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State Estimation Techniques

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Presentation on theme: "State Estimation Techniques"— Presentation transcript:

1 State Estimation Techniques

2 Simple basics What is SCADA? Purpose of SCADA? What else now needed
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?

3 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?

4 Simple basics What is State Estimation (SE)? Why is it required?
How is it achieved? Techniques? Process?

5 Need of the Modern Load Dispatch Center
A robust Energy Management System capable of meeting the requirements of changed scenarios of deregulated market mechanisms. The EMS system shall be capable of being easily integrated with Market Management System.

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 ….

7 EMS functions objective
Power system monitoring Power system control Power system economics Security assessment

8 EMS Functions : Classification Based on Function
State Estimation Power Flow Analysis Contingency Analysis Security enhancement

9 EMS Functions : Classification Based on Time Domain
Pre Dispatch Functions Load Forecasting/Inflow forecasting 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 line activities Dispatcher training Simulator Other features like Historicar Data Recording, Historical Information Management, Sequence Of Events, Load Flow Studies ( STNET) ….

10 SE Problem Development
What’s 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 aren’t perfect. Meters aren’t everywhere. Very few phase measurements? SE suppresses bad measurements and uses the measurement set to the fullest extent.

11 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

12 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.

13 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 that estimates 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.

14 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.

15 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 system voltage and phase angles, recognizing that there are errors in the measured quantities and that they may be redundant measurements.

16 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 a specified set of operating conditions. The starting point for other applications dealing with system disturbances and system optimization.

17 Basics of state estimation

18 Case1-Measurement with accurate meters)
100 MW M12 60 MW 65 MW Only two of these meter readings are required to calculate the bus phase angles and all load and generation values fully. M13 Bus2 Bus1 40 MW Per unit Reactances (100 MVA Base): X12=0.2 X13=0.4 X23=0.25 The only information we have about this system is provided by three MW power flow meters located as shown. M32 35 MW Bus3 Meter Location 5 MW

19 Case-1 Suppose we use M13 and M32 and further suppose that M13 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 = 0.40 Since 3=0 rad 1/0.4*(1- 0 )= 0.05 1/0.25*(0- 2) = 0.40 1 =0.02 rad 2 =-0.10 rad

20 Case2-result of system flow.
100 MW M12 62 MW 65 MW M13 Bus2 Bus1 37 MW Per unit Reactances (100 MVA Base): X12=0.2 X13=0.4 X23=0.25 The only information we have about this system is provided by three MW power flow meters located as shown. M32 Mismatch 35 MW 6 MW (7.875MW) Bus3 Meter Location

21 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 = 0.37 Since 3=0 rad 1/0.4*(1- 0 )= 0.06 1/0.25*(0- 2) = 0.37 1 =0.024 rad 2 = rad

22 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 = 0.37 Since 3=0 rad 1/0.2*(1- 2 )= 0.62 1/0.25*(0- 2) = 0.37 1 = rad 2 = rad

23 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 to calculate 1 and 2 the other reading is always “extra”. However, the “extra” reading does carry useful information and ought not to be discarded summarily.

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 and angles) e = Error Vector associated with the

25 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

26 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 will give a reading within +/- 3 MW of the true value being measured for approximately 99 % of time. Mathematically we say that the errors are distributed according to a normal probability density function with a standard deviation ,, I.e. +/- 3 MW corresponds to a metering standard deviation of ,  =1 MW=0.01 pu.

27 X est =[ [H]T[R-1][H] ]-1 X [H]T[R-1]Zmeas
[H]= an Nm by Ns matrix containing the coefficients of the linear functions fi(x) [R] = 1 2 2 2 . Nm 2 [Z meas]= Z 1meas Z 2meas Z Nmmeas

28 [H]=measurement function coefficient matrix.
To derive the [H] matrix , we need to write the measurements as a function of the state variables 1 and 2. These functions are written in per unit as M12 = f12 = 1/0.2 x(1 - 2) =5 1 - 52 M13= f13 = 1/0.4 x(1 - 3) =2.5 1 M32 = f32 = 1/0.25 x(3 - 2) =-4 2 [H]= 2.5 0 0 -4

29 [R]=measurement covariance matrix.
=0.0001 0.0001

30 SE Functionality So What’s 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).

31 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

32 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.

33 Observability Analysis
Bus Observability A bus is observable if enough information is available to determine it’s voltage magnitude and angle. Observable area can be specified (“Region of Interest”). Bus or station basis

34 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.

35 Final Measurement Statuses
Used… The measurement was found to be “good” and was used in determining the final SE solution. Not Used… Not enough information was available to use 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, the measurement was removed. Later it was determined that the measurement was “smeared” by another bad measurement.

36 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 matrix R = Error covariance matrix

37 Solution Algorithms (Cont.)
Given’s 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.

38 SE Problem Development (Cont.)
Hybrid Approach Mixture of Normal Equations and Orthogonalization. Orthogonalization uses a fast Given’s rotation for numerical robustness. Normal Equations used for solution state updates which minimizes storage requirements. Stable, reliable and efficient.

39 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.

40 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.

41 Penalty Factors Real-Time Penalty Factors Penalty Factor Grid
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.

42 State Estimator (RTNET) INPUTS & OUTPUTS
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 Area Transactions 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

43 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?

44 Contingency Analysis A contingency is a defined set of hypothetical equipment outages and / or breaker operations Also : node outage, substation outage Conditional contingencies Contingency Analysis reports which hypothetical contingencies would cause component limit violations.

45 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.

46 Requirement for Good CA results:
A good Base Case based on the State Estimator Output. Defined all the possible credible contingencies. Correct limits for all power system elements.

47 Thank You Rajiv Porwal Contact me on

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