parameters estimation of electric power systems

8/3/2019 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

parameters estimation of electric power systems

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