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State Estimation Techniques
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Simple basics What is SCADA?
Supervisory Control
Data Acquisition
Purpose of SCADA?
What else now needed
Controls
Look into future & able to control future events What is EMS?
Need for EMS?
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Simple basics
How to look into the future?
How to know present problems/state?
How & what actions to take?
Which are best actions?
Optimisation?
How can we control the events?
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Simple basics
What is State Estimation (SE)?
Why is it required?
How is it achieved?
Techniques?
Process?
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Need of the Modern LoadDispatch Center
A robust Energy Management Systemcapable of meeting the requirements of
changed scenarios of deregulated market
mechanisms.
The EMS system shall be capable of being
easily integrated with Market ManagementSystem.
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Requirement of EMS Functions.
Why do we need EMS functions? Help grid operators in decision making .
Gives scientific logic for any actions.
Gives warning for any emergency situation.
Power system can be analysed for different
operating conditions. To get a base case for further Analysis
.
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EMS functions objective
Power system monitoring
Power system control
Power system economics
Security assessment
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EMS Functions : Classification Based onFunction
1. State Estimation
2. Power Flow Analysis
3. Contingency Analysis
4. Security enhancement
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EMS Functions : Classification Based on
Time Domain
Pre Dispatch Functions
Load Forecasting/Inflowforecasting
Resource Scheduling And
Commitment
Network Outage Planning Real Time Operation
State Estimator (RTNET)
Real Time contingency analysis
(RTCA) Real Time Security Enhancement
(RTSENH)
Real Time Generation Control
(RTGEN)
Voltage Var Dispatch
Post Dispatch / off lineactivities
Dispatcher training
Simulator
Other features like
Historicar Data Recording,
Historical Information
Management,
Sequence Of Events,
Load Flow Studies (STNET)
.
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SE Problem Development
Whats A State? The complete solution of the power system is known
if all voltages and angles are identified at each bus.
These quantities are the state variables of the system.
Why Estimate?
Meters arent perfect.
Meters arent everywhere.
Very few phase measurements?
SE suppresses bad measurements and uses the
measurement set to the fullest extent.
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Few Analogies given by F. Schweppe
Life blood of control system :
clean pure data defining system state status (voltage, network
configuration)
Nourishment for this life blood:
from measurements gathered from around the system (data
acquisition)
State Estimator: like a digestive system
removes impurities from the measurements
converts them into a form which brain (man/computer) of
central control centre can use to make action decisions on
system economy, quality and security
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EMS Functions
Out of the all EMS functions State Estimator is the
first and most important function.
All other EMS functions will work only when the
State Estimator is running well.
State Estimator gives the base case for further
analysis.
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State Estimation State Estimation is the process of assigning a value to an unknown system
state variable based on measurements from that system according to some
criteria.
The process involves imperfect measurements that are redundant and the
process of estimating the system states is based on a statistical criterion thatestimates the true value of the state variables to minimize or maximize the
selected criterion.
Most Commonly used criterion for State Estimator in Power System is the
Weighted Least Square Criteria.
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State Estimation It originated in the aerospace industry where the basic problem have
involved the location of an aerospace vechicle (i.e. missile , airplane, or
space vechicle) and the estimation of its trajectory given redundant and
imperfect measurements of its position and velocity vector.
In many applications, these measurements are based on optical
observations and/or radar signals that may be contaminated with noise and
may contain system measurement errors.
The state estimators came to be of interest to power engineers in1960s.Since then , state estimators have been installed on a regular basis in a new
energy control centers and have proved quite useful.
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State Estimation In the Power System, The State Variables are the voltage Magnitudes and
Relative Phase Angles at the System Nodes.
The inputs to an estimator are imperfect power system measurements of
voltage magnitude and power, VAR, or ampere flow quantities.
The Estimator is designed to produce the best estimate of the systemvoltage and phase angles, recognizing that there are errors in the measured
quantities and that they may be redundant measurements.
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Base Case Definition
A Base Case Is The solution to the basic network problem
posed to find the voltages, flow, etc. of a
specific power system configuration with aspecified set of operating conditions.
The starting point for other applications dealing
with system disturbances and systemoptimization.
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Basics of state estimation
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Bus1Bus2
Bus3
60 MW
40 MW
65 MW
100 MW
Per unit Reactances
(100 MVA Base):
X12=0.2
X13=0.4
X23=0.25
M12
M13
M32
5 MWMeter Location
35 MW
Case1-Measurement with accurate meters)
Only two of thesemeter readings are
required to calculate
the bus phase angles
and all load andgeneration values
fully.
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Suppose we use M13 and M32 and further suppose thatM13 and M32 gives us perfect readings of the flows on their
respective transmission lines.
M13=5 MW=0.05pu M32 =40 MW=0.40pu
f13=1/x13*(1- 3 )=M13 = 0.05
f32=1/x32*(
3-
2)=M32 = 0.40Since 3=0 rad
1/0.4*(1- 0 )= 0.05
1/0.25*(0-
2) = 0.401 =0.02 rad
2 =-0.10 rad
Case-1
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Bus1Bus2
Bus3
62 MW
37 MW
65 MW
100 MW
Per unit Reactances
(100 MVA Base):
X12=0.2
X13=0.4
X23=0.25
M12
M13
M32
6 MW (7.875MW)Meter Location
35 MW
Case2-result of system flow.
Mismatch
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Again if we use only M13 and M32.
M13=6 MW=0.06pu M32 =37 MW=0.37pu
f13=1/x13*(1- 3 )=M13 = 0.06
f32=1/x32*(
3-
2)=M32 = 0.37Since 3=0 rad
1/0.4*(1- 0 )= 0.06
1/0.25*(0- 2) = 0.37
1 =0.024 rad
2 =-0.0925 rad
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Case-2:Again if we use only M12 and M32.
M12=62 MW=0.62pu M32 =37 MW=0.37pu
f12=1/x12*(1- 2 )=M12 = 0.62
f32=1/x32*(
3-
2)=M32 = 0.37Since 3=0 rad
1/0.2*(1- 2 )= 0.62
1/0.25*(0- 2) = 0.37
1 =0.0315 rad
2 =-0.0925 rad
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What we need ?A procedure that uses the information available
from all the three meters to produce the best
estimate of the actual angles, line flows, and bus
load and generation.
We have three meters providing us with a set of
redundant readings with which to estimate the
two states 1 and 2.. We say that the readings are redundant
since, as we saw earlier, only two readings are necessary tocalculate 1 and 2 the other reading is always extra.
However, the extra reading does carry useful information
and ought not to be discarded summarily.
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SE Problem Development (Cont.)
Mathematically Speaking...Z = [ h( x ) + e ]
where,Z = Measurement Vector
h = System Model relating state vector to the
measurement set
x = State Vector (voltage magnitudes andangles)
e = Error Vector associated with the
measurement set
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SE Problem Development (Cont.)
Linearizing
Classical Approach -> Weighted Least
Squares
Z = H x + e
(This looks like a load flow equation )
Minimize: J(x) = [z - h(x)]t
. W. [z - h(x)]where,
J = Weighted least squares matrix
W = Error covariance matrix
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Weighted least squares state
estimation. Assume that all the three meters have the
following characterstics.
Meter full scale value: 100 MW
Meter Accuracy: +/- 3 MW
This is interpreted to mean that the meters willgive a reading within +/- 3 MW of the true valuebeing measured for approximately 99 % of time.
Mathematically we say that the errors aredistributed according to a normal probabilitydensity function with a standard deviation ,,
I.e. +/- 3 MW corresponds to a metering standard
deviation of , =1 MW=0.01 pu.
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X est =[ [H]T[R-1][H] ]-1 X [H]T[R-1]Zmeas
[H]= an Nm by Ns matrix containing thecoefficients of the linear functions fi(x)
[R] = 12
2 2
.
.
Nm 2
[Z meas]= Z 1meas
Z 2meas
.
.
Z Nmmeas
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[H]=measurement function coefficient matrix.
To derive the [H] matrix , we need to write the
measurements as a function of the state variables1 and 2. These functions are written in per unit as M12 = f12 = 1/0.2 x(1 - 2) =5 1 - 52
M13
= f13
= 1/0.4 x(1
- 3
) =2.5 1
M32 = f32 = 1/0.25 x(3 - 2) =-4 2
[H]= 5 -52.5 0
0 -4
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[R]=measurement covariance matrix.
[R] =
M12
2
M132
M232
=0.0001
0.0001
0.0001
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SE Functionality
So Whats It Do? Identifies observability of the power system.
Minimize deviations of measured vs estimated
values.
Status and Parameter estimation.
Detect and identify bad telemetry.
Solve unobservable system subject to
observable solution.
Observe inequality constraints (option).
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SE Measurement Types
What Measurements Can Be Used? Bus voltage magnitudes.
Real, reactive and ampere injections.
Real, reactive and ampere branch flows. Bus voltage magnitude and angle differences.
Transformer tap/phase settings.
Sums of real and reactive power flows. Real and reactive zone interchanges.
Unpaired measurements ok
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State Estimation Process
Two Pass Algorithm First pass observable network.
Second pass total network (subject to first
pass solution).
High confidence to actual measurements.
Lower confidence to schedule values.
Option to terminate after first pass.
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Observability Analysis
Bus Observability A bus is observable if enough information is
available to determine its voltage magnitude
and angle. Observable area can be specified (Region of
Interest).
Bus or station basis
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Bad Data Suppression
Bad Data Detection Mulit-level process.
Bad data pockets identified.
Zoom in on bad data pocket for rigorous
topological analysis.
Status estimation in the event of topological
errors.
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Final Measurement Statuses
Used The measurement was found to be good andwas used in determining the final SE solution.
Not Used Not enough information was available touse this information in the SE solution.
Suppressed The measurement was initially used,but found to be inconsistent (or bad).
Smeared At some point in the solution process, themeasurement was removed. Later it was determined that
the measurement was smeared by another bad
measurement.
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Solution Algorithms
Objective Weighted Least Squares:
Choice of Givens Rotation or Hybrid
Solution Methods
Minimize: J(x) = .5 [Z - h(x)] t R -1 [Z - h(x)]
where,
J = Weighted least squares matrixR = Error covariance matrix
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Solution Algorithms (Cont.)
Givens Rotation (Orthogonalization) Least tendency for numerical ill-conditioning.
Uses orthogonal transformation methods to
minimize the classical least squares equation. Higher computational effort.
Stable and reliable.
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SE Problem Development (Cont.)
Hybrid Approach Mixture of Normal Equations and
Orthogonalization.
Orthogonalization uses a fast Givens rotationfor numerical robustness.
Normal Equations used for solution state
updates which minimizes storage requirements.
Stable, reliable and efficient.
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State Estimation...
Measurements and Estimates
SE Measurement Summary Display Standard Deviations Indicates the relative
confidence placed on an individual
measurement. Measurement Status Each measurement may
be determined as used, not used, or
suppressed. Meter Bias Accumulates residual to help
identify metering that is consistently poor. The
bias value should hover about zero.
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State Estimation...
Measurements and Estimates (Cont.)
Observable System Portions of the system that can be completely solved
based on real-time telemetry are called observable.
Observable buses and devices are not color-coded
(white).
Unobservable System
Portions of the network that cannot be solved
completely based on real-time telemetry are called
unobservable and are color-coded yellow.
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Penalty Factors
Real-Time Penalty Factors
Calculated on successful completion of RTNA.
Available for use by Generation Dispatch and Control.
Penalty Factor display.
Penalty Factor Grid
Historical smoothed factors.
Available for use by Generation Dispatch and Control
and Unit Commitment.
HISR Form interface.
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State Estimator (RTNET) INPUTS& OUTPUTS
Input
SCADA
Network component P,Q
Bus Voltage magnitude
Values
Tap Positions Data Quality Information
RTGEN
Unit MW base points and
MW limits
Unit Participation Factors
Unit Ramp Rates
Unit Control Status and
on/off line status
Scheduled AreaTransactions
Output
Bus Voltages And Angles
MW/MVAR Flows
Limit Violations
Generation And Load
Tap Position
Anomalous input Data
Loss Sensitivity
In addition to all these SE also
Detects & Identifies the Bad
Measurements
42
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Causes of Poor Estimate quality Topology/Model error in the vicinity of the problem Switching devices in wrong status, particularly non telemetered.
New construction
Bad equivalents
Branch parameters incorrect
Capacitors or reactor in wrong state.
Unsuitable pseudo measurements
Unrealistic Unit Limits
Unrealistic Load model
Incorrect target values for regulation schedule
Incorrect tap position
Should it be on AVR?
Should it be estimated?
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Contingency Analysis
A contingency is a defined set ofhypothetical equipment outages and / or
breaker operations
Also : node outage, substation outage Conditional contingencies
Contingency Analysis reports which
hypothetical contingencies would cause
component limit violations.
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Real Time Contingency Analysis
Based on predefined limits it gives a list of
contingencies in the base case.
This gives the consequences of predefined
Contingencies.
Contingencies can be grouped depending on
requirement.
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Requirement for Good CAresults:
A good Base Case based on the State
Estimator Output.
Defined all the possible credible
contingencies.
Correct limits for all power system
elements.
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Thank YouRajiv Porwal
Contact me on
+91-9871581133
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