sakis meliopoulos georgia power distinguished professor

Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 1

Sakis MeliopoulosGeorgia Power Distinguished ProfessorSchool of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlanta, Georgia 30332

From Fault Recording to Disturbance Recording


Outline• Why FR DR

• Requirements/data processing

• Historian for Disturbance Play Back

• Conclusions


Why FR DR• Following the 2003 blackout,

numerous engineers worked for months to align and synthesize fault recorded data for the purpose of re-creating the disturbance and the evolution of the blackout

• There must be a BETTER WAY


FR DR: How• For Disturbance Recording and

playback:• Recording system requirements• Storage schemes (what to store

– data processing - model)• Playback and system

synthesizing


The SuperCalibrator is conceptually very simple:• Utilizes all available data (Relays, DFRs, PMUs, Meters, etc.).

• Utilizes a detailed substation model (three-phase, breaker-oriented model, instrumentation channel inclusive and data acquisition model inclusive).

• At least one GPS synchronized device (PMU, Relay with PMU, etc.) Results on UTC time enabling a truly decentralized State Estimator.

• Extracts the Real Time Model of the System form all available measurements mentioned above.

What to StoreThe SuperCalibrator Concept as a Data Compressor

Recently this approach has been extended to dynamic state estimation with build in fault locating: PMU data of phasors, frequency and rate of frequency change are used to provide the dynamic state of the system in a reliable and robust way

Fact: A plethora of data is available at the substation level


Distributed Dynamic State Estimation Implementation

System is Represented with a Set of Differential Equations (DE)The Dynamic State Estimator Fits the Streaming Data to the Dynamic Model (DE) of the System


SuperCalibrator Measurement SetConversion of Non-Synchronized Measurements into

Phasors

jmeassync eAA ~~

α is a synchronizing unknown variable

cos(α) and sin(α) are unknown variables in the state estimation algorithm.

There is one α variable for each non-synchronized relay


8

Dynamic State Estimation – Block diagram

• Dynamic State Estimation Problem is Converted to Static by Integration

• Least Squares Solution

• Bad Data Detection

• Bad Data Identification and Removal

Power System Dynamic Model

x,y,z

Measurements

Dynamic State

Estimator

Application(Stability

Monitoring)

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eee zyx ˆ,ˆ,ˆ

z

mz e+

-

Pseudo-measurements

-Bad Data Detection

NO

YES


Dynamic State Estimation: Numerical Experiments

• Test System: 3 generating substations and an infinite bus connected through overhead transmission lines

• Substation 1: Substation of interest where DSE is performed• Simulated 3 phase fault near Substation 3• DSE uses PMU and other relay measurements in the first substation • DSE algorithm estimates local and neighboring substation states


Numerical Experiments: Post Fault DSE Performance

Simulated and Estimated Voltage Magnitude at

Substation 3 (neighboring substation) for post fault condition

Simulated and Estimated Voltage Phase angle at

Substation 3 (neighboring substation) for post fault condition


Distributed Dynamic State Estimation Implementation 1

Phase Conductor

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PMUVendor A

Burden

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Cab

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v(t)

v1(t) v2(t)

Burdeni2(t)i1(t)

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Attenuator

Attenuator Anti-AliasingFilters

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PMUVendor C

Measurement Layer

Super-Calibrator

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yData/Measurements from all PMUs, Relays, IEDs, Meters, FDRs, etc are collected via a Local Area Network in a data concentrator.

The data is used in a dynamic state estimator which provides the validated and high fidelity dynamic model of the system.

Bad data detection and rejection is achieved because of high level of redundant measurements at this level.

Physical Arrangement

Data Flow


Distributed Dynamic State Estimation: Implementation 2


Program XfmHms - Page 1 of 1

c:\agc-projects\virginislands_ii\viwapa_events\february20_2008\d-303 disturb - Feb 20, 2008, 12:18:11.980001 - 2000.0 samples/sec - 3003 Samples

12.48 12.53 12.58

-28.00 k

-16.87 k

-5.735 k

5.399 k

16.53 k

27.67 k VA (V)VB (V)VC (V)

-3.181 k

-1.929 k

-677.0

574.8

1.827 k

3.079 k IA (A)IB (A)IC (A)

54.81

56.61

58.41

60.21

62.01

63.81 Frequency (Hertz)

The USVI WAPA System Provides an Excellent Testbed for the Distributed

Dynamic State EstimatorThe USVI WAPA system is a small 270 MW, Five Substations, 35 kV/13 kV System. 17 relay/PMUs.

Faults create large swings of the generators as manifested by the frequency oscillations in the Feb 20, 2008 event.

In addition events are more frequent in the USVI system than mainland systems.


The Dynamic State Estimator Operates at 10 times per secondWhat happens when a fault occurs?

Introduce the Fault Location (F) as another State to be Estimated

Distributed Dynamic State Estimation During a Fault


• System FULL MODEL stored once a day in WinIGS format – time of day can be arbitrarily selected, for example at 2 am. (example storage follows)

• Report system changes by exception – UTC time (example storage follows)

• Storage of state data: at each occurrence of the state estimator, the estimated states are stored in COMTRADE-like format. (example storage follows)

Historian for Disturbance Play-BackSubstation Storage SchemeFull Model + Model Changes + Data


System FULL MODEL stored once a day in WinIGS format.Time of day can be arbitrarily selected, for example at 2 am.Example storage:

Substation Storage SchemeFULL MODEL + Model Changes + Data

MODEL 3DEV_TITLE Long Bay SubstationNUMERIC_ID 77NET_LAYER 3GEO_COORDINATES 18.339260000 -64.920927000COORDINATES -137 2 -144 -1 -137 4 -138 -1 -145 0 -145 7 -145 4 -141 6COORDINATES -141 -2 -142 2INTERFACES FDR-9B 3-0A0B2 FDR-8B FDR10B FDR-YH1 3-0B0D 3-0A0B1 FDR-7BINTERFACES FDR-YH2PARAMETERS LONGBAY VIWAPA VIWAPAEND_MODEL

MODEL 123DEV_TITLE Feeder #11, Long Bay to East End Substation - Section 1NUMERIC_ID 246COORDINATES -145 7 -145 10 -141 13 -132 13 -126 10 -120 6 -114 4 -109 3COORDINATES -107 1 -105 -2CIRCUITS 1INTERFACES 3-0B0D_N 3-0B0D_A 3-0B0D_N 3-0B0D_B 3-0B0D_N 3-0B0D_C 3-0B0D_N UG350_NINTERFACES UG350_A UG350_N UG350_B UG350_N UG350_C UG350_NPARAMETERS 5 7 14.40 3868.0 0.0 0.0 0.0 CABLEPARAMETERS VI34KV750KCM-CU-TS -0.10802 -3.09671 CKT1 CABLE VI34KV750KCM-CU-TS -0.00119 -2.92351PARAMETERS CKT1 CABLE VI34KV750KCM-CU-TS 0.11108 -3.09234 CKT1 CABLE CONDUIT8PARAMETERS -0.00656 -2.93099 CKT1 COPPER 4/0 0.00667 -3.18108 CKT1PARAMETERS 1 CKT1 5499.0 25.0000 34.5000END_MODEL

MODEL 123………………

Substation Model

TransmissionLine Model


Report system changes by exception – UTC time

Substation Storage SchemeFull Model + MODEL CHANGES + Data

MODEL_CHANGE TIME 1267771497 450123 TYPE XFMR_TAP DEVICE_ID 1265 VALUE R12END_MODEL_CHANGE

MODEL_CHANGE TIME 1267771791 609355 TYPE BREAKER_OPERATION DEVICE_ID 3409 VALUE CLOSEEND_MODEL_CHANGE

. . .

. . .

. . .

SOC + Fractional SecondMarch 05, 01:44:57.450123

File Format – Each line begins with a keywordoptionally followed by one or more arguments.


Storage of state data: at each occurrence of the state estimator, the estimated states are stored in COMTRADE-like format. The following File Types Are Used:

Configuration Files: Description of State Names Types and Locations

State Data Files: State Values plus Model Change Information

Triggered Event Files: Waveform data recorded for each triggeringevent in COMTRADE format.



Storage of state data: at each occurrence of the state estimator, the estimated states are stored in COMTRADE-like format.

Configuration File – One for Each Day


File Naming Standard: CompanyName_SubstationName_SOC.scf

File Content:<Title or Brief Description><SOC> <uSec><Number of States><State Name>, <State Type>, <Bus Name>, <Phase>, <Power Device ID><State Name>, <State Type>, <Bus Name>, <Phase>, <Power Device ID>. . . . . .

Where:

• SOC: is the Second of Century Time Code defined as the number of seconds elapsed since midnight of January 1, 1970 (in UTC time)

• uSec is a fractional second value in microseconds.

• Above structure repeated each time the set of states changes



State Data File – One for Each Day


File Naming Standard: CompanyName_SubstationName_SOC.sdf

File Content:

STATE_VECTOR <SOC> <uSec> <State Value> <State Value> <State Value>. . . STATE_VECTOR <SOC> <uSec> <State Value> <State Value> <State Value>. . . . . .. . .STATE_VECTOR <SOC> <uSec> <State Value> <State Value> <State Value>. . .MODEL_CHANGE TIME 1267771791 609355 TYPE BREAKER_OPERATION DEVICE_ID 3409 VALUE CLOSEEND_MODEL_CHANGESTATE_VECTOR <SOC> <uSec> <State Value> <State Value> <State Value>. . .STATE_VECTOR <SOC> <uSec> <State Value> <State Value> <State Value>. . .. . .



Triggered Event Files – One for Each Event


File Naming Standard:

CompanyName_SubstationName_SOC.cfgCompanyName_SubstationName_SOC.dat

File Content:

Standard COMTRADE Waveform File Format


Re-Construction of System State

• System Operation “Play Back” over a user specified time interval (t1 to t2)

• Reconstructed state is presented via graphical visualization Techniques, ( 3-D rendering, animation etc) with multiple user options.


Wide Area Monitoring and Disturbance Play-Back

The SuperCalibrator at each substation stores the streaming data with (a) time tags, (b) network status, and (c) substation real time model at the time.

This data can be “played back” for any user specified past time interval. Various visualizations allow the user to observe specific performance parameters of the system. Examples are: (a) voltage profile evolution, (b) transient swings of the system, (c) electric current flow, etc.


Conclusions• The Dynamic State Estimator fits PMU data to the

Dynamic Model of the System: Enables a powerful method to study system dynamics and predict performance.

• Fault Location Estimation has been integrated into the Dynamic State Estimator.

• Substation storage scheme (historian) that enables automated Disturbance Play Back. The implemented historian of (full model) + (model changes) + (data) has been presented. Comments and suggestions are welcome.

• Need for standards for disturbance+model storage and playback that include coincident system models.

sakis meliopoulos georgia power distinguished professor

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