parameters estimation of electric power systems
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Yasser WEHBE
Dissertation Proposal
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Master of Science in Electrical Engineering - University ofSouth Florida Tam a FL
Master of Aeronautical Science - Embry-Riddle, FL Diploma in Electrical and Electronics Engineering
Lebanese University, Lebanon Research Assistant - University of South Florida - Tampa,
FL: 2010 2011
Power systems dynamics especially on the use of PMUs in studying Pron anal sis
RTDMS Eastern Interconnection real-time PMU data
Research Interests Power systems dynamics and modeling
System identification Numeric techniques
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Y. Wehbe and L. Fan, "Estimation of a Shunted",
appear in the Proceedings of the IEEE PES GeneralMeeting 2011" ". . , ,
technical report submitted to Midwest ISO, 2011
Y. Wehbe and L. Fan, "Estimating Synchronous",submitted in April 2011 to43rd North AmericanPower Symposium, 2011
". . ,Area Equivalent Machine Parameters with PMUMeasurements", work in progress
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Very interesting, mixes several knowledge
areas We need it!
ac ou
System integrity and reliability
S stem economics
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Monitor the grid in real time (WAMS)
Dynamic state estimation Is the system under stress
What is the current capacity of the system
Validate simulation models ev se correct ve measures amp ng
Improve protective measures (adaptive
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Phasor: sinusoidal signal with amplitude,
frequency and angle )2sin(2)( fVtv rms
PMU: measurement device with precise time
)2sin(2)( fIti rms
),( iirmsV ),( iirmsI
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Time AMOSCLDN(kV) AMOSCLDN(Degree)
02/20/201111:58:15:000AM
[EST] 765.23 12.38
02/20/201111:58:15:033AM
[EST] 765.27 12.63
02/20/201111:58:15:067AM
[EST] 765.24 12.64
: : :
[EST] 765.26 12.38
02/20/201111:58:15:133AM
[EST] 765.29 12.63
02/20/201111:58:15:167
AM
[EST] 765.33 12.63
02/20/201111:58:15:200AM
[EST] Null Null
02/20/201111:58:15:233AM
[EST] 765.36 12.64
02/20/201111:58:15:267AM
[EST] 765.37 12.63
02/20/201111:58:15:300AM
[EST] Null Null
02/20/201111:58:15:333AM
[EST] 765.48 12.65
[EST] 765.49 12.63
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Time AMOSCLDN(kV) AMOSCLDN(Degree)
02/20/2011
11:58:15:000
AM[EST] 765.23 12.38
02/20/2011
11:58:15:033
AM[EST] 765.27 12.63
02/20/2011
11:58:15:067
AM[EST] 765.24 12.64
02/20/2011
11:58:15:100
AM[EST] 765.26 12.38
02/20/2011
11:58:15:133
AM[EST] 765.29 12.63
02/20/2011
11:58:15:167
AM[EST] 765.33 12.63
02/20/2011
11:58:15:200
AM[EST] Null Null
02/20/2011
11:58:15:233
AM[EST] 765.36 12.64
02/20/2011
11:58:15:267
AM
[EST] 765.37
12.6302/20/2011
11:58:15:300
AM[EST] Null Null
02/20/2011
11:58:15:333
AM[EST] 765.48 12.65
11:58:15:367
AM[EST] 765.49 12.63
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Wide Area Measurement Systems (WAMS)
Dynamic State Estimation Model validation
System stress
System Capacity rotect on
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Wide Area Measurement Systems (WAMS) Traditional: 1/5s, estimation every couple of
minutes
,
Improve reliability and economics
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Two areas parameters estimation (Chow):
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Sim lest Model
Algebraic equationfor the voltagesource
erent a sw ngequation
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Two areas shunted transmission path(Wehbe):
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Inertia Calibration (Kalsi): Extend Kalman Filter
Calibrate the inertia H and damping factor D
Extended Kalman Filter
Needs terminal measurements (like PMU) and othermodel parameters (like the transient reactance).
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Machine Parameter Estimation Wehbe
Estimate Machine Parameters : Inertia Damping factor Mechanical Power Transient Reactance Stator Resistance Electromagnetic Force Rotor Angle No noise Model
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Flux Deca Machine Rotor An le Tri ath
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Flux Deca Machine Rotor An le Tri ath :
Estimates Rotor Angle +Noise and Process Noise
ee s e vo tage an s
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Estimation of a Shunted Radial Transfer Pathynam cs s ng s
Very fast Networ topo ogy
dependent
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Estimating Synchronous Machine Parameters witheasurements
Very fast Independent from Network
Works on classical model Various parameters and states:
Inertia Damping factor Mechanical Power Transient Reactance
Stator Resistance Electromagnetic Force Rotor Angle o no se o e
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Develo time inde endent e uations:
Estimate transient impe ance
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Find the rotor an le and Electroma netic force
Fin t e mec anica parameters: inertai amping actorand mechanical power using finite differences
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Real Time D namics Monitorin S stems Data
Analysis: Apply previous method on real world data
Problems: the system is not ideal
Least square fitting
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Im rove on revious research b estimatin
dynamic states and parameters in thepresence of noise
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1. Study the nature and impact of measurement noise:
2. Develop state and parameter estimation for the classical
model and H taking measurement noise intoconsideration:
(b) Study the implementation of non linear digital filters: i-
Unscented Kalman filter, ii- Extend Kalman filter, and iii-Divi e- y-Di erence i ter
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3. Develop parameter (H) estimation method for realcoherent areas based on the real system of the RTDMS:(a) Estimate the system model uncertainties: although
coherent group of machines are modeled as singleclassical machine, yet this modeling is not prefect in real
to encounter for such system modeling uncertainties.(b) Develop any necessary method to complement the
method described in item 2 contaminated by the processnoise o item 3a(c) Verify results with inter-area oscillation frequency.
4. Develop state and parameters estimation techniques forthe ux deca
model