CPRI

Phasor Measurement Units - Applications in Power Systems and Renewable Energy Systems

1

Dr. Amit Jain

Joint Director Smart Power & Energy System

Central Power Research Institute amitjain@cpri.in

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Energy Management System - EMS

EMS capabilities have evolved over the past five decades

EMS manage the flow of electricity in the grid

Operate the electric grid within safe limits

Operate the system reliably - Prevent Blackouts

Keep the Lights On !

Automatically adjust generation to follow instantaneous customer load changes (Electricity Cannot be Stored)

Identify potential risks and take preventive action

Expedite restoration of customers after an emergency

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EMS Applications

Supervisory Control and Data Acquisition (SCADA)

- Monitor physical system conditions in real time (2-4 sec)

- Perform supervisory controls

- Exchange data with external functions

Transmission Grid Management

- State Estimation (SE) for real-time transmission system

- Network Security Analysis: real time contingency analysis (CA) for N-1 system security

- System Optimization: remedial actions, volt/var control

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Current New Influences

Types of Generation and Customers

Renewable Generation and uncertainty in Wind, Solar generation etc.

Central markets (ISOs, RTOs, etc)

Market Participants (Gencos, retail, traders)

Network & Operations planning

Distribution management

Mandates from Regulators

RTOs (very large networks, UI, robustness)

Transmission planning & Congestion management

Technologies & Tools

Software advances: Artificial Intelligence, optimization engines, visualization

engines, integrated development/UI environments, Desktop applications

Economic communication and standardized device protocols

Web-enabled IT systems

New types of synchronized, fast measurements 4

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SCADA Architecture

Other control sites

Operators Reports

SCADA

Communication network

Supervisory C`ontrol Data Acquisition

RTU IED

IED

IED RTU

RTU PLC

Distributed

SCADA

RTU IED

IED

PLC RTU

RTU RTU

Distributed

SCADA

RTU

IED RTU

RTU

PLC

Front-end Power System Analysis

Decision Making

PMU

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Motivation/Need for Synchrophasor Technology

A wish list for system operators to empower them:

Visualisation of dynamic behaviour

Stability aspects

Operate the system at its limits

State determination

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Phasor Representation of the Sinusoid

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Inputs and type of Data

Voltage inputs from Bus CVT/PT

Line currents from line CT of selected feeder

GPS clock input

Synchrophasor data available at the control centre

Voltage & Current phasor (Positive, negative & zero sequence)

Frequency & Rate of change of frequency

Power flow (MW and MVAr)

Angular difference

Inputs to PMU

Time synchronized

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Synchrophasor Applications

Situational Awareness

Decision Support

Real Time Analysis (SE/CA)

Oscillation monitoring

Planning

Automated Control

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The lack of wide-area visibility prevented early

identification of the August 14, 2003 Northeast blackout

The U.S.-Canada investigation report into the blackout

hypothesized that if a phasor system had been in

operation at that time, the blackout preconditions — in

particular, the growing voltage problems in Ohio —

could have been identified and understood earlier in the

day

In the last few minutes before the cascade, there was a

significant divergence in phase angle between Cleveland

and Michigan

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Diverging phase angles on the afternoon of August 14, 2003

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Cleveland Separation –Aug 14 , 2003

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Wind Generation on 03-06-10

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PMUs collect data at a much higher sampling rate than SCADA, the

granularity of the data can reveal new information about dynamic

stability events on the grid

This is evident when comparing the phasor data to SCADA data

collected for the same event on February 7, 2010

This offers an example of the information that SCADA misses because

its relatively slow scan times cannot capture the dynamic response of

the system.

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PMU Data v. 4-second SCADA data for February 7, 2010 event

This plot shows 4 second scan rate SCADA frequency data for several

sites in a small geographic area. Some small fluctuations in system

frequency are visible but since only two units recorded a change, the

signals appear to be more noise than a measurement of anything of

note.

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PMU data from several sites for the same event. Not only did the

PMUs reveal system dynamics that the SCADA data missed, it

captured more accurate information about the event

The observed frequency excursion captured by the PMUs shown was

much larger than what the SCADA data indicated (59.91 HZ

minimum versus 60.00 HZ). The PMUs also captured system

oscillations that continued for about 7 seconds after the event

PMU data for February 7, 2010 event

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The voltage and angle at the wind turbine generator

transients in wpp has very small window, conventional SCADA is not able to capture them. But PMU has the ability to capture these small transients too.

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PSS tuning at Karcham Wangtoo Hydro Electric Power Plant

Screenshot of PMU data display at NRLDC on 23-August 2012 at 19:02 hrs

R phase to Neutral voltage of Wangtoo 400 kV Bus

Source: Synchrophasors - Initiative in India 2013

PMUs high resolution data is used in online tuning of hydro power plant. This is extremely helpful in the situations when dam is overflowing and it is difficult to shut down the hydro station.

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Wide Area Monitoring

A Wide Area Monitoring System acquires

GPS-synchronized current, voltage and

frequency phasor measurements, which are

measured by Phasor Measurement Units

(PMUs), from selected locations across the

power system.

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Wide Area Monitoring (cont…)

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Potential PMU Applications for Wide Area Monitoring and

Control

Wide-Area Visualization and Monitoring

Angle and Frequency Monitoring

Inter-area Oscillation Detection & Analysis

Proximity to Voltage Collapse

State Estimation

Fast Frequency Regulation

Transmission Fault Location Estimation

Dynamic Model Validation.

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Visualization Applications

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Frequency and rate-of-change of frequency

Positive, negative, and zero sequence plots of system voltage

Damping constant calculations

Power flow / change in power flow / general change detection

Oscillation Identification / frequency calculation

Historical Trends

Event Signature Analysis

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PMU placement

It is not at all necessary to place PMUs at all busses in the

power system to make it observable.

When a PMU is placed at a bus, then it's neighbouring

busses also become observable.

In general, a system can be made observable by placement

of PMUs on approximately 15% to 25% of the busses in the

system

Optimal PMU placement problem i.e., minimum PMU

placement problem for system observability, can be

formulated as an Integer Linear Programming (ILP)

problem.

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Present Indian Grid Plan

National Load Dispatch Center (NLDC) in Delhi

Regional Load Dispatch Center (RLDC) North

Regional Load Dispatch Center (RLDC) W, S, E, NE

State Load Dispatch Center (SLDC)

Sub Load Dispatch Center (SubLDC)

Substation

State Load Dispatch Center (SLDC)

Substation

Sub Load Dispatch Center (SubLDC)

User User

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System Architecture

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Modeling of Power System Components

Generators

Transformers

Transmission Lines

Shunts (Reactors and Capacitors)

HVDC etc

LoadS

Appropriate Simulation/Analysis Software

Power System Modeling

Most critical for Appropriate results

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Real-Time Market

Security Constrained Economic Dispatch using:

Load Forecast or Current Demand

Telemetered generator output, dispatch limits or ramp rates

Energy offers

Transmission Constraints

Good and reliable State Estimation crucial

Run every 5 minutes and also on demand

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Measurements from SCADA

- Line power flows, bus voltage, line current magnitudes, generator outputs, loads

- Circuit breaker, switch status information

- Transformer tap positions, and switchable capacitor bank values

- Used for various EMS applications at the control center like state estimation,

contingency analysis, load forecasting, AGC and optimal power flow

Limitations

- Unreliable due to errors in measurements, telemetry failures, communication noise

- Not the direct operating state of the system ( V, θ )

- Every point cannot be telemetered

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The main objective of the real time monitoring is to maintain the secure operation of the system as the operating conditions vary.

The basis for the security analysis is the knowledge of the system state or the state vector under steady state conditions.

State Estimator consists of:

Topology processor

Observability Analysis

State Estimation Solution

Bad data processing

Parameter and Structural Error Processing

The output of the state estimation is in turn used for other functions in the control center such as load forecasting, optimal load dispatch, security analysis, generation scheduling etc

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State Estimation (SE)

The State Estimation (SE) is used for assessing (Estimating) the

Electric network state.

It shall assess loads of all network nodes, and, consequently,

assessment of all other state variables (voltage and current phasors

of all buses, sections and transformers, active and reactive power

losses in all sections and transformers, etc.) in the Electric network.

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PMUs in Static State Estimation (SSE)

The accuracy of the measurements used is an important parameter in determining the accuracy of the estimation process.

The high accuracy of the PMU measurements and their ability to measure voltage angular measurements, allow them to have a special status in the state estimation techniques.

As the PMUs may not be installed at all locations, the measurements available at the control center will have a mixture of both normal and PMU measurements.

The state estimation function has to carefully use both the measurement sets and estimate the state of the power systems accurately.

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PMUs in the system and estimation error

PMU location

Minimum Average Voltage

magnitude Estimate

Minimum Average Angular

Estimate

No PMU 2.9821% 8.7062%

2 0.3765% 1.5356%

7 0.3301% 1.6898%

15 0.4608% 0.7545%

22 0.2934% 0.8028%

28 0.3360% 1.9609%

PMU location

Minimum Average Voltage

magnitude Estimate

Minimum Average Angular Estimate

No PMU 2.9821% 8.7062%

2 and 7 0.3280% 1.6981%

7 and 15 0.3874% 0.8469%

22 and 28 0.2587% 0.8361%

2 and 28 0.3240% 1.6861%

2, 7 and 15 0.3931% 0.9483%

15, 22 and 28 0.2237% 0.8039%

2, 7, 15 and 22 0.2562% 0.8831%

2, 7, 15, 22 and 28 0.2173% 0.7958%

Single PMU case Multiple PMU case

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0.0000%0.5000%1.0000%1.5000%2.0000%2.5000%3.0000%3.5000%

No P

MU 2 7

15

22

28

2 a

nd 7

7 a

nd 1

5

22 a

nd 2

8

2 a

nd 2

8

2,

7,

and 1

5

15,

22 a

nd 2

8

2,

7,

15 a

nd 2

2

2,

7,

15,

22 a

nd 2

8

PMU Location

Vari

ait

on

of

Vo

ltag

e m

ag

nit

ud

e

sti

mate

Err

or

(%)

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

No

PM

U 2 7 15 22 28

2 an

d 7

7 an

d 15

22 a

nd 2

8

2 an

d 28

2, 7

, an

d 15

15,

22 a

nd 2

8

2, 7

, 15

and

22

2, 7

, 15

, 22

and

28

PMU location

Var

iait

on

of

Err

or

in V

olt

age

An

gu

lar

Est

imat

es (

%)

Average Voltage Magnitude Estimates with

PMU

Average Voltage Angular Estimates with PMU

weight

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Dynamic State Estimation

Power system is dynamic system and its dynamism is triggered by its

continuous variation of loads.

These dynamic changes in the power system cannot be captured by the

traditional Static State Estimation (SSE) techniques

The estimation techniques are computationally intensive to be used

continuously, hence, they are run at fixed instants of time or when there is

sufficient change in the power system.

An advance Estimation technique will help:

Dynamic State Estimation

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DSE algorithms use mathematical modeling of the time

behavior of the system to cater to the dynamic changes of the

power system and predict the state of the system one step

ahead.

Once the new measurements at the next instant of time arrive,

the predicted values are simply upgraded or filtered to obtain a

more accurate estimate of the states.

This ability of predicting the system state ahead of time is a

big advantage as the security analysis can be performed ahead

of time and hence, gives the system operator more time to

decide on the future course of action, in case of an emergency.

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Dynamic State Estimation (DSE) The quasi static nature of the power system necessitates a more accurate

model to describe its time varying nature, which led to the use of DSE.

The Steps of DSE are:

Mathematical Modeling

State Prediction

State Filtering

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Model Used for DSE

Mathematical Model:

State Prediction:

Where

The covariance of the predicted state vector is given by:

1k k k k kx F x G w

1ˆ

k k k kx F x G

‘k’ refers to present instant of time and ‘k+1’ refers to the next instant of time.

( ) (1 )i i iF k

( ) (1 )(1 ) ( ) ( 1) (1 ) ( 1)i i i i i k iG k x k a k b k

( ) ( ) (1 ) ( )i i i ia k x k x k

( ) [ ( ) ( 1)] (1 ) ( 1)i i i i ib k a k a k b k

1

T

k k k k kN F F Q

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State Filtering:

The problem of incorporating the PMU measurements in to the DSE has to be dealt in the state filtering stage

Here the optimization function is given by:

The Extended Kalman Filter (EKF) technique is used for optimizing the above equation and the final equation for the filtering step can be written as

Where

1 1( ) [ ( )] [ ( )] [ ] [ ]T TJ x Z h x R Z h x x x N x x

1 1 1 1 1ˆ [ ( )]k k k k kx x K Z h x

1 1 1 1 1

1 1 [ ]T T T

k kK H R H R H N H R

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14 bus single PMU case

Location of PMU

Average Predicted Voltage

magnitude estimate error

(%)

Average Predicted

Voltage angular estimate error

(%)

Average filtered Voltage

magnitude estimate error

(%)

Average filtered Voltage angular estimate error (%)

No PMU 0.9363% 3.6236% 0.5368% 2.8683%

2 0.6603% 1.4824% 0.1662% 0.6247%

3 0.6832% 2.1125% 0.1641% 1.1004%

4 0.5618% 1.3379% 0.0970% 0.6088%

5 0.7029% 2.3787% 0.2115% 1.4202%

6 0.6373% 0.9601% 0.1864% 0.6438%

7 0.5538% 1.1105% 0.0448% 0.5825%

8 0.6603% 1.4824% 0.1662% 0.6247%

9 0.5841% 1.0379% 0.0429% 0.4715%

10 0.5167% 1.4697% 0.0513% 0.6662%

11 0.5517% 2.1092% 0.1020% 1.2301%

12 0.3175% 1.6836% 0.0697% 0.9207%

13 0.4259% 0.9797% 0.0995% 0.4142%

14 0.4540% 2.1743% 0.0459% 1.4805%

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IEEE 14 bus – Multiple PMU Case

location

Average Predicted

Voltage magnitude

estimate error (%)

Average Predicted

Voltage angular

estimate error (%)

Average filtered

Voltage

magnitude

estimate error

(%)

Average filtered

Voltage angular

estimate error

(%)

No PMU 0.9363% 3.6236% 0.5368% 2.8683%

7 and 9 0.5672% 1.0532% 0.0394% 0.4749%

7 and 10 0.5627% 1.2402% 0.0490% 0.5505%

7 and 14 0.5406% 1.2711% 0.0407% 0.6210%

9 and 10 0.5766% 1.2276% 0.0500% 0.6379%

9 and 14 0.5816% 1.0432% 0.0421% 0.4395%

10 and 14 0.5190% 1.6028% 0.0490% 0.7399%

7, 9 and 10 0.5816% 1.2153% 0.0453% 0.6159%

7, 10 and 14 0.5413% 1.2225% 0.0383% 0.5305%

7, 9, 10 and 14 0.5837% 1.2101% 0.0473% 0.5635%

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0.0000%

0.2000%

0.4000%

0.6000%

0.8000%

1.0000%

No

PM

U 7 9

10

14

7 a

nd

9

7 a

nd

10

7 a

nd

14

9 a

nd

10

9 a

nd

14

10

an

d 1

4

7,

9 a

nd

10

7,

10

an

d 1

4

7,

9,

10

an

d 1

4

location of PMU

Av

era

ge

Vo

lta

ge

Ma

gn

itu

de

Err

or

(%)

Predicted Values

Filteerd Values

0.0000%0.5000%1.0000%1.5000%2.0000%

2.5000%3.0000%3.5000%4.0000%

No

PM

U 7 9 10 14

7 an

d 9

7 an

d 10

7 an

d 14

9 an

d 10

9 an

d 14

10 a

nd 1

4

7, 9

and

10

7, 1

0 an

d 14

7, 9

, 10

and

14

Location of PMU

Ave

rag

e V

olt

age

An

gu

lar

Err

or

(%)

Predicted values

Filtered Values

Variation of voltage magnitude error for various locations of PMU

Variation of voltage angular error for various locations of PMU

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IEEE 30 bus - Single PMU case

Location

of PMU

Average

Predicted Voltage

magnitude

estimate error

(%)

Average

Predicted Voltage

angular estimate

error (%)

Average filtered

Voltage

magnitude

estimate error

(%)

Average

filtered Voltage

angular

estimate error

(%)

No PMU 1.3202% 13.2941% 0.7030% 5.8810%

2 1.2974% 7.4226% 0.4862% 3.6865%

7 1.2644% 7.4909% 0.4663% 3.1897%

15 1.3140% 5.9341% 0.3799% 1.6914%

22 1.1873% 6.3590% 0.3641% 1.8900%

28 1.3058% 7.1982% 0.5201% 3.5637%

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IEEE 30 bus – Multiple PMU case

Location of

PMU

Average

Predicted Voltage

magnitude

estimate error

(%)

Average Predicted

Voltage angular

estimate error (%)

Average filtered

Voltage magnitude

estimate error (%)

Average filtered

Voltage angular

estimate error

(%)

No PMU 1.3202% 13.2941% 0.7030% 5.8810%

2 and 7 0.9675% 8.6076% 0.4453% 2.9504%

7 and 15 1.2562% 6.3429% 0.3451% 1.7686%

22 and 28 1.1288% 6.7794% 0.2928% 2.2906%

2 and 28 1.2520% 7.5604% 0.4359% 3.5984%

2, 7 and 15 1.2512% 6.6138% 0.3360% 1.8787%

15, 22 and 28 1.1214% 5.9944% 0.2007% 1.9395%

2, 7, 15 and

22 1.1187% 6.0148% 0.1933% 1.7209%

2, 7, 15, 22

and 28 1.1037% 6.2560% 0.1818% 1.9434%

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IEEE 30 bus – Multiple PMU case

0.0000%0.2000%0.4000%0.6000%0.8000%1.0000%1.2000%1.4000%1.6000%

No

PM

U 2 7

15

22

28

2 a

nd

7

7 a

nd

15

22

an

d 2

8

2 a

nd

28

2, 7

an

d 1

5

15

, 2

2 a

nd

28

2, 7

, 1

5 a

nd

22

2, 7

, 1

5, 2

2 a

nd

28

PMU location

Avera

ge v

olt

ag

e m

ag

nit

ud

e E

rro

r

Predicted Values

Filtered Values

0.0000%

2.0000%4.0000%

6.0000%8.0000%

10.0000%12.0000%

14.0000%

No

PM

U 2 7 15 22 28

2 an

d 7

7 an

d 15

22 a

nd 2

8

2 an

d 28

2, 7

and

15

15, 2

2 an

d 28

2, 7

, 15

and

22

2, 7

, 15,

22

and

28

location of PMU

Ave

rae

Vo

ltag

e A

ng

ula

r E

rro

r

Predicted values

Filtered Values

Variation of error in predicted and filtered voltage magnitude estimates

Variation of error in predicted and filtered voltage angular estimates

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By 2013, many projects in North America are using

the following applications (though not in real-time

operations yet):

Wide-area monitoring and visualization

Voltage stability monitoring

Islanding and restoration

Post-event analysis

Model validation

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Summary

Growing power systems also result in a growing need for more accurate monitoring for detection and control of risks

Wide Area Monitoring Systems (WAMS) to have better visualization of grid to improve real time monitoring of power systems to enhance system operation capabilities

Integration of Wind and Solar Generations, and distributed generation and latency and time skew in data necessitates installation of PMUs

With developments in hardware and software technologies resulting in reduction of the prices of phasor measurement units, more and more utilities are increasing their PMU installations for Wide Area Monitoring of Power Systems

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Thank You

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