1

Abstract—Time delay has adverse effects on control systems in

an electric power grid. This paper analyzes the time delay issues,

and deals with the minimization of negative effects of time delays

in smart gird system. The prediction method has been used to

minimize the negative effects of time delay. The OPRT (optimal

reclosing time) method to reclose circuit breakers are considered.

30ms and 300ms communication delays have been considered.

The effectiveness of the proposed method has been tested in the

IEEE nine-bus power system model. Both balanced and

unbalanced permanent faults are considered. Simulations are

performed using Matlab/Simulink software. The performance of

the system considering time delays with and without the

predictor has been compared. From the simulation results, it is

shown that the proposed predictor performs well in minimizing

the negative effects of time delays, and that the prediction

method works more efficiently for a smaller time delay.

Index Terms-- Balanced fault, optimal reclosing time (OPRT),

predictor, time delay, total kinetic energy, unbalanced fault,

unsuccessful reclosing.

I. INTRODUCTION

IME delay arises in a wide range of power system

measurements. However, traditional controlling strategies

in power system only use the local measuring data. And the

time delay of the local measuring data is usually very small

(<10ms) compared to the system time constants and can be

ignored [1]. Since 1990s, centralized controllers using wide-

area signals have been suggested with the new technology

PMUs (phasor measurement units) [2]. PMUs are units that

measure dynamic data of power system, such as voltage,

current, load angle, output power and frequency. All these

measurements are synchronized by GPS (global positioning

system) satellites. As explained in [1], the system dynamic

performance can be enhanced if remote signals are applied to

the controller with respect to inter-area oscillations. However,

coincidentally time delay is usually obvious in wide area

measurement and may have impact on central controller.

The time delay caused by transmission of remote signals is

one of the key factors influencing the whole system stability

and damping performance [1]. In wide-area power systems the

time delay can vary from tens to several hundred milliseconds

[3].

Authors are with the Electrical and Computer Engineering

Department, University of Memphis, Tennessee, 38152, USA

[yzhou5@memphis.edu; sghosh2@memphis.edu;

mhali@memphis.edu; tewyatt@memphis.edu].

In the past, studies have been done in the engineering fields

that evaluate time delay impacts on the controlling mechanism

and power systems [1], [4-8], while minimizing the effect

caused by time delay [9]. In [9], a prediction method has been

mentioned that proposes an optimal reclosing scheme in extra

high voltage (EHV) lines, using the power angle, and power

angle velocity as input values to optimize the reclosing

method.

This paper analyzes the causes and effects of time delays,

and uses the prediction method [9] to minimize the negative

effects of time delay in smart grid applications. And this is the

main contribution of this work. The prediction method is

simple and efficient and easy to add to the controller without

changing the system configuration.

The effectiveness of the proposed method has been tested

in the IEEE nine-bus power system model [10]. The optimal

reclosing time (OPRT) method [10] has been used for finding

the reclosing times of circuit breakers for both balanced and

unbalanced permanent faults. The Windex (total kinetic energy

deviation index) with and without predictors are compared in

cases of different type of faults and delay times.

II. COMMUNICATION DELAY ISSUES IN SMART GRID

Communication delays in a smart grid occur mainly from

signal transmission through optical fiber, microwave, analog-

to-digital (A/D) conversion, online calculation of global input

variable, and the time synchronization of signals by GPS.

Such delay will affect the controllers and system performance

[11].

In wide area control system (WACS) communications

network, data are transmitted in the form of packets arranged

in three sections: the header, the payload, and the trailer. The

time delay has following four types [8]:

serial delays: the delay of having one bit being sent one

after another;

“between packet” serial delays: the time after a packet is

sent to when the next packet is sent;

routing delays: the time required for data to be sent

through a router, and resent to another location;

propagation delays: the time required to transmit data

over a particular communications medium.

The total signal time delay may be represented as

s b p rT T T T T (1)

ss

r

PT

D

(2)

Minimization of Negative Effects of Time Delay

in Smart Grid System Yang Zhou, Student Member, IEEE, Sagnika Ghosh, Student Member, IEEE, Mohd. Hasan Ali, Senior

Member, IEEE, and Thomas Edgar Wyatt, Senior Member, IEEE

T

978-1-4799-0053-4/13/$31.00 ©2013 IEEE

Minimization ofNegative Effects of Time Delayin Smart Grid System

2

p

lT

v

(3)

where Ts is the serial delay, Tb is the between packet delay, Tp

is the propagation delay, Tr is the routing delay, Ps is the size

of the packet (bites/packet), Dr is the data rate of network, l is

the length of the communication medium, and v is the velocity

at which the data are sent though the communications

medium.

Methods have been given to calculate different kinds of

time delay [8]. Unlike the small time delay in local control, in

wide-area power systems the time delay can vary from tens to

several hundred milliseconds or more [12-15]. In the

Bonneville Power Administration (BPA) system, the latency

of fiber optic digital communication has been reported as

approximately 38 ms for one way, while latency using

modems via microwave is over 80 ms [16]. Communication

systems that involve satellites may have even longer delays.

The delay of a signal feedback in a wide-area power system is

usually considered to be in the order of 100 ms [16]. If routing

delay is included, and if a large number of signals are to be

routed, there is potential for long delays and variability

(uncertainty) in these delays. According to some other reports

[17-18], 150 to 200 milliseconds communication delay values

are considered in the design of some actual transient stability

control systems. The time delay range chosen for this work is

30ms and 300ms.

III. DESCRIPTION OF MODEL SYSTEM

For the analysis of time delay issues, the nine-bus power

system model shown in Fig.1 has been used. The nine-bus

system consists of two synchronous generators (G1 and G2),

an infinite bus, transformers, double-circuit transmission lines,

and loads. In Fig.1, the double circuit transmission line

parameters are numerically shown in the forms R+jX(jB/2),

where R, X and B represent resistance, reactance and

susceptance, respectively, per phase with two lines. The AVR

(automatic voltage regulator) and GOV (governor) control

models and the parameters of Generator 1 and Generator 2 are

mentioned in [20]. The system bases are 50 Hz and 100 MVA,

and the capacities of generators G1 and G2 are 200MVA and

130 MVA, respectively. Faults are considered to take place at

F1 (between bus 5 and 7), F2 (between bus 6 and 9), and F3

(between bus 8 and 9). In this paper, we considered 3LL

(three-line), 2LG (double-line-to-ground), 2LL (line-to-line)

and 1LG (single-line-to-ground) permanent faults.

IV. OPTIMAL RECLOSING TECHNIQUE

Compared to conventional reclosing, which adopts a

constant value of reclosing time, optimal reclosing time keeps

the system more stable, especially in case of unsuccessful

reclosing [10]. Reclosing of circuit breakers at optimal

reclosing time (ORCT) can maintain synchronism and

enhance transient stability of the system. In this paper, the

optimal reclosing time is considered as the point which meets

the following conditions:

I. Without the circuit breakers reclosing, the point when the

total kinetic energy oscillation becomes the minimum.

II. The value obtained from condition I must be greater than

TCB, the required reclosing time for circuit breakers to deionize

the fault arc. The calculation equation for TCB is from trials

and experiments done by previous researchers [19], and shown

below:

(10.5 )

34.5CB

KVT cycles

where KV indicates the line-to-line rms voltage of the system.

Here 1 cycle =20 ms. If the first minimum point in condition I

is less than TCB, the second minimum time of the kinetic

energy response should be chosen. In this paper, TCB is

calculated as 0.223 sec.

Fig. 1. IEEE Nine-bus Power system model.

This method [14] uses the kinetic energy of each generator,

which can be obtained easily. The total kinetic energy, Wtotal,

can be calculated by knowing the rotor speed of each

generator and is given by

2

1

1( )

2

N

total i mi

i

W J J

where wmi is the rotor angular velocity in mechanical rad/s, i is

generator number, N is total number of generators, and Ji is

the moment of inertia in kg·m 2 shown below

2

9 2

( )

5.48 10

i

i

i rating

i

s

H MVAJ kg m

N

In (6), Hi is inertia constant of ith generator and

120

si

fN

p

(7)

where f is system frequency, p is the number of poles, and Nsi

is the synchronous speed of the ith generator.

Fig. 2 gives an example of how to get OPRT when 2LL

fault occurred at point F3 in the nine-bus system. Fig. 2 shows

(6)

(5)

3

the total kinetic energy response without reclosing operation

after fault happening. The time for the first minimum point is

0.8366 sec, which is greater than TCB.

Using the optimal reclosing technique, the values of

optimal reclosing time corresponding to different types of

permanent faults at different points are calculated from the

simulation graphs of total kinetic energy responses, and are

shown in Table I.

Fig. 2. Response for OPRT (fault type: 2LL at F3 )

TABLE I

OPRT FOR DIFFERENT TYPES OF FAULTS AT DIFFERENT POINTS

V. PREDICTION METHOD IN TIME DELAYED SYSTEM

A. Communication Delay in IEEE Nine-Bus System

In this work, simulations are carried out by

Matlab/Simulink software. During the simulation, constant

time delays (td) 30ms and 300ms are applied to controller

input. Fig. 3 illustrates the signal which has transmission delay

corrected by predictor, and then goes to the controller.

Fig.3. Figure of delay and predictor.

B. Prediction Method Used in Minimizing Negative Effects of

Communication Delay

The flow diagram for optimal reclosing including the

predictor algorithm is shown in Fig. 4. Since the predictor can

modify the delayed signal to the original curve before

inputting it into controller, the predictor easily adapts to

different kinds of controllers.

As already explained, the OPRT is the time when the total

kinetic energy response first reaches its minimum point. There

will be a delay between the measured OPRT and the actual

OPRT. If the time delay is td, the predicted OPRT can be

obtained from the measured point, previous measured point

and changing rate [9]:

p previous dTKE TKE t (8)

( 1)k kTKE TKE

t

(9)

where p is the predicted point, k is measured point, and w is

changing rate of Wtotal..

Since the total kinetic energy curve is not linear, the larger

the value of td, the greater the error in the prediction curve.

However, the delay time is generally very small and the errors

can be ignored. Fig. 5 is the OPRTs calculation for predicted,

delayed and ideal curves in case of 3LG fault at points F1 and

F3. In Fig. 5, Ti, Tp and Td are OPRTs for ideal, predicted, and

delayed system, respectively. The changing trends of predicted

curve and ideal curve are almost the same. Only the amplitude

is different, however that will have little influence on

controller.

Fig.4. Flow chart diagram of OPTR with predictor.

Fault type Fault point OPRT(sec)

3LG

F1 0.9590

F2 0.9572

F3 0.8426

3LL

F1 0.9597

F2 0.9558

F3 0.8415

2LG

F1 0.9175

F2 0.9135

F3 0.8465

2LL

F1 0.9474

F2 0.9380

F3 0.8366

1LG

F1 0.8930

F2 0.8860

F3 0.8640

4

(a) 3LG Fault at F1

(b) 3LG Fault at F3

Fig.5. Responses for predicted, delayed and ideal OPRTs for different time

delay systems (a) 300ms delay and (b) 30ms delay

Fig.6. Predictor in Matlab/Simulink.

Fig. 6 illustrates the predictor which is simulated in

Matlab/Simulink. The input signal is the Wtotal, and the signals

into the scope are the delayed version of Wtotal and the

predictor signal.

VI. SIMULATION RESULTS AND DISCUSSION

In this work, 3LG, 3LL, 2LG, 2LL and 1LG permanent

faults are considered to occur at points F1, F2 and F3 of the

power system model in Fig. 1. It is assumed that the fault

occurs at 0.0 sec, circuit breakers opens at 0.1 sec, breakers

reclose at OPRT time and reopen at the time 0.1sec after the

OPRT. The delay time td is chosen as 30ms and 300ms.

The difference between the total kinetic energy (Wtotal) of

the generators at transient state and steady state is defined as

the total kinetic energy deviation, TKED.

total transient state total steady stateTKED W W (10)

For the evaluation of transient stability, the total kinetic

energy deviation index, Windex, has been used in this paper.

0

T

index

TKEDdtW

system base power

(11)

Where, T is the simulation time selected as 20.0s, TKED is the

total kinetic energy deviation as calculated in (10). It is

apparent that the system performs better for smaller values of

Windex. TABLE II

COMPARISON OF indexW IN IDEAL SYSTEM AND 30MS DELAY SYSTEM WITH

AND WITHOUT PREDICTOR

OPRT, Windex Without delay

30ms delay

without

predictor

30ms delay with predictor

Fault

type Fault

point Time Windex Time Windex Time Windex

3LG

F1 0.9590 0.3530 0.9890 0.3966 0.9620 0.3563

F2 0.9572 0.6582 0.9872 0.7291 0.9538 0.6525

F3 0.8426 0.3848 0.8726 0.4395 0.8440 0.3837

3LL

F1 0.9597 0.3467 0.9897 0.3906 0.9540 0.3407

F2 0.9558 0.6557 0.9858 0.7265 0.9586 0.6489

F3 0.8415 0.3741 0.8715 0.4271 0.8423 0.3796

2LG

F1 0.9175 0.2195 0.9475 0.2499 0.9150 0.2186

F2 0.9135 0.5919 0.9435 0.5405 0.9148 0.4950

F3 0.8465 0.3395 0.8765 0.3730 0.8534 0.3442

2LL

F1 0.9474 0.1904 0.9802 0.2032 0.9550 0.1946

F2 0.9380 0.3336 0.9680 0.3597 0.9348 0.3468

F3 0.8366 0.1958 0.8666 0.2269 0.8335 0.1950

1LG

F1 0.8930 0.09645 0.9230 0.1144 0.8960 0.1003

F2 0.8860 0.2759 0.9160 0.3023 0.8766 0.2715

F3 0.8640 0.2092 0.8940 0.2258 0.8652 0.2140

TABLE III

COMPARISON OF indexW IN IDEAL SYSTEM AND 300MS DELAY SYSTEM WITH

AND WITHOUT PREDICTOR

OPRT,Windex Without delay 30ms delay

without

predictor

30ms delay with

predictor

Fault

type Fault

point Time Windex Time Windex Time Windex

3LG

F1 0.9590 0.3253 1.259 0.6974 1.0625 0.4455

F2 0.9572 0.6582 1.2527 1.015 1.0627 0.8229

F3 0.8426 0.3848 1.2426 0.5102 0.9810 0.4990

3LL

F1 0.9597 0.3467 1.2597 0.6920 1.0625 0.4403

F2 0.9558 0.6557 1.005 0.9776 1.0627 0.8142

F3 0.8415 0.3741 1.1415 0.4213 0.983 0.4998

2LG

F1 0.9175 0.2195 1.2175 0.4744 1.015 0.2760

F2 0.9135 0.4917 1.2135 0.8684 1.004 0.5996

F3 0.8465 0.3395 1.1465 0.5690 0.9832 0.4334

2LL

F1 0.9474 0.1904 1.2474 0.3707 1.0446 0.2283

F2 0.9380 0.3336 1.2380 0.4865 1.0246 0.3995

F3 0.8366 0.1958 1.2366 0.2963 0.9632 0.2271

1LG

F1 0.8930 0.09645 1.1930 0.2488 0.9965 0.1275

F2 0.8860 0.2759 1.286 0.5269 0.9860 0.3455

F3 0.8640 0.2092 1.264 0.3954 1.004 0.2776

5

Tables II and III compare the value of indexW with and

without the predictor for 30ms and 300ms delays,

respectively. The system will oscillate more when Windex is

larger. It can be seen from Tables II and III that,

1) The 3LG fault exhibits the most oscillation while the 1LG

fault shows the least. For example, under without delay

situation, the indexW value for 3LG at F1 is 0.3530 while

for 1LG is 0.09645.

2) With the same fault type and fault point, a larger time

delays in the system has larger oscillation. For example,

for the 2LG fault at F2, Windex of 300ms delayed system is

0.8684; while 30ms delayed system is 0.5405.

3) The predictor assists in maintaining system stability when

compared to the ideal and delayed system. For example,

for 300ms delayed system, when 3LL fault occurs at point

F1, the Windex for ideal, predicted and delayed system are

0.6557, 0.8142 and 0.9776, respectively.

Some of the simulation results have been shown in Figs. 7-

10. Figs. 7-10 are the total kinetic energy responses for 3LG,

and 1LG permanent faults at points F1, F2 and F3 with and

without predictor for cases of 300ms and 30ms delays. For

300ms delayed system, we can see from Figs. 7-9 that there

are significant differences among ideal, with predictor and

without predictor cases. This demonstrates that although the

predictor is useful for minimizing oscillations arising from

delay, the oscillation is still larger than in the ideal curve. In

Fig. 10, we can see that for small delayed system, the curve

with the predictor is almost similar to the ideal curve.

Fig. 7. Responses for with and without predictor in case of 3LG permanent

fault at F1 in 300ms delayed system.

Fig.8. Responses for with and without predictor in case of 3LG permanent

fault at F2 in 300ms delayed system.

Fig.9. Responses for with and without predictor in case of 1LG permanent

fault at F2 in 300ms delayed system.

Fig.10. Responses for with and without predictor in case of 3LG permanent

fault at F2 in 30ms delayed system.

VII. CONCLUSION

This paper deals with the minimization of negative effects

of time delays in a smart gird system. The prediction method

has been used to minimize the negative effects of time delay.

6

The OPRT (optimal reclosing time) method to reclose circuit

breakers is considered. Time delays of 30ms and 300ms as

well as balanced and unbalanced permanent faults at different

locations have been considered. From the simulation results,

the following points are noteworthy.

a) The proposed predictor performs well in minimizing the

negative effects of time delays.

b) The predictor can work more efficiently and accurately

when the time delay is smaller.

Simulation results also show the predicted curve may have

oscillations in some scenarios. Future works should be

directed to exploring ways to: minimize oscillations in the

prediction method, combining prediction methods with

methods to minimize communication delay, and finding more

methods to minimize the effect of time delay in a smart grid.

VIII. REFERENCES

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[15] W. Yao, L. Jiang, Q. H. Wu, J. Y. Wen, and S. J. Cheng, “Delay-

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IX. BIOGRAPHIES

Yang Zhou (S’12) received her B.Sc. degree in Electrical Engineering & Automation from China Three Gorges University, in 2010. Currently she is

pursuing her M.Sc. degree in electrical engineering at the University of Memphis, Memphis, Tennessee, USA. Her main fields of interests includes

advanced power systems, smart-grid, renewable energy systems, energy

storage system, and power delivery. She is a student member of the IEEE.

Sagnika Ghosh (S’12) received her B.Tech in Electrical Engineering from

Academy of Technology, India, in 2010. She worked in TIL India as electrical

design engineer since September 2010 to April 2011. Currently she is pursuing her PhD degree in electrical engineering at University of Memphis,

Memphis, Tennessee, USA. Her main field of interest includes advanced

power systems, smart-grid and micro-grid systems, renewable energy systems,

energy storage systems, and flexible AC transmission systems (FACTS). She

is a student member of the IEEE.

Mohd. Hasan Ali (SM’08) received his Ph.D. Degree in Electrical and Electronic Engineering from Kitami Institute of Technology, Japan, in 2004.

He served as a lecturer in EEE dept. of RUET, Bangladesh since 1995 to 2004,

and also became an assistant professor in the same university in 2004. Dr. Ali was a Postdoctoral Research Fellow under the Japan Society for the

Promotion of Science (JSPS) Program at the Kitami Institute of Technology,

Japan, since November 2004 to January 2007. He also worked as a Research Professor in Electrical Engineering Department of Changwon National

University, South Korea since February 2007 to December 2007. He was a

Postdoctoral Research Fellow with the Electrical and Computer Engineering Department of Ryerson University, Canada, from January 2008 to June 2009.

He also worked as a Faculty at the Electrical Engineering Department,

University of South Carolina, USA, since July 2009 to August 2011. Currently he is working as an Assistant Professor at the Electrical and

Computer Engineering Department, University of Memphis, Tennessee, USA.

He leads the Electric Power and Energy Systems Laboratory of the University.

His main field of interest includes advanced power systems, smart-grid and

micro-grid systems, renewable energy systems, energy storage systems, and flexible AC transmission systems (FACTS). Dr. Ali is a Senior Member of the

IEEE Power and Energy Society (PES). Also, he is the Chair of the PES of the

IEEE Memphis Section.

Thomas Edgar Wyatt, P.E (SM ’88) received a B.S. (1973) from The

University of Tennessee at Martin, B.S.E.E. (1979) and M.S.E.E. (1985) from

Memphis State University. He was employed at Memphis Light Gas and Water Division, Memphis, Tennessee from 1980 until 2005 working in

Distribution Planning, Systems Operations and as Supervisor of the Reliability

and Power Quality Department. Mr. Wyatt joined The University of Memphis as a Research Assistant in the Center for Advanced Sensors and worked as a

Visiting Assistant Research Professor prior to becoming an Instructor in 2007.

His current interest areas are: Infrared Detection and Power Quality.