2204development of a wide area measurement system for smart grid applications m. m. amin, student...

Development of a Wide Area Measurement System for Smart Grid Applications

M. M. Amin, Student Member, IEEE, H. B. Moussa, Student Member, IEEE

and O. A. Mohammed, Fellow, IEEE

Energy Systems Research Laboratory, Electrical & Computer Engineering Department, Florida international university,

Miami, FL 33174, USA (Tel: +1 305-348-3040; e-mail: mohammed@ fiu.edu).

Abstract:

In this paper, the modeling for a complete scenario of a proposed wide area measurement system (WAMS)

based on synchronized phasor measurement units (PMUs) technology with the access of a broadband

communication capability is presented. The purpose is to increase the overall system efficiency and

reliability for all power stages via significant dependence on WAMS as distributed intelligence agents with

improved monitoring, protection, and control capabilities of power networks. The developed system is

simulated using the Matlab/Simulink program. The power system layer consists of a 50 kW generation

station, 20 kW wind turbine, three transformers, four circuit breakers, four buses, two short transmission

lines, and two 30 kW loads. The communication layer consists of three PMUs, located at generation and

load buses, and one phasor data concentrator (PDC), that will collect the data received from remote PMUs

and send it to the control center for analysis and control actions. The proposed system is tested under two

possible cases; normal operation and fault state. It was found that power system status can be easily

monitored and controlled in real time by using the measured bus values online which improves the overall

system reliability and avoids cascaded blackout during fault occurrence. The simulation results confirm the

validity of the proposed WAMS technology for smart grid applications.

1. INTRODUCTION

WAMS became one of the most recent technologies that are

popular for upgrading the traditional electric grid. This

upgrade has become a necessity to modernize the electricity

delivery system following the occurrence of major blackouts

in power systems around the world. Although many

algorithms have been developed in the past for online

monitoring of transmission systems, distribution systems and

estimation of operating frequency, with a few examples

included as references (A. A. Girgis 1982, M. S. Sachdev

1985 and S. Paul et al. 1987), but still the required level of

details for online assessment is yet to be achieved. In early

1980s, synchronized phasor measurement units (PMUs) were

first introduced and since then have become the ultimate data

acquisition technology, which will be used in wide area

measurement systems with many applications that are

currently under development around the world (H. Hr. 2004).

Synchronized phasor measurements, or synchrophasors,

provide a method for comparing phase and sequence values

from anywhere on a power system which can be integrated

with phasor data concentrators (PDCs) at substations in a

hierarchical structure (A. R. Metke 2010 and M. Pipat. 2009).

The precise and accurate data that can be acquired from

PMUs in a WAMS built on the power system confirms the

need for a robust, reliable communication network with

secure and high speed capabilities for online data access.

As smart grid applications, utility power grid analysts can get

benefited from WAMS in validation of system models and

components which has been one of the first uses of

synchrophasors. This validation occurs through use of inter-

area communication or simultaneous data collection of

conditions at a single point in time (Z. Zhong 2005).

In addition, Real-Time System Monitoring (RTSM) for

stability assessment and state measurement is another

application where phasor measurements at nodes help system

operators to gain a dynamic view of the power system and

initiate the necessary measures in proper time, with the latest

IEEE standard (C37.118-2005) developed to standardize data

transmission format and sampling rates of PMUs. This can

significantly be supported by the stability assessment

algorithms, which are designed to take advantage of the

phasor measurement information (M. Venk. 2009).

In the past, post-event analysis was an application of

synchrophasors (PMUs) without wide-area communication

where data was archived locally. However, it was not a useful

tool for online (dynamic) control. Recently, Real-Time

Control (RTC) of WAMS became a powerful control and

analysis tool that provides a new view of power systems (J.

De La Ree 2010). This is achieved by improving

communication network capabilities while maintain PMUs as

a main component in the network. The use of PMUs for RTC

will increase the control accuracy since data are measured

online. Also, it will enhance the power system stability and

delivery automation capabilities after challenges of new data

communication requirements across the system are firstly

resolved (A. Bose 2010, Y. Zhang 2008 and A.G.Pha. 2007).

Depth of observability is another advantage for PMUs. It

means the ability of measuring bus voltage phasor directly or

calculating it using the PMU voltage and line current of the

Preprints of the 18th IFAC World CongressMilano (Italy) August 28 - September 2, 2011

Copyright by theInternational Federation of Automatic Control (IFAC)

1672

nearest connected bus. This is a cost effective part since it

reduces the number of data acquisition tools needed across

the network as measuring line currents can extend the voltage

measurements to buses where no PMU is installed. In Fig. 1,

a simple generalization of PMU block diagram is shown,

which serves as the basis of simulating such unit (R. F.2005).

This paper discusses a method to utilize this type of data

collection to check the health state of power system

networks. This is achieved through building WAMS

infrastructure communication network. The performance of

the overall proposed system is investigated through a Matlab

simulation of PMUs in a small scenario of a WAMS on a 4-

bus utility network with the associated communication

network. This paper is organized as follows. In section 1, an

introduction to the subject is presented. In Sections 2, 3

description and mathematical modeling of the system are

introduced. In Section 4, test results are presented and

discussed under different system conditions. Finally, Section

5 presents some conclusions.

2 . SYSTEM DESCRIPTION

The principle of a WAMS network based on synchrophasors

data with the aid of a broadband communication network is

described in this section. The system consists mainly of two

layers as shown in Fig. 2. First, the electrical power system

layer which consists of line-line 208V generation station with

50 kW output rated power, 208V wind turbine as a renewable

source of 20 kW rated power, 3-power transformers (T1, T2,

and T3) linking different parts of the electrical system, 2-

short transmission lines (T.L1, and T.L2), 4-buses (B1, B2,

B3, and B4), 4- circuit breakers (CB1, CB2, CB3, and CB4)

and 2-loads each of 30 kW (O. A. Mohammed 2005). Second,

the WAMS layer which consists of 3-PMUs, and 1-PDC that

will collect the data received from remote PMUs and send it

to the control center for analysis and control actions (Bei Xu

2005).

3. SYSTEM MODELING

A small size WAMS platform built on a 208V, 60 Hz test-

bed network was modeled as shown in Fig. 3. This proposed

communication network can be implemented in the lab by

locating one PMU at each generation or load bus where all

PMUs will send its measured voltage and current

measurements to the PDC in order to monitor the system

status and taking the proper control action if required.

Furthermore, depth of observability can be utilized here in

order to significantly reduce system costs through reducing

the number of PMUs since one can read voltage and current

measurements at its bus and other buses measurements

locating at same area can be calculated. However, this

algorithm has less accuracy than installing one PMU at each

bus. A simulation of the PMU units was done with using

sampling clock pulses to achieve synchronization between

synchrophasors which are phase locked to the signal provided

by the Global positioning system (GPS) receiver built inside

or outside the PMU. The GPS module is simulated as a clock

enabling pulses sent to all PMUs at the same time so that all

of them will have the same time tags and in accordance the

same reference wave can be used at all different PMU

locations through the WAMS.

Fig. 1. Block Diagram of PMU

Fig. 2. Schematic diagram of the proposed WAMS according

to the installed test-bed power system in the lab.

3.1 PMU network analysis

The PMU has to separate the fundamental frequency

component from other harmonics and find its phasor

representation. Discrete Fourier transform (DFT) is then

applied on the sampled input signal to compute its phasor.

Also, it has to compensate for the phase delay introduced to

the signal by the antialiasing filters present in the input to the

PMU. For x��k = 0,1, … , N� where N is the number of

samples taken over one period, then the phasor representation

will be given by;

= √2� � ��

��

�� (1)

Since the components for real input signals at a frequency

appears in DFT and are complex conjugates of each other so

they can be combined giving the factor of 2 in front of the

summation in (1) and then the rms of the fundamental

frequency peak value can be obtained through dividing by

√2. Matlab Simulink model was built to evaluate the system

performance. Different cases were studied during normal

CB3

Control Station

B2

B3

T3

Rx

T1 T2

B4

Wind Turbine

B1

CB4 30 kW

Load

30 kW

Load

PMU1 PMU2

PMU3

T.L1

T.L2

Gen.

Station CB1 CB2

RPDC

S/H

A/D

PLL

Oscillator

Analog

Filter

Micro

Processor

GPS

Receiver Antenna

Analog

Input

Remote

Communication

Local

Communication


1673

Fig. 3. Simulink model for a scenario of the proposed PMUs communication network layer on a power system Smart Grid

Test-bed in our Laboratory.

mode of operation and fault occurrence mode. The simulated

system parameters are shown in Table 1.

In Steady State, all generators have the same frequency

( �� Hz). In accordance, the voltage at all points of the power

system will have the same frequency ��which is measured

by PMU according to the following equation;

!"#$ = %!&'( "2) ��# + +!$ (2)

In case of frequency disturbance, the power system different

generators will run with different frequencies and each

generator may be considered as a voltage source with

different values of %! , �� ,-. +! as slow time varying

functions. It can be assumed for a small interval of time

"∆t = n cycles$ that E8, f:: and δ8 constants. As a result, the

power system can be represented as a circuit with several

voltage sources of different frequencies. The actual voltage at

any bus i using superposition theorem becomes as in (3)

(Ning Jiaxin 2009);

>!?@A = >!,�"#$ + ⋯ + >!,�C"#$ = ∑ >!,�"#$�C�� =

∑ E!,�&'(F2)��C# + G!,�CH�C�� (3)

Where E!,� represents the voltage at bus i due to generator j.

which indicates that this bus have a multi frequency voltage

that are all close to 60 HZ. In dynamic power system studies

this can be estimated as in (4);

>!I�A"#$ = E!I�Acos "2)�!I�A# + G!I�A$ (4)

In equation (4), the frequency �!I�A represents the frequency

of the system at this location and equals to the frequency

measured by the PMU at that bus by assuming that >!I�A"#$ =>!?@A and having access to the sampled data of

>!?@A so �!I�Acan be easily evaluated (R. Malpani 2010).

3.2 Communication channel analysis

IEEE PC37.118 16 protocol format is usually used in PMUs

communication. This format standard includes frequency and

rate of change of frequency in each message. Once the

frequency and size of the messages are known, the following

equation can be used to determine the bit-per-second (bps)

rate at which the data can be sent (V.K. Sood 2009);

KL( = 1.2"-- . N . �$ (5) where:

nn = message size (bytes)

L = length of frame (1 start bit, 8 data bits, 2 stop bits, 1

parity = 12)

f = messages frequency

1.2 = factor to account for system delays (based on typical

experience)

The synchrophasor data can be sent at various rates,

depending on application requirements. The communications

link connecting the substations could be a fiber-optic

multiplexer. Relays communicate with the multiplexer via

EIA-232 asynchronous interface.

Enable

1To control center

Discrete,Ts = 5e-005 s.

powergui

[A]clk pulses

Wind TurbineInduction Generator

20 KW

v+ -

v+ -

v+ -

Vab3

Vab2

Vab1

A

B

C

a

b

c

Transformer 3

A

B

C

a

b

c

Transformer 2

A

B

C

a

b

c

Transformer 1

A B C

A B C

TL2

Va

Ia

Va-ph

Ia-ph

Rx

En

ab

le

C1

B1

A1 A B C

PMU3

En

ab

le

C1

B1

A1 A B C

PMU2

En

ab

le A B C

A1

B1

C1

PMU1

PDC

A

B

C

Main AC Grid50 KW

A B C

Load30 kW

[A]

From2

[A]

From1

[A]

From

A

B

C

A

B

C

Fault

Breaker

A

B

C

a

b

c

CB4

A B C

a b c

CB3

A

B

C

a

b

c

CB2

A

B

C

a

b

c

CB1

A

B

C

a

b

c

B4208 V

A

B

C

a

b

c

B3208 V

A

B

C

a

b

c

B2208 V

A

B

C

a

b

c

B1208 V

AC Load : 30 kW

A B C

1 Watt

ABC

ABC

TL1


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4. SIMULATION RESULTS

A Matlab Simulink model has been constructed in order to

investigate the performance of the proposed WAMS for

smart grid applications. The model was carried out according

to the operation that has been described in section 2. The

simulation parameters are shown in Table 1. In order to

estimate the PMUs characteristics, two types of tests are

carried out. The first is normal operation test without any

fault or unbalance in the network. The second is fault

operation test which used as an extreme condition to show

the behavior of the network under this condition.

Table 1. System simulation parameters

Symbol Parameter Value

vg, vt Generator and wind

turbine output voltage

208 Vrms

fg, ft Generator and wind

turbine frequency

60 Hz

R PMU message report

rate

60 msg/sec

Pg Generator power

rating

50 kW

Pg Wind turbine power

rating

20 kW

L Load rating 60 kW

4.1 Normal operation test

The electrical system under normal operation conditions is

observed. The 30 kW load on bus 2 is supplied from the wind

generator sharing with generation station and the other 30 kW

load on bus 3 supplied by the generation station, so all PMUs

shows stable readings within the references.

From Figs. 4-6, the three PMUs read accurate information

about line voltage >?O; a sampled data of about 296 V

average voltage amplitude starts from 0 sec for bus 1 and 2.

At bus 3, zero voltage amplitude for the first 0.1 sec at no

load then tracking the right average voltage amplitude level

as other buses with a phase difference of 2.65 degrees at

stable state for all readings. The exported data by the

simulated PMUs to the control center shows that the

developed WAMS succeeded to accurately reflect the system

status in real-time (online). However, for complete

verification of its performance another test with applying a

fault at bus 3 and observing their responses.

4.2 Fault operation test

In this case, a three line to ground short circuit (3-LG SC)

fault is applied at bus 3. Figs. 7-10 show the readings for all

PMUs at the 3-buses. According to Fig. 7, the whole system

shows normal operation for 0.2 sec while bus 3 is loaded

after 0.1 sec. the fault is occurred after 0.2 and it is cleared

after 0.05 sec. PMUs 1&2 reads larger phase differences

(7.54 degrees) than in normal mode (2.65 degrees) while the

voltage amplitude dropped with small value (10 V) which

means that the fault is not located on their buses area as

shown in Figs. 8, 9. On the other hand, PMU 3 has extremely

phase difference change (50 degrees) associated with a large

drop in the voltage amplitude as a result to the fault that

occurs in this area as shown in Fig. 10. Consequently, the

control center has to send control signal to the relay to release

the circuit breaker at that bus upon receiving these data in

real time from PMUs to protect the other generation stations

which are the most valuable part in the power network from

damage, preventing cascaded turnoff stations which may

result in major blackouts and maintaining a healthy power

system (S. H. Horowitz 2003). Furthermore, it helps analysts

to determine the type of fault that has been occurred using the

data transmitted from PMUs.

Fig. 4. PMU1 readings under normal operation condition.


0 0.05 0.1 0.15 0.2 0.25 0.30

100

200

300

400Vab-S

am

ple

d0 0.05 0.1 0.15 0.2 0.25 0.3

-400

-200

0

200

400

Vab R

ef. &

Act.

PMU1PMU1PMU1PMU1

0 0.05 0.1 0.15 0.2 0.25 0.30

1

2

3

4

time sec

Phase A

ngle

Phase Diff. =2.65

0 0.05 0.1 0.15 0.2 0.25 0.30

100

200

300

400

Vab-S

am

ple

d

0 0.05 0.1 0.15 0.2 0.25 0.3-400

-200

0

200

400

Vab R

ef. &

Act.

PMU2

0 0.05 0.1 0.15 0.2 0.25 0.30

1

2

3

4

time sec

Phase A

ngle

Phase Diff. =2.65


1675


Fig. 7. Line voltages of Buses 1, 2 and 3

Fig. 8. PMU1 readings during fault occurrence


0 0.05 0.1 0.15 0.2 0.25 0.30

100

200

300

400

Vab-S

am

ple

d

0 0.05 0.1 0.15 0.2 0.25 0.3-400

-200

0

200

400

Vab

Ref.

& A

ct.

PMU3

0 0.05 0.1 0.15 0.2 0.25 0.30

1

2

3

4

time sec

Ph

ase A

ngle

Phase Diff. =2.65

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

-500

-300

-100

100

300

VL

ine-B

1

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

-500

-300

-100

100

300

VL

ine-B

2

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

-300

-100

100

300

time sec

VL

ine-B

3

0.18 0.2 0.22 0.24 0.26 0.28-400

-200

0

200

400

Va

b R

ef.

& A

ct.

PMU1

0.18 0.2 0.22 0.24 0.26 0.280

100

200

300

Va

b-A

mp

litu

de

0.18 0.2 0.22 0.24 0.26 0.280

1

2

3

4

time sec

Ph

as

e A

ng

le

Phase Diff.=7.54 degrees

0.18 0.2 0.22 0.24 0.26 0.28-400

-200

0

200

400

Vab

Ref.

& A

ct.

PMU2

0.18 0.2 0.22 0.24 0.26 0.280

100

200

300

Vab

-Am

plitu

de

0.18 0.2 0.22 0.24 0.26 0.280

1

2

3

4

time sec

Ph

ase A

ng

le

Phase Diff.=7.54 degrees

Normal

operation

with no

load at

B3

Normal

operation

with

connecting

load at B3

Fault

operation

Occurrence

at B3

Return to

normal

operation

with loading

at B3


1676


5. CONCLUSIONS

A performance analysis for a PMU based WAMS network

was presented. The developed system was tested under two

different possible conditions. The simulated PMUs shows

coincident data with the real values of a maximum phase

difference equal to 2.65 degrees and normal average

amplitude reading which shows the system stability. In this

case, no action has to be taken from the control center during

the dynamic system monitoring. On the other hand, during

the fault state the PMUs data shows that the system has

unstable part with about 50 degrees phase difference added to

a large drop in voltage amplitude in the area where the fault

was occurred which should be disconnected or cleared via

dynamic control signals before spreading to other parts

resulting in catastrophic failure in some parts of the power

system or blackouts. The developed simulated WAMS

network verified its effectiveness for smart grid applications.

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0.18 0.2 0.22 0.24 0.26 0.28-400

-200

0

200

400

Vab

Re

f. &

Act.

PMU3

0.18 0.2 0.22 0.24 0.26 0.280

100

200

300

Va

b-A

mp

litu

de

0.18 0.2 0.22 0.24 0.26 0.280

1

2

3

4

time sec

Ph

ase

An

gle

Phase Diff.=50 degrees


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2204development of a wide area measurement system for smart grid applications m. m. amin, student...

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