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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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 2
Outline
• Why FR DR
• Requirements/data processing
• Historian for Disturbance Play Back
• Conclusions
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 3
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 4
FR DR: How
• For Disturbance Recording and playback:
• Recording system requirements• Storage schemes (what to store
– data processing - model)• Playback and system
synthesizing
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 5
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 6
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 7
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 8
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)
ex̂
eee zyx ˆ,ˆ,ˆ
z
mz e+
-
Pseudo-measurements
-
Bad Data Detection
NO
YES
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 9
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 10
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 11
Distributed Dynamic State Estimation Implementation 1
Phase Conductor
Po
tent
ial
Tra
nsf
orm
er
CurrentTransformer
PMUVendor A
Burden
Inst
rum
en
tatio
nC
abl
es
v(t)
v1(t) v2(t)
Burdeni2(t)i1(t)
i(t)
Attenuator
Attenuator Anti-AliasingFilters
RelayVendor C
PMUVendor C
Measurement Layer
Super-Calibrator
Dat
aP
roce
ssi
ng
IED Vendor D
LA
NL
AN
FireWall
Enc
odin
g/D
eco
din
gC
ryp
tog
raph
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 12
Distributed Dynamic State Estimation: Implementation 2
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 13
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 kVA (V)VB (V)VC (V)
-3.181 k
-1.929 k
-677.0
574.8
1.827 k
3.079 kIA (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.
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 14
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 15
• 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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 16
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 17
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.
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 18
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.
Substation Storage SchemeFull Model + MODEL CHANGES + Data
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 19
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
Substation Storage SchemeFull Model + MODEL CHANGES + Data
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
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 20
Storage of state data: at each occurrence of the state estimator, the estimated states are stored in COMTRADE-like format.
State Data File – One for Each Day
Substation Storage SchemeFull Model + MODEL CHANGES + Data
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>. . .. . .
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 21
Storage of state data: at each occurrence of the state estimator, the estimated states are stored in COMTRADE-like format.
Triggered Event Files – One for Each Event
Substation Storage SchemeFull Model + MODEL CHANGES + Data
File Naming Standard:
CompanyName_SubstationName_SOC.cfgCompanyName_SubstationName_SOC.dat
File Content:
Standard COMTRADE Waveform File Format
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 22
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.
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 23
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.
Georgia Tech Fault and Disturbance Analysis ConferenceAtlanta, Georgia, May 3-4, 2010 24
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.