detecting and locating faulty nodes in smart grids.pdf

7/27/2019 Detecting and Locating Faulty Nodes in Smart Grids.pdf

http://slidepdf.com/reader/full/detecting-and-locating-faulty-nodes-in-smart-gridspdf 1/9

IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 2, JUNE 2013 1067

Detecting and Locating Faulty Nodes in Smart GridsBased on High Frequency Signal Injection

Amir Mehdi Pasdar , Student Member, IEEE , Yilmaz Sozer , Member, IEEE , and Iqbal Husain , Fellow, IEEE

Abstract— An on-line method for detecting and locating a faultynode in the utility grid is proposed for smart grids. The methodis based on injection of high frequency (A-Band) current signalinto the grid that would impose voltages (less than 1V according

to EN50065-1 standard) on the nodes to determine changes in theimpedance characteristics. This detection is accomplished on-line

without interrupting the powerflow in the network. The developedalgorithm has been implemented within an electrical power systemmodel. This low voltage network model has been tested with dif-ferent fault scenarios. The proposed procedure is able to detect the

faulty nodes with high accuracy.

Index Terms— Faulty node detection, illegal electricity usage,

signal injection to grid, smart grid services.

I. I NTRODUCTION

F AULT IN THE power grids is an unpredictable situation

which could lead to an unbalance in the overall system

operation. A Faulty node (FN) could appear in between the seg-

ments of the distribution network due to a variety of reasons.

Illegal connection for electricity pilferage is one unwanted way

of establishing a FN. This type of connection leads to an unpre-

dictable technical as well as economical problems in the utility

industry. From an economic point of view, this issue has a dra-

matic impact on the supplier’s budget because of unaccounted

electricity usage [1]. Early restoration of the fault improves the

reliability and safety of the distribution network [2], [3].

A new method for the detection of illegal usage based on au-

tomatic meter reading has been suggested in [4]. The method is

based on traditional automatic meter readers (AMR). The host

controller can detect the existence of the FN in each feeder by

comparing the sum of all the energy meters within the system

with the host energy meter. The disadvantage of this method is

its inability to localize the FN. This method also requires high

quality calibrated energy meters. In [5], a FN locating method

for a high voltage long range simple distribution network is pre-sented. The fault is detected based on the difference between the

Manuscript received April 15, 2011; revised September 02, 2011, December 19, 2011, May 18, 2012, and July 30, 2012; accepted September 18, 2012. Dateof publication April 12, 2013; date of current version May 18, 2013. Paper no.TSG-00150-2011.

A. M. Pasdar and Y. Sozer are with Electrical Engineering, University of Akron, Akron, OH 44325 USA.

I. Husain was with Electrical Engineering, University of Akron, Akron, OH44325 USA. He is now with North Carolina State University, Raleigh, NC27606 USA.

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TSG.2012.2221148

injected and reflected signals. This method is not applicable for

a short range or multilayer distribution network.

In [6], a method to locate the position of the FN in the low

voltage simple distribution network has been introduced. The

method is based on determination of the power line characteris-

tics by injecting the current signal into the grid to be measured

by smart meters. The main disadvantage of this method is the

requirement of powering down the system during the test, which

is not acceptable in residential networks.

In this study, a novel approach for detecting a FN, and the

procedure for localizing the position of the FN in a three phase

low voltage complex distribution network is presented. The ap-

proach is based on the information provided by the smart meters

to the central station, and capturing the network parameters after

the injection of the test signal into the network. The proposed

method can be applied to a multilayer complex distribution net-

work as opposed to other methods mentioned earlier. The lo-

calization of the FN can be accomplished quite accurately com-

pared to most of the available technologies. Section I provides

the introduction to the well-known procedures to detect and lo-

calize the FN; Section II provides an introduction to the smart

grid structure and the description of the hardware needed for the

new method; Section III describes the power network models at

high frequency based on low frequency measurements and highfrequency test signal injection; Section IV presents the proposed

method for detection and localization of the FN. The test results

for the example network topologies are presented in Section V;

Section VI gives the summary of the research.

II. SMART GRID STRUCTURE

In modern times, smart grid infrastructure can contribute to-

wards power network automation which would improve the ca-

pabilities of existing power networks significantly. The primary

focus is the improvements in the distribution network through

communication and penetration of renewable energy systems

[7], [8]. This modern power grid will integrate advanced infor-

mation techniques, communication techniques, computer sci-

ence and pre-existing physical grids [9]. In the smart grid infra-

structure, it is possible to show when, where and how the energy

is used through the on line bidirectional connectivity between

users and utilities. It is also possible to manage the loads in smart

grid networks to increase the energy delivery ef ficiency [10].

The block diagram of a smart grid network in the low voltage

side (LV) is shown in Fig. 1 where each individual smart meter

communicates with the central unit.

Two important elements in the smart grid network are the

smar t meter and the central unit. A smart meter is the data

gateway which is installed at each customer node. The meter

1949-3053/$31.00 © 2013 IEEE



1068 IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 2, JUNE 2013

Fig. 1. LV side smart grid topology.

measures the electricity usage information and sends it to the

intermediate central unit. The intermediate unit collects the

data from smart meters, and sends it to the utility central unit

[11], [12], [15].

The connectivity between customers and the central unit

could be power line carrier communication, ZigBee [13] or

GSM [14]. This research will show that it is possible to detect

and locate the FN in the distribution side before the meter.

The existence of the FN in other areas is also detectable by

the measurement devices. The central unit hardware needed to

perform the proposed method has an extra injection unit. Each

smart meter has a separate high frequency voltage measurement

unit to measure the received signal voltage.

A. Smart Meter

The proposed method requires two units for proper operation.

The first unit is a common smart meter that should measure the

data needed to calculate the impedance characteristics of each

node; this unit also has the ability to store data into external

memory and send it to the host via a physical layer. The meter

transducer should have wide bandwidth to measure the current

and voltage of the received signal. Another unit is the modem

which sends data to a specified node. The smart meter unit is

based on commercial energy meter chips [16], [17]; chips that

have the ability to measure most of the energy usage param-

eters. Each meter sends six data to the host unit to calculate

the impedance of the power line at low frequency: These are

active power (P), reactive power (Q), apparent power (S), rms

voltage , and rms current and line frequency (f).

The power usage of each node is computed and transferred to

the main unit.

The modem unit could be designed based on several com-

munication techniques such as power line carrier [18], [19] and

wireless communication methods.

B. Current Injection Unit

The current injection unit is composed of a programmable

reference signal generator, power amplifier to generate the com-

manded current into the network and high voltage coupler [20].

In this research, a constant amount of current is injected into

the network to determine the difference between the predicted

and measured power line impedance model. A programmable

current source is used for this purpose. The proposed current

injection unit is considered to be installed in the central unit.

The injected current signal at the main feeder imposes specific

voltages on individual nodes [21], [22].

The EN50065-1 standard specifies the maximum peak to

peak voltage which the central unit is allowed to impose on the

Fig. 2. EN50065-1 Standard for power line carrier communication.

Fig. 3. Test signal injection unit.

individual nodes. The maximum permissible peak-to-peak im-

posed voltage on each node is defined based on the frequency of

the injected current signal; this definition is shown graphically

in Fig. 2. Based on the desired frequency (A-Band) with respect

to the EN50065-1 standard, the maximum peak-to-peak im-

posed voltage is 120 dB which is equal to 1 V. A constant

value of the current is injected to maintain the limitation set by

the standard. An accurate power line impedance estimation is

needed to set the current value which is determined based onstatistical data or on-line impedance measurements. The block

diagram of the current injection unit is shown in Fig. 3.

III. POWER NETWORK MODEL

The steps forlocating the FN are based on thecapabilityof the

hardware as well as on a developed model of the power network.

The method relies on the load flow analysis of the power

system at PLC frequency which requires the high frequency

impedance of the loads. The high frequency load impedances

are either directly measured at PLC frequency or first measured

at power transmission frequency and then transformed intoPLC frequency by smart meters using load models. If the

second method is chosen for the implementation, the model of

individual loads helps to predict the high frequency impedance

using the low frequency impedance can be programmed into

the individual smart meters. After determining the load imped-

ances, the smart meters transmit the information to the central

unit periodically to be used in the high frequency load flow

analysis. Since each measurement period can be fixed, it is

assumed that the load impedances are constant during these

intervals. For better accuracy, the measurement periods can

be shortened by the smart meters. Other information needed

for this method is the network response characteristics to

the injected signal. These steps are explained in detail in the

following subsections.



PASDAR et al.: DETECTING AND LOCATING FAULTY NODES IN SMART GRIDS BASED ON HIGH FREQUENCY SIGNAL INJECTION 1069

Fig. 4. Smart grid impedance model in LV side.

A. Network Impedance Model at Low Frequency

One of the most important data needed for detecting and lo-

cating the place of the FN is the power line network impedance

model which consists of the node’s load impedance model and

each feeder segment’s impedance model.

Reactive as well as active power measured at each node de-

termine the basic version of the node load impedance model at

low frequency. The real and imaginary parts of the impedance

are determined separately. The characteristics of these two el-

ements are determined based on the apparent power as well as

active power which are measured by the smart meters.

The smart grid impedance model in the LV side is shown in

Fig. 4. There are number of nodes in the network. Here

is the feeder impedance in section is the cable impedance

which connects feeder to the customer, is the grid fre-

quency and is the load impedance which has resistive

and inductive components. The parameters

and are obtained from the energy consumption information

as shown in (2) and (3).

(1)

(2)

(3)

and are the feeder segments, , and are

load real power, apparent power and RMS current.

B. Network Impedance Model at High Frequency

Smart meters in smart grids measure the apparent power aswell as active power at the line frequency. The impedance mea-

sured of the power line frequency is converted to the current

injection level at high frequency. It is possible to construct the

high frequency impedance parameters based on the measured

low frequency impedance. The polynomial mapped transfer

function could be used for conversion. The transfer function

varies for each load and changes with the operating frequency.

The high frequency model definition is given as follows:

(4)

where is the complex impedance at the operating

frequency is the low frequency impedance and is

the polynomial mapped complex transfer function that converts

Fig. 5. Heater impedance magnitude.

Fig. 6. Heater impedance phase.

TABLE IPOWER LINE CHARACTERISTICS AT 90 KHz

complex low frequency impedance to . The

general form for the is shown in (5)

(5)

where and are the coef ficients of the degree poly-

nomial to represent . Polynomial coef ficients are defined

based on the impedance magnitude and phase variations of typ-

ical loads at home. As an example, the impedance magnitude

and phase variation of the residential heater are presented in

Figs. 5 and 6, respectively.

Although it is possible to determine the conversion ratio

for the load, it is more precise to measure the high frequency

impedance directly. The current and voltage transducers shouldhave wide bandwidth to measure the high frequency portion

of the current and the voltage signals. Based on the high fre-

quency voltage and current measurements the individual load

impedances would be determined. The measured or estimated

individual high frequency load impedances are communicated

to the central unit by the individual smart meters. The central

unit has predetermined network information which is basically

the number of nodes and impedance between the individual

nodes. The impedance between the nodes is based on the high

frequency impedance of the power cable among the nodes.

Table I shows the typical power line impedance characteristics

at 90 KHz. The central unit constructs new admittance matrix

every time it gathers high frequency load impedances from the

smart meter. In the network architecture, there are




Fig. 7. Power and information flow diagram.

number of nodes including the ground and central unit nodes.

The ground is considered as node number 0. The loads are

treated as an impedance between the meter location and the

ground. The admittance between two nodes (node i and k) can

be found as:

(6)

where and are the real and imaginary parts of the

impedance between the nodes. The admittance matrix in the

network can be constructed as:

(7)

array has the individual nodes voltages and

array represents the source currents into the

individual nodes. The diagonal element of each node, known as

the self-admittance, is the sum of all the admittances connected

to it as given by:

(8)

The off-diagonal element, known as the mutual admittance is

equal to the negative of the admittance between the nodes. This

element is given by:

(9)

For a given high frequency injected current, load flow anal-

ysis has been carried out with the constructed impedance matrix

using theNewton Raphson method [23] tofind thevoltage of the

individual nodes. In the high frequency domain, the only source

that injects the signal is the programmable current source; there-

fore, all the node currents are zero except the first node current

. The individual node voltages are recorded in the array

for the fault diagnosis presented in Section IV.

C. Response of the Network to High Frequency Test Signal

A high frequency test signal can be injected to the starting point of the feeder to determine the real power line network

characteristics. The FN is also considered since the test is con-

ducted on the real network. The test signal is considered as a

programmable current source. With respect to the EN50065-1

standard, the amount of current is limited due to the injected

signal peak-to-peak voltage limitation at high frequency. Two

different methods for adjusting the current magnitude are pro-

posed. The statistical data showing the average of the total net-

work impedance from the base station side for a reasonable time

period could be used for setting the current. An alternative ap-

proach is the adaptive current adjustment. In this approach, the

high frequency voltage is monitored continuously. The ampli-

tude of the injected current is controlled based on measured volt-

ages of the individual nodes. In the second method, although

the control is more optimum than the first one, the controller

requires more processing time. The high frequency signal mea-

surement unit is used to determine the characteristics of the re-

ceived signal. The unit consists of the high voltage coupler, band

pass filter and the high frequency RMS voltmeter. The high

voltage coupler is used to separate the high frequency test signal

from the high voltage power line. The structure of the coupler

is based on LC band stop filter which has a high impedance

within the range of the test signal. To mitigate the noise effects

on the received signal a sharp band pass filter is used to pass

only the test signal which has the frequency within the range of the EN50065-1 standard. The communication between devices

can be based on power line carrier or wireless communication.

Power and informationflow diagram based on power line carrier

communication method is shown in Fig. 7, where s repre-

sent the smart meters at the specified nodes.

IV. FAULT DIAGNOSIS

The array of the node voltages based on simulation of a pre-

defined model with respect to test signal injection is . The

array of the measured high frequency received signal voltage at

the nodes due to the injection of the test signal in the real world

is . The detection unit runs the specific procedure at each

iteration based on the two input matrices to detect the existence




Fig. 8. 3 layer power line network model with 22 smart meters.

of the FN on the network. The difference between the measured

and estimated voltage arrays is given as:

(10)

In the absence of a FN, will be null. is used to show

the changes in the predefined network impedance model. Each

element in the array is compared with the threshold value. This

procedure can be summarized as:

(11)

where is the set of elements in vector , and is the

threshold voltage.

An anomalous increase in matrix elements shows the area

where a FN is present. The peak value of elements inside the

faulty node area (FNA) shows the FN which is the closest node

to the fault location in the network. The definition of the FN is

given as:

(12)

The method allows the detection of the existence of the FN as

well as the determination of the FN location. The flow diagram

for the detection and localization method is given in Fig. 9.

V. CASE STUDIES

Different case studies are conducted to verify the validity of

this novel method in experimental and simulated networks. In

Section V-A, three different scenarios are simulated in the pro-

posed three layer network to examine the validity and the per-

formance of the concept. In Section V-B, the proposed method

is tested in the real network with eight nodes. The effects of in-

accurate measurements are also presented in simulated and ex-

perimental networks.

A. Network Simulation

A three layer network has been selected as the example net-

work. The studied three layer network is shown in Fig. 8. This

Fig. 9. Detecting and localizing the FN procedure flow diagram.

model includes the most possible complicated topology for the

low voltage power line network. All of the simulations have

been performed on this model. In the first case, there is no FN

in the network. In the second and third cases, one FN is located

at different locations for each one of the cases. In the simulated

three layer network, there are 45 nodes (1,2, 45), and only

22 of them (24,25, 45) have a smart meter (sm24, sm25,

sm45). The loads are selected based on the typical home

load pattern. Inductive and resistive loads are both considered

in this case study. In the model, each feeder and cable segments

are modeled as a shunt medium transmission line. For modeling

the feeder segments, the characteristics of the cable are obtained




Fig. 10. for each node where FN is close to node 40.

from Table I based on the 25 , cable model. The

feeder and cable are modeled as:

(13)

where is the feeder segment impedance, is the cable

impedance which connects the feeder to the load, and

is the frequency of the injected signal. The feeder segment re-

sistance is taken to be 100 and the feeder segment

inductance is taken to be 16 . Since the length of the

cable which connects the feeder to each load is very short, it ismodeled with just a resistive element . has a

range of (3 to 10 ) and has a range of (0 to 20 mH). The

test signal frequency is selected as 90 KHz.

Three different scenarios are considered for method valida-

tion. In the first one, the network is running in the normal mode;

is about zero where there is no illegal connection or FN

in the network.

In the second situation, a FN is considered close to node 40

with a 2 resistive load connection. Due to the absence of a

meter at node 20, the vector has a peak in node 40 which

is the closest node to the FN.

For the third scenario, the FN is considered to be near node16. The impedance of the FN is the same as the second scenario.

As shown in Fig. 11, has the largest value near node 34.

Since node 34 is the closest smart meter near the FN, we can

conclude that the algorithm accurately detects the FN in the net-

work. It can also be concluded that the FNA is depended on the

value of the threshold voltage . The elements in the

vector which are greater than the are considered as a FNA.

The should be selected so that the FNA could be identified

with a reasonable accuracy. Selecting as gives

accurate identification of the FN in the third scenario.

Many factors have an effect on the accuracy of the FN loca-

tion estimation. The dominant factors that lead to miss identifi-

cation of the FN are the truncation effects due to the analog-to-

digital converter resolution, the inaccurate model of the power


Fig. 12. Analog to digital converter resolution versus FNDR.

line impedance matrix, and the nonlinearity of the load estima-

tion model. It is expected that the magnitude of the at the

FN is much larger than the in any other node in the net-

work. The faulty node detection ratio given in (14),

is defined as the ratio of the magnitude at FN to the next

largest value at any node in the network. The isa good measurement for the quality of the FN detection.

(14)

The analysis of the effect of the analog-to-digital converter

resolution on the is presented in Fig. 12.

should be grater than 1 for the possibility of the FN detection;

the quality of the detection increases as increases.

The high frequency network model is based on node power

measurements and linear load conversion from low to high

frequencies. Tolerance of power measurements causes inaccu-

racy in network model estimation. The tolerance is modeled

by injecting a random error to the final network model before

performing the power flow analysis. Relation between the




Fig. 13. Measurement tolerance versus total error.

Fig. 14. Implemented experimental network.

tolerance percentage versus total error is shown in Fig. 13. Thetotal error means the number of miss identification of the FN

when the location of FN is changing from the first node to the

last.

B. Experimental Network

A one-layer eight-node network has been implemented in the

laboratory to analyse the performance of the proposed method

in a real system. Higher load impedance compared to the low

impedance characteristics of the feeder segments increases the

complexity of the FN detection procedure. In the experimental

network, the impedance of the nodes have been selected in the

range of 40 to 80 to reflect the worst case scenarios. Theimplemented network is shown in Fig. 14.

The injection unit has to inject a current into the utility net-

work. Each node in the network has a decoupling network to

measure the injected signal effect on that node. The coupling/de-

coupling circuit built in thelab is shown in Fig. 15. Coupling/de-

coupling circuits are made of a high pass filter that blocks the

60 Hz signal. Circuits protect the low voltage current injection

and voltage measurment circuitry from higher power levels in

the network.

The EN50065-1 standard has been followed for current injec-

tion into the power network during the laboratory experiments.

The amplitude of the current injected into the feeder is 142 mA.Amplitude of the injected current has been set based on max-

imum allowed peak-to-peak imposed voltage on each one of

the nodes which is 1 V [21]. The voltage and the current of each

node were used to compute the high frequency load impedances.

Characteristics of the feeder segments are defined based on the

length of each segment. The injected signal waveform is shown

in Fig. 16.

The test has been conducted with the FN connected to node

5. The voltage and current information for each node have been

collected based on the 142 mA current injection. Highfrequency

impedances for each node along with the predetermined cable

impedance are combined to form the high frequency impedance

network. The high frequency load flow analysis is performed

for the injected current. The measured and the estimated node

Fig. 15. Coupler/decoupler circuit.

Fig. 16. Injected test signal waveform.

TABLE IISIMULATED AND MEASURED NODES VOLTAGE WITHOUT AND WITH FN

voltages from the load flow analysis are compared to calcu-

late . Table II shows the measured and estimated node

voltages.

The voltage matrix elements are used in the FN detection

algorithm. Based on the developed method as it is shown in

Fig. 17, the FN has been identified to be close to where the fault

occurred. The absolute value of peaks at node 4 which is

very close to the FN (node 5).

The measurement accuracy can affect the performance of the

detection procedure. Measurement error has been added to the

measured data to show the sensitivity of the performance with

respect to measurement accuracy. The error tolerance is in the

range of 0 to 20% of the total measurement. In Fig. 18, the min-

imum voltage difference of FN voltage compared to those of





Fig. 18. Minimum voltage deference of FN voltage to other node voltagesversus measurement error tolerance.

other node voltages are presented with respect to the measure-

ment error tolerance.

In order to have a suf ficient voltage difference for detecting

the FN, the measurements and the low to high frequency con-

version matrices should have a good accuracy. High error tol-

erance in the measurement or the conversion matrices makesinaccurate detection for FN.

VI. CONCLUSION

In this paper, a novel method for detecting and localizing a

FN in lowvoltage power line networks is presented. The method

can be run in a live network withoutany interference. Theproce-

dure is based on the power line network characteristics and the

response of the network to a high frequency test signal. Simula-

tions based on three different scenarios have been implemented

to demonstrate the validity of the method. The truncation ef-

fects of analog-to-digital conversion on localizing the FN have

been identified; the dependence of the method on the resolution

of smart meters are simulated. The method has been validated

with an experimental network developed in the laboratory. Ex-

perimental results showed that the proposed method effectively

detects and localizes the FN.

R EFERENCES

[1] M. T. Hagh, K. Razi, and H. Taghizadeh, “Fault classifi

cation and lo-cation of power transmission lines using artificial neural network,” in Proc. 200 7 I EEE Int. C onf. Power Eng., pp. 1109–1114.

[2] M. Gilany, E. S. T. El Din, M. M. Abdel Aziz, and D. K. Ibrahim,“An accurate scheme for fault location in combined overhead line withunderground power cable,” in Proc. 2005 IEEE Power Eng. Soc. Gen.

Meet., pp. 2521–2527.[3] H. Zhang, Z. Tian, and E. Zhang, “An improved algorithm for fault

location in distribution network,” in Proc. 2008 IEEE Int. Conf. Con-

dition Monitoring and Diagnosis, pp. 727–730.[4] I. H. Cavdar, “A solution to remote detection of illegal electricity usage

via power line communications,” IEEE Trans. Power Del., vol. 19, no.4, pp. 1663–1667, Oct. 2004.

[5] M. da Silva, D. V. Coury, M. Oleskovicz, and E. C. Segatto, “Com- bined solution for fault location in three-terminal lines based onwavelet transforms,” IEEE Trans. Gener., Transm., Distrib., vol. 4,no. 1, pp. 94–103, Jan. 2010.

[6] A. Pasdar and S. Mirzakuchaki, “A solution to remote detecting of il-legal electricity usage based on smart metering,” in Proc. 2007 IEEE

Int. Workshop Soft Comput. Appl., pp. 163–167.[7] C. Cecati, C. Citro, and P. Siano, “Combined operations of renewable

energy systems and responsive demand in a smart grid,” IEEE Trans.

Sustainable Energy, vol. 2, no. 4, pp. 468–476, Oct. 2011.[8] K. Tanaka, K. Uchida, K. Ogimi, T. Goya, A. Yona, T. Senjy, T. Fun-

abashi, and K. Chul-Hwan, “Optimal operation by controllable loads based on smart grid topology considering insolation forecasted error,” IEEE Trans. Smart Grid , vol. 2, no. 3, pp. 438–444, Sep. 2011.

[9] Z. Ruihua, D. Yumei, and Y. Liu, “New challenges to power system planning and operation of smart grid development in China,” in Proc.

2010 Int. Conf. Power Syst. Technol., pp. 1–8.[10] M. A. S. Masoum, P. S. Moses, and S. Deilami, “Load management in

smart grids considering harmonic distortion and transformer derating,”in Proc. 2010 IEEE Innov. Smart Grid Technol., pp. 1–7.

[11] S. Soergel, “An economic smart metering pilot implementation usingstandards-based protocols,” in Proc. 2010 IEEE Int. Conf. Innov.Technol. Ef ficient Reliable Electr. Supply, pp. 216–219.

[12] F. M. Cleveland, “Cyber security issues for advanced metering infra-structure (AMI),” in Proc. 2008 IEEE Power Energy Soc. Gen. Meet., pp. 1–5.

[13] C. Tatsiopoulosand A. Ktena, “A smart ZIGBEE based wireless sensor meter system,” in Proc. 2009 IEEE Int. Conf. Syst., Signals, Image

Process., pp. 1–4.[14] C. Hochgraf, R. Tripathi, andS. Herzberg,“Smart grid charger forelec-

tric vehicles using existing cellular networks and SMS text messages,”in Proc. 2010 IEEE Int. Conf. Smart Grid Commun., pp. 167–172.

[15] M. Venables, “Smart meters make smart consumers [Analysis],” IEEE

Trans. Eng. Technol., vol. 2, no. 4, p. 23, Apr. 2007.[16] J. Jiang and L. Yu, “Design of a new three-phase multi-rate watt-hour

meter based on singlechip,” in Proc. 2009 IEEE Int. Conf. Comput. Intell. Softw. Eng., pp. 1–4.

[17] H. Ju-Yong and Y. Hu-Mei, “Design of GPRS netwok watt-hour meter based on ADE7758,” IEEE Trans. Microcomput. Inf., vol. 23, pp.166–167, Jan. 2007.

[18] L. Dong and Z. BaoHuz, “Design and emulation of high-speed narrow- band PLC system for Smart meter reading,” in Proc. 2010 IEEE Power

Energy Eng. Conf., pp. 1–4.[19] A. Pasdar and S. Mirzakuchaki, “Three phase power line balancing

based on smart energy meters,” in Proc. 2009 IEEE EUROCON , pp.1876–1878.

[20] T. D. Rybel, A. Singh, P. Pak, and J. R. Marti, “Online signal injec-tion through a bus-referenced current transformer,” IEEE Trans. Power

Del., vol. 25, no. 1, pp. 27–34, Jan. 2010.[21] Signalling on Low-Voltage Electrical Installations in the Frequency

Range 3 kHz to 14 8.5 kHz , CENELEC Standard EN 50065-1, 1991.[22] F. J. Cortes, E. M. Rubio, and A. Valdovinos, “Embedded powerline

DSP modem for domotic SNMP networking in European countries,”

IEEE Trans. Consum. Electron., vol. 48,no. 4, pp.854–862, Nov. 2002.[23] H. Saadat , Power System Analysis, 2nd ed. New York: McGraw-Hill,

pp. 228–233.




Amir Mehdi Pasdar (S’10) received the B.Sc. de-gree in electrical engineering from the Universityof Mazandaran, Iran, in 2004 and the M.Sc. degreein electrical engineering from Iran University of Science and Technology in 2007. He is currentlyworking toward the Ph.D. degree in electrical en-gineering at the University of Akron, Akron, OH.

He was a Technical Manager of ABB in Iran.His main research interests include real time highfrequency impedance modeling of the power net-work, smart grid sensor design, and next genera-

tion smart grid applications.

Yilmaz Sozer (M’05) received his B.S. degree inelectrical engineering from the Middle East Tech-nical University Ankara, Turkey, and his M.S. andPh.D. degrees in electric power engineering fromRensselaer Polytechnic Institute, Troy, NY.

He has worked at Advanced Energy Conversion,Schenectady, NY, and developed expertise in allaspects of electronic power conversion and itscontrol. He joined the faculty of Electrical andComputer Engineering Department at the University

of Akron, Akron, OH, in August 2009, where he isdeveloping a research and teaching program on alternative energy systems.His research interests are in the areas of control and modeling of electricaldrives, alternative energy systems, design of electric machines, large industrial

static power conversion systems that interface energy storage, and distributedgeneration sources with the electric utility and smart grid.

Dr. Sozer has been involved in IEEE activities which support power elec-tronics, electric machines and alternative energy systems. He is serving as anAssociate Editor for the IEEE TRANSACTIONS ON I NDUSTRY APPLICATIONS So-ciety (IAS) Electrical Machine Committee and secretary for the IEEE IAS Sus-tainable and Renewable Energy Systems Committee.

Iqbal Husain (S’89–M’89–SM’99–F’09) receivedthe B.Sc. degree from Bangladesh University of En-gineering and Technology, Bangladesh, and the M.S.and Ph.D. degrees from Texas A&M University,College Station.

He is currently the ABB Distinguished Professor in the department of Electrical & Computer Engi-neering at North Carolina State University, Raleigh,engaged in teaching and research. He is the Co-Di-rector of the Advanced Transportation Energy Center (ATEC), and a faculty member at the NSF FREEDM

Engineering Research Center at NC State. His research interests are in the areasof control and modeling of electrical drives, design of electric machines, devel-opment of power conditioning circuits, and design and modeling of electric andhybrid vehicle systems.

Dr. Husain received the 2006 SAE Vincent Bendix Automotive ElectronicsEngineering Award, and the 2000 IEEE Third Millennium Medal and the 1998IEEE-IAS Outstanding Young Member award.

detecting and locating faulty nodes in smart grids.pdf

Documents