State Estimation 150611

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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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6

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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2-10

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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2-24

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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2-25


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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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

[email protected]

+91-9871581133

Date post:	03-Apr-2018
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State Estimation 150611

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