSE Publisher Module.

14

• In order to be platform-independent: A Platform Abstraction Layer is Implemented.

Depending on the platform, the relevant

platform-dependent functions are utilized

(i.e. Socket, Thread, Time and …).

• The Khorjin Gateway is designed and implemented in three main components of: 1) IEEE C37.118.2 Module, 2) IEC 61850 Mapping Module, and 3) IEC 61850-90-5 R-SV / R-GOOSE Publisher Module.

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• The Khorjin library is an Open Source code providing following functionalities:

IEEE C37.118.2 Traffic Parser

IEEE C37.118.2 to IEC 61850-90-5 Protocol Converter

IEC 61850-90-5 Traffic Generation

Routed-Sampled Value

Routed-GOOSE

IEC 61850-90-5 Traffic Parser

Routed-Sampled Value

Routed-GOOSE

• The Khorjin library supports different platforms.

Will be publicly available sometime in 2017,

keep an eye on our Github repo: https://github.com/SmarTS-Lab-Parapluie

Khorjin

Windows

Linux

Mac

NI cRIO

Raspberry Pi

15 Khorjin Functionalities – it’s more than a gateway!

https://github.com/SmarTS-Lab-Parapluie







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• PMU SW Apps require real-time data acquisition. • PMU data is sent to these SW Apps using many different comm. protocols. • For fast software prototyping and testing, communication protocol parsing

is required. • Low level data management routines (windowing, etc.) do not need to be

reinvented N times. • A tool-set is needed to assist students and researchers with a background

in power systems, but lacking proficient software development and programming skills.

PMU 1

PMU 2

PMU n

PDC Communication Network

Infrastructure

Data in IEEE C37.118 Protocol

Real-time data locked into vendor specific

software system

Historical Data in Proprietary Database

Interfaces using standard protocols and a

flexible development

environment are needed

Last Mile in PMU App Development

16 The next step…

Now you got the data… how do you implement an application?

ideal grid for all S3DK Smart grid Synchrophasor Software Development tool-Kit (SDK)

• Infrastructure (external).

• Software Development Kit (SDK): a set of different computer software that allows a user to develop other derived software applications.

• Our SDK is composed by two main pieces: • Real-Time Data Mediator (DLL)

• PMU Recorder Light (PRL)

Computer/Server

S3DK

Real-Time Data

Mediator (RTDM) “DLL”

LabView

PMU Recorder Light

(PRL)

PMU 1

PMU 2

PMU n

PDC Communication Network

Infrastructure

Data in IEEE C37.118 Protocol

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• A library in C++ was implemented with the following architecture design in mind

Client Applications (PRL)

Synchronization Layer

IEEE C37.118 Server

IEC 61850 Server

Other protocols

Only prepared, not fully implemented.

Future Support / Integration

IEEE C37.118 Client

IEC 61850 Client

Other protocols

Console Test Tool

PMU or PDC 1 PMU or PDC n

Interface

Implemented and Tested

Other Protocol Servers

Clients

Servers

IEEE C37.118

DLL

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GUI

Many blocks to access and handle

RT data!

Graphical User Interfaces

Communication Configuration

Development Tooling

S3DK – LabView API, UI and Tools

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S3DK’s LabVIEW Function Library/Toolbox/Pallete (aka PRL)

ideal grid for all Repositories Currently Available at GitHub!

• Main repo: https://github.com/SmarTS-Lab-Parapluie/

• S3DK: https://github.com/SmarTS-Lab-Parapluie/S3DK

• BabelFish: https://github.com/SmarTS-Lab-Parapluie/BabelFish

• Khorjin: Will be available at GitHub sometime in 2017. L. Vanfretti, V. H. Aarstrand, M. S. Almas, V. S. Perić and J. O. Gjerde, "A software development toolkit for real-time synchrophasor applications," PowerTech (POWERTECH), 2013 IEEE Grenoble, Grenoble, 2013, pp. 1-6. doi: 10.1109/PTC.2013.6652191 L. Vanfretti, I. A. Khatib and M. S. Almas, "Real-time data mediation for synchrophasor application development compliant with IEEE C37.118.2," Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, Washington, DC, 2015, pp. 1-5. doi: 10.1109/ISGT.2015.7131910 L. Vanfretti, M.S. Almas and M. Baudette, “BabelFish – Tools for IEEE C37.118.2-compliant Real-Time Synchrophasor Data Mediation,” SoftwareX, submitted, June 2016. S.R. Firouzi, L. Vanfretti, A. Ruiz-Alvarez, F. Mahmood, H. Hooshyar, I. Cairo, “An IEC 61850-90-5 Gateway for IEEE C37.118.2 Synchrophasor Data Transfer,” IEEE PES General Meeting 2016, Boston, MA, USA. Pre-print: link. S.R. Firouzi, L. Vanfretti, A. Ruiz-Alvarez, H. Hooshyar and F. Mahmood, “Interpreting and Implementing IEC 61850-90-5 Routed-Sampled Value and Routed-GOOSE Protocols for IEEE C37.118.2 Compliant Wide-Area Synchrophasor Data Transfer,” Electric Power Systems Research, 2017. G.M. Jonsdottir, E. Rebello, S.R. Firouzi, M.S. Almas, M. Baudette and L. Vanfretti, “Audur – Templates for Custom Synchrophasor-Based Wide-Area Control System Implementations,” SoftwareX, in preparation, 2016.

https://github.com/SmarTS-Lab-Parapluie/





https://github.com/SmarTS-Lab-Parapluie/S3DK







https://github.com/SmarTS-Lab-Parapluie/BabelFish







https://www.dropbox.com/s/n0i2s1bngj7fdbg/Reza_iec61850.pdf?dl=0


Cleaning the Data!

PMU Data Curation, Extraction of Different Time-Scale Components

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PMU Data Processing (1/3)

• PMU measurements are normally polluted with

• Undesirable noise

• Outliers

• Missing data

• Measurements obtained from PMUs during different events in power systems contain different signal features at different time scales, i.e. features of different types of power system dynamics.

• So before being fed to the applications: • PMU measurements should be curated.

• Signals required for each application should be extracted from the PMU measurements.


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PMU Data Processing (2/3) • An enhanced Kalman filtering technique has been utilized for both bad

data removal, i.e. data curation, and to extract different time-scale dynamics from the PMU data (ie used as a real-time filter)

• R and Q (the measurement and process noise covariance matrices), can be updated in real-time to perform the data processing.


Step Equation Significance

1 Prediction 𝑥 𝑘− = 𝐴𝑥 𝑘−1

− + 𝐵𝑢𝑘 Project the state ahead.

2 𝑃𝑘− = 𝐴𝑃𝑘−1𝐴

𝑇 + 𝑄 Project the error covariance ahead.

3 Correction 𝐾𝑘 = 𝑃𝑘−𝐻𝑇 𝐻𝑃𝑘

−𝐻𝑇 + 𝑅 −1 Compute the Kalman gain.

4 𝑥 𝑘 = 𝑥 𝑘− + 𝐾𝑘(𝑧𝑘 − 𝐻𝑥 𝑘

−) Update estimate with measurement zk.

5 𝑃𝑘 = 𝐼 − 𝐾𝑘𝐻 𝑃𝑘− Update the error covariance.

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PMU Data Processing (3/3) • Sample results.


0 20 40 60 80 100 120 140 160-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Time (sec)

Vm

ag (

p.u

.)

Raw data

Curated data

Steady state component

Dynamics

0 20 40 60 80 100 120 140 160-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Time (sec)

Vang (

rad.)

Raw data

Curated data

Steady state component

Dynamics

voltage magnitude (per unit) voltage angle (radian)

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• Methodology and Experimental Lab Testing • Development, Implementation and Testing using RT-HIL

Simulation







• Conclusions


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Real-Time Steady State Model Synthesis of

Active Distribution Networks


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Background • Currently, TSOs maintain reduced models of

portions of the distribution networks, however:

• The models covers limited portions of the distribution network due to the lack of network “observability” (measurement points) and computational burden associated with simulating large joint T&D models.

• The models are not updated frequently.

• The reduction methods, used by TSOs, often make assumptions that are no longer valid for active distribution networks.



Methodology and Application

V1abc<δ1

abc

PMU1

I1abc<φ1

abc

Any feeder

configuration with an

arbitrary combination

of load and DG

PMU2

V1a<δ1

a

I1a<φ1

a

I2a<φ2

a

V2a<δ2

aR a X

aR

a X a

R a

X a

E a<δ a

V0a<δ0

a

I0

a<φ

0a

The reduced steady state

equivalent model for

phase ‘a’

3

3

3 ... V3abc<δ3

abc

I3abc<φ3

abc

3

PMU3

.

.

.

PMU N

VTabc<δT

abc

PMU at

Transmission

Level

ITabc<φT

abc

Any feeder

configuration with an

arbitrary combination

of load and DG

3

3

3

V1abc<δ1

abc

I1abc<φ1

abc

3

PMU at

Distribution Level

VTa<δT

aIT

a<φTa

I1a<φ1

a

V1a<δ1

aR a X a

R a X

aV0

a<δ0a

E a<δ a

R a

X a

I0a<φ

0a

The reduced steady

state equivalent

model for phase ’a’

2R a 2X a

The reduced steady state equivalent

model for phase ’a’

E a<δ a

I2=0Open-circuited

VTa<δT

aIT

a<φTa

With N available PMUs With 1 available PMU

• Model parameters are obtained by writing KVL equations across the model branches and equate Vi’s and Ii’s to PMU measurements.

Event1

Event2

0 80 160 0 80 160

Event1

Event2

Event1

Event2

Event1

Event2

R (

p.u

.)

X (

p.u

.)

Ea (

p.u

.)

δa (

ra

d)

Estimated parameters of equivalent model

LabVIEW Application


Illustration Example (1/2) • Steady state model of a portion of the reference grid is

estimated during wind curtailment at different dispatch levels.

R

X

E

δ E<δ

R

X


Illustration Example (2/2) • True and reproduced current and voltage phasors are

compared using TVE.

• The mean TVE is less than 2.5%.

1.5 1.55 1.6 1.65 1.7 1.75 1.80

5

10

15

20

25

30

35

40

TVE for V2 (%)

Pro

bab

ilit

y d

istr

ibu

tio

n

TVE for Voltage Phasor at PMU2

empirical

generalized pareto

generalized extreme value

inversegaussian

birnbaumsaunders

1 1.5 2 2.5 3 3.5 4 4.50

0.5

1

1.5

2

2.5

3

3.5

4

TVE for I1 (%)

Pro

bab

ilit

y d

istr

ibu

tio

n

TVE for Current Phasor at PMU1

empirical

generalized pareto


inversegaussian

birnbaumsaunders

1.3 1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.70

5

10

15

20

25

30

35

40

TVE for I2 (%)

Pro

bab

ilit

y d

istr

ibu

tio

n

TVE for Current Phasor at PMU2

empirical


generalized pareto

inversegaussian

birnbaumsaunders

0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.180

10

20

30

40

50

60

70

80

90

TVE for V1 (%)

Pro

bab

ilit

y d

istr

ibu

tio

n

TVE for Voltage Phasor at PMU1

empirical

generalized pareto


beta

gamma

Variables with hat are reproduced ones.

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Dynamic Line Rating for Aerial Distribution Feeders



Background • Dynamic line rating (DLR) is a way to optimize

the ampacity of transmission and distribution lines by measuring the effects of weather and actual line current.

• The inputs needed for the method: • Weather data => provided by a close-by weather

station.

• Line loading => provided by PMU!

• Real-time sag => provided by a GPS-based measurement device.



LabVIEW Application


• Results are obtained by applying the method on data from a real feeder.

• Output shows accurate correlation with different inputs.

Sample Results

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Small Signal Dynamic Analysis of Active Distribution Networks



Background • For reliable operation of the power system,

oscillations are required to decay.

• Accurate and real-time estimates of active distribution networks oscillatory modes has become ever so important.

• Timely extraction of these modes and related parameters from network measurements has considerable potential for near real-time dynamic security assessment.

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Extracting and Exchanging Small Signal Stability Information

38

Data curation, fusion and extraction of steady state

component

• Ambient Data Analysis (for stochastic variations)

• Ringdown Data Analysis (for transients)

TSO

Calculation of stability indices

Data process and analysis



Centralized Architecture

Decentralized Architecture


Implementation Architectures

Centralized

Decentralized


PMU data collection

and transmission

Dynamic component extraction by

Kalman filter

Data pre-processing (detrending

and downsampling)

Ringdown

detected? Ambient analysis

(ARMA)

Ringdown

analysis (ERA) Modal parameters

Modal parameters

LabVIEW Application



Illustration Example (1/2) • 4 PMUs were deployed in the

reference grid to be used for mode estimation under two different

architectures

of centralized

and

decentralized


Illustration Example (2/2)

Centralized Decentralized

Inter-area oscillatory mode between HV section and rest of the grid

Forced local oscillatory mode detectable in decentralized architecture

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References and Resources of work presented and related to this presentation

• Github Repositories (Open Source Software Resources) • https://github.com/SmarTS-Lab/

• https://github.com/SmarTS-Lab-Parapluie (PMU applications and software tools)

• Under Review: • N. Singh, H. Hooshyar and L. Vanfretti, “Feeder Dynamic Rating Application for

Active Distribution Networks using Synchrophasors,” Sustainable Energy, Grids and Networks. (under third review)

• F. Mahmood, H. Hooshyar, and L. Vanfretti, “Sensitivity Analysis of a PMU-Based Steady State Model Synthesis Method,” IEEE PES GM 2017.

• R.S. Singh, H. Hooshyar, and L. Vanfretti, “Real-Time Testing of a Decentralized PMU Data-Based Power System Mode Estimator,” IEEE PES GM 2017.


https://github.com/SmarTS-Lab/








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References and Resources of work presented and related to this presentation

• Published/Accepted for Publication: • S.R. Firouzi, L. Vanfretti, A. Ruiz-Alvarez, H. Hooshyar and F. Mahmood, “Interpretation and Implementation of IEC 61850-90-5 Routed-

Sampled Value and Routed-GOOSE Protocols for IEEE C37.118.2 Compliant Wide-Area Synchrophasor Data Transfer,” Electric Power Systems Research, 2017.

• F. Mahmood, H. Hooshyar, J. Lavenius, P. Lund and L. Vanfretti, “Real-Time Reduced Steady State Model Synthesis of Active Distribution Networks using PMU Measurements,” IEEE Transactions on Power Delivery, 2017. http://dx.doi.org/10.1109/TPWRD.2016.2602302

• F. Mahmood, H. Hooshyar and L. Vanfretti, "Extracting Steady State Components from Synchrophasor Data Using Kalman Filters," Energies, vol. 9, 2016.

• H. Hooshyar, F. Mahmood, L. Vanfretti, and M. Baudette “Specification, Implementation and Hardware-in-the-Loop Real-Time Simulation of an Active Distribution Grid,” Sustainable Energy, Grids and Networks (SEGAN), Elsevier, vol. 3, Sept. 2015, pp. 36-51. Available in open-access: https://dx.doi.org/doi:10.1016/j.segan.2015.06.002

• H. Hooshyar, L. Vanfretti and C. Dufour, “Delay-Free Parallelization for Real-Time Simulation of a Large Active Distribution Grid Model,” IEEE IECON 2016, Firenze, October 2016.

• R.S. Singh, M. Baudette, H. Hooshyar, M.S. Almas, Stig Løvlund, L. Vanfretti, “‘In Silico’ Testing of a Decentralized PMU Data-Based Power Systems Mode Estimator,” IEEE PES General Meeting 2016, Boston, MA, USA.

• A. Bidadfar, H. Hooshyar, M. Monadi, L. Vanfretti, Decoupled Voltage Stability Assessment of Distribution Networks using Synchrophasors,” IEEE PES General Meeting 2016, Boston, MA, USA.

• S.R. Firouzi, L. Vanfretti, A. Ruiz-Alvarez, F. Mahmood, H. Hooshyar, I. Cairo, “An IEC 61850-90-5 Gateway for IEEE C37.118.2 Synchrophasor Data Transfer,” IEEE PES General Meeting 2016, Boston, MA, USA.

• R.S. Singh, H. Hooshyar and L. Vanfretti, “Laboratory Test Set-Up for the Assessment of PMU Time Synchronization Requirements,” IEEE PowerTech 2015, The Netherlands, 2015.

• R.S. Singh, H. Hooshyar and L. Vanfretti, “Assessment of Time Synchronization Requirements for Phasor Measurement Units,” IEEE PES PowerTech 2015, The Netherlands, 2015.

• H. Hooshyar, F. Mahmood, and L. Vanfretti, “HIL Simulation of a Distribution System Reference Model,” NORDAC 2014.

• H. Hooshyar and L. Vanfretti, “Specification and Implementation of a Reference Grid for Distribution Network Dynamics Studies,” IEEE PES General Meeting, 2014, National Harbor, MD (Washington, DC Metro Area).

• L. Vanfretti, V. H. Aarstrand, M. Shoaib Almas, V. Peric, and J. O. Gjerde, ”A software development toolkit for real-time synchrophasor applications”, 2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013; Grenoble, France, 16-20 June 2013.


http://dx.doi.org/10.1109/TPWRD.2016.2602302

https://dx.doi.org/doi:10.1016/j.segan.2015.06.002

ideal grid for all

Conclusions • The increase of intermittent renewable sources at the distribution network

bring technical challenges into DSO operations.

• Synchrophasor measurements have a great potential to support the technical operation of distribution networks

• Applications can play an important role of extracting key information about the operation of the grid

• TSOs-to-DSOs interaction in operations through PMU Apps. • DSOs can enhance the way they operate by having better knowledge of the

system’s performance in near real-time

• TSOs can gain visibility of the phenomena at lower voltage levels, and device actions

• Real-time automatic control and protection is the next big step • Existing architecture, automation and system level technology not up to the task

• Interoperability and Standardization:

• Too slow and maybe even useless without Open Source Software and Hardware platforms and building blocks

• Need to develop and support a truly open market of products and services


ideal grid for all

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Synchrophasor Applications