PESGM2011 Feeder Communication

7/31/2019 PESGM2011 Feeder Communication

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Wireless Communication for Smart Grid Applicationsat Distribution Level Feasibility and Requirements

Visvakumar Aravinthan,Member IEEE, Babak Karimi, Student Member IEEE, Vinod Namboodiri, Senior Member

IEEE, and Ward Jewell,Fellow IEEE

Abstract Smart grid technology places greater demands for

reliability on communications infrastructure. This work focuses

on identifying requirements for distribution feeder level

communications. Due to the large number of distribution

components connected to the distribution level feeders, a

massively deployed wireless communication network is identified

as the potential technology for this application. This network

would allow prioritized communication: high priority for

abnormal events and system control operations, and low priority

communication for asset management tasks. A three-layer

wireless communication architecture is proposed in this work to

increase the reliability and reduce the latency of event

notification. Fault location is considered as an exampleapplication to illustrate the proposed architecture.

I. INTRODUCTIONReliable communications at the distribution level has high

importance for the smart grid. There has already been

significant work done on power system communication needs

and applications. IEC 61850 and DNP 3 standardize the

communication within the substation. ANSI C12.22

networking standards apply to advanced metering

infrastructure [1]. Even though 80% of consumer interruptions

are attributed to distribution component failures at the feeder

level, getting reliable information is currently a challenging

task. Because of this, only limited monitoring is done oncomponents in the distribution system and the associated

communication infrastructure. Due to these difficulties,

distribution failure/abnormality analysis is done by harvesting

information from the components at substation level. There

has been a significant amount of work to analyze such data

[2]-[5], but even analyzing the whole feeder using information

from substations will not capture all necessary information.

Accurate prediction and location of distribution failures are

still in an early stage of development. If the communication

infrastructure is improved a more reliable approach could be

taken and this would also aid better asset management

strategies. The motivation for this paper is to identify the

needs for communication at different levels in the distribution

system, the substation level, feeder level, distributed source

level, and consumer level, with more emphasis on the feeder

level. In addition to the asset and outage management tasks,

communications will also help improve energy management

and tariff related tasks.This work considers wireless communication as a medium

for feeder level communication. Leon, et al, have proposed a

two-layer wireless sensor network for transmission towers,

mainly to reduce the cost of operation while overcoming the

limitations of wireless communication range [6]. Muthukumar,

et al, proposed a wireless sensor network for distribution level

automation [7]. Motivated from these, this work identifies the

requirements for communication for distribution feeders and

explores the feasibility of wireless communication. Specific

contributions of this work include:

i. Explain the selection of a wireless medium forcommunication at the feeder level.

ii. Identify the specific requirements for wirelesscommunication at the feeder level including metrics for

reliability.

iii. Compare and contrast various wireless technologies andidentify a feasible subset for the envisioned architecture.

iv. Describe how the chosen wireless technologies cometogether in a three-layer communication architecture.

II. COMMUNICATION REQUIREMENTS AND DESIGN FORDISTRIBUTION

Traditional system control and data acquisition (SCADA)

level communication has limited bandwidth, 75 bits/s to 2400

bits/s [8]. Greater bandwidth is necessary if the information

from the components is going to be used not only formonitoring (abnormality detection), but also for control and

asset management tasks. Intra-substation communication is

moving from binary or analog communication to Ethernet and

TCP/IP based wide area network. IEC 61850 standardizes the

communication network within a substation [9], [10]. IEC

61850 could be extended to distributed sources. Smart meter

technologies are capable of using TCP/IP based

communication to/from the control center. The emerging

This work was supported in part by the Power Systems Engineering Research

Center (PSerc). Project No: T-39, Communication Requirements and

Integration Options for Smart Grid Deployment, and by the US Department of

Energy Project DE-FG36-08GO88149, Sustainable Energy Solutions.

V. Aravinthan is with the Department of Electrical and ComputerEngineering, Clemson University, Clemson, SC, 29634 USA (e-mail:[email protected]).

B. Karimi is with the Department of Electrical Engineering and ComputerScience, Wichita State University, Wichita, KS 67260 USA (e-mail: [email protected])

V. Namboodiri is with the Department of Electrical Engineering andComputer Science, Wichita State University, Wichita, KS 67260 USA (e-mail:

vinod.namboodiri @wichita.edu).W. Jewell is with the Department of Electrical Engineering and Computer

Science, Wichita State University, Wichita, KS 67260 USA (e-mail:[email protected]).


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consumer communication needs. One of the advantages of this

medium is that the utility has to bear only the terminal

equipment cost and costs associated with leasing the line. This

will reduce the overhead for the utility while improving

communications. On the other hand the utility will not have

control over the medium as it will not own the dedicated wired

medium in most cases. This will require physical connections

and will reduce the flexibility. Further when a pole goes down,

the communication link will be broken and may result in poor

performance.

Wireless communication is another promising alternative

for distribution level communication. One of the important

characteristics of wireless communication is the feasibility of

communication without a physical connection between two

nodes. This would ensure the continued communication even

with a few poles down. In other words redundant paths for

communication are possible without additional cost.

According to Huertas et al, discharges between the line

components which arise in power lines under 70 kV and the

corona effect which arise in power lines over 110 kV have the

dominating frequency spectrum in the range of 10 30 MHz[17]. Selection of medium with communication frequency

spectrum above these limits would minimize the interference.

Wireless Fidelity (WiFi / IEEE Standard 802.11), ZigBee

(IEEE Standard 802.15.4) or Worldwide Interoperability for

Microwave Access (WiMAX / IEEE Standard 802.16) could

be utilized in the distribution system with minimal

interference.

Another advantage of using wireless communication is that

the utility has to own only the terminal units, which are

relatively cheap and could be integrated with cost effective

local processors. When multi-hopping is used in wireless

communication, especially in WiFi and ZigBee, the range of

communication can be extended and the nodes located in thefeeder could be able to communicate with the control center.

Disadvantages of wireless communication would be

interference in the presence of buildings and trees which could

result in multi-path; this can be avoided with improved

receivers and directional antennas, which will increase the

cost. Another major concern with wireless medium is easy

accessibility, which could result in security issues. This can be

avoided by using secure protocols. Rural feeder sections

would be long and range of communication could become a

concern; however, directional antennas could mitigate this

issue.

Both PLC and wireless communication are promising in

the distribution level communication. Based on the need and

the availability of the technology a combination of both could

be used for improved communication infrastructure.

B.Feeder Level RequirementsThe expected communication need for the feeder level is

shown in Figure 2.

At the smart meter level, a consumer will have three types

of appliances, the ones which will not be controlled by the

smart meter, e.g.: lights, cooker, etc, the ones which can be

controlled by the smart meter, e.g.: washer, dryer, air

condition, etc and the ones which needs to be controlled by the

utility through the smart meter, eg electric vehicles (EV). This

work suggests three different types of communication for

these three types.

Figure 2: Communication Requirement at Feeder Level

The ones which need not be controlled by the smart meter

needs only unidirectional communication, whereas the other

two will need bidirectional communication. In order to

minimize the cost the ones which may not be controlled could

use PLC as the medium for communication. Due to the large

amount of data and the possibility to communicate with the

smart meter from different locations EVs needs to have

wireless capabilities and should be able to have a secured

connection to the smart meter via Internet. It should be noted

that for controlled charging applications, EVs may have to

communicate with the utility while on road and from locations

where wired communication is not possible.

To minimize the cost, overcome the restriction of range

issue of sensor nodes, and increase the redundancy, a three

layer communication model, shown in Figure 3 is proposed forfeeder level communication. This no new wires technology

is developed to ensure easy installation on the existing system.

Figure 3: Three-Layer Communication for Distribution Feeder

Wireless sensor nodes with minimum processing capacity

and with short transmission range are installed on the towers

of the distribution system. Functionality of both transmitter

and receiver are combined in these wireless sensor nodes.

Since these sensors need to be deployed massively, the

cheaper wireless sensor nodes are more economical. Each

tower with the group of sensors is termed as the cluster. Each

cluster is equipped with a processing unit with long

transmission range, which is known as the master node. Layer-

1 communication, showed as Cluster in Figure 3, occurs

between the wireless sensor nodes and master node. Readings

from these sensor nodes are communicated in this step.


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The master node continuously monitors data from all

wireless sensor nodes belonging to its cluster. In case of an

abnormality observed by a master node, communication

among clusters will be initiated. Observed abnormality will be

shared among the clusters and the abnormality and location is

identified by processing the cumulative information. This

process is expected to reduce the false alarm and missed

detections. This will be known as Layer-2 communication as

shown in Figure 3, and will occur among master nodes of

adjacent clusters.

Layer-3 communication in Figure 3 is between a master

node which saw the abnormality and the control center. WiFi

is used in this layer due to its cost effectiveness. To overcome

range constraint of WiFi, Layer-3 communicates detected

abnormal event from the master node which saw the

abnormality first to the control center. Due to longer distance,

multi-hop approach is utilized to transmit data as shown in

Figure 3.

In the process of assuring that the necessary information is

not a false alarm, it could communicate with neighboring

master nodes which would be the second stage ofcommunication. It should be noted that any information loss

in a cluster would result in missing an event and when the

master node is communicating with the control center, it

should ensure that any abnormality in the system should reach

the control center, requiring redundant paths for higher

reliability. When the master node tries to send asset

management information, it could wait and ensure that reliable

communication is possible before sending the information.

C.ReliabilityAvailability of communications infrastructure will have a

direct effect on smart grid and power system performance. The

electric power system is a reliable system already, so any

communications system supporting it must have a similar high

reliability. If the availability of the communication system is

lower than the availability of the power system, then the

communication infrastructure may not serve its purpose or

even be worth the investment. Therefore a way to measure the

performance of the communication system and benchmarking

it is very important. Communication requirements for smart

meter applications should be higher than that of the feeder

level. Household appliances will depend on this information

received by the smart meter for the operation and any error

could cause unexpected outcomes. Following reliability

standards for distribution level communication is derived

based on the power system and communication reliabilitystandards.

Active Communication should be available both during the

sustained interruption (any interruption longer than five

minutes) and momentary interruptions. Since sustained

interruptions for the smart meter directly involves the

consumer satisfaction, sustained interruption indices for

distribution communication similar to sustained interruption

indices for distribution reliability (IEEE std. 1366-2003) [18].

Average Communication Failure Frequency Index (ACFFI)

A common metric to measure the feasibility that wireless

communication can be done on a link between two points is

based on signal-to-noise ratio (SNR). If the SNR at the

receiver is below the threshold then that information could not

be accessed by the smart meter. Each set of information sent

to the smart meter needs to be received above the required

SNR value. Therefore as a tool to ensure reliable

communication, the number of times the SNR was less than

the required as an average over all the components could be

used. It should be noted that not all piece of equipments in a

household will have same level of importance. For example

information from a dryer is more important to the smart meter

than an electric bulb. Therefore this work proposes a

predefined weighed index to measure the reliability. This is

defined as;

Total weighted number of missed eventsWeighted number of appliancesMissed events include all the missed detections and false

alarms of both the smart meter and all appliances. Thefollowing equation could be used to calculate this index,

where, is the predefined weight for the appliance type , is the number of times a communication (packet) is missed or

miss detected for appliance type was less than the thresholdand is the number of appliances in a household of type .

Consumers will be responsible for reliable communication

and may be penalized for a less reliable smart meter system.

The communication protocol must ensure the priority among

different appliances.

Average Communication Interruption Duration Index (ACIDI)

If continuous communication is necessary, for example

each node in a cluster at a feeder level communication would

be continuously sending data to the master node and master

node will process the data to detect any abnormality.

Therefore rather than the number of times the SNR is poor, the

length of time the SNR is poor has more significance. In this

case all the nodes connected to the master node will have same

priority. Therefore with the assumption each cluster operates

independently, the reliability index for the cluster withrespect to the duration of poor SNR would be defined as,

Duration of communication failure for

each node due defect in cluster Total number of nodes in a cluster Therefore the average interruption duration would be,

Energy Not Served Due to Communication Failure (ENS-C)

One purpose of the smart grid communicationsinfrastructure is to increase distribution reliability, and thusincrease the total energy provided by the system in a time


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period. Measure of energy not served due to communicationssystem failure is thus proposed:

Energy Not Served whileCommunication Failure

Total Energy Not Served

This ENS-C index provides another measure of the

performance of the smart grid, and any return on investment it

will provide.

III. WIRELESS COMMUNICATION ARCHITECTUREA. Essential Features

In our envisioned wireless communication architecture, the

following features are essential for end to end communication:

Security: As the network is for a large nationwide electric

grid, protecting the connectivity and its data confidentiality is

critical. Design choices in developed protocols should include

aspects for secure communication.

Low latency: As grid functionality can be critical, having fast

and real time reaction upon an abnormal event is vital. The

wireless architecture should strive for high priority, low

latency alerts upon abnormality.

Fixed stations (and few mobiles): In a smart grid almost all

the nodes are fixed. Therefore the communication architecture

does not have to factor in node mobility explicitly. However,

the ability to support mobility for some nodes would be

helpful for situations like a maintenance vehicle trying to

connect the network through the grid.

Low Overhead: Excessive use of control packets and multiple

nodes trying to send same information constitutes high

overhead. This reduces available bandwidth for data traffic,

and can also result in higher latencies for critical alert packets.

Therefore it is necessary to ensure that overhead is kept low.

Scalability: The network of wireless nodes is expected to bequite large considering the scale of towers from the sub-station

to residential units. Communication architecture must work

equally well for a small network, as well as a large network.

B. Comparison between wireless technologiesAmong the choices of wireless technologies WiFi (peak

bandwidth 54 Mbps), WiMAX (peak bandwidth 100 Mbps),

and Cellular data service (peak bandwidth 10 Mbps) are

compared in Table1. Based on Table1, WiFi Mesh seems to

be the superior technology for smart grid applications at feeder

level and WiMAX could be used as a gateway for long-haul

communication based upon availability and cost

considerations. Taking into account above considerations, afinal architecture could be as shown in Figure 4. For WiFi

unlicensed frequency bands are preferred due to cost benefits,

and additional robustness that could be possibly by leveraging

community WiFi networks1.

1 We will consider the security and interference implications of this choicein future work and balance this against cost benefits.

TABLE 1:COMPARISON OF POSSIBLE WIRELESS TECHNOLOGIES

Customer Need WiMAX Wi-Fi Mesh GSM/UMTS

Cost High Medium High

RangeRural 4 km

Urban 500-900m200 400 m 1-2 mile

Max. Data Rate 70 Mbps 54 Mbps 20-800 kbps

Frequency Band2-11 GHz and

10-66 GHz

2.4 GHz and

5 GHz

700 MHz

2.1 GHz

Band LicenseFree andLicensed

Free Licensed

Flexibility High Medium Medium

Robustness Medium High Low

Figure 4: Layer Three Communication Mode

Among the unlicensed bands, we chose the 2.4GHz rangedue its greater communication range when compared to higherfrequency bands.

C. Communication ProtocolsWhen first layer of feeder level communication (refer Fig.

3) is considered, it is normally limited within a single tower.

Since range is small single-hop ZigBee, IEEE std. 802.15.4, at

2.4 GHz frequency is ideal. Master node should be placed

such that all nodes are within single-hop reach of master node.Second layer of communication requires information being

shared among all neighboring nodes; while ensuring that the

information is shared among all the nodes concerned. Multi-

hop architecture will ensure that the necessary redundancy is

available upon the failure of a master node. A typical range

using 2.4 GHz with a stock antenna is 300 feet outdoors,

which fits the distance between the distribution poles.

For third layer of communication, messages from selected

master node to the control center needs to be routed while

satisfying mentioned Smart Grid requirements.

Usually routing in wireless networks can be done in two

modes; Infrastructure mode and Ad hoc mode. In Smart Gridarchitecture there is no central node for controlling

communication between nodes. The ad-hoc mode of operation

of WiFi can be used in the formation of a mesh network

among all nodes. Three major types of routing algorithms

were considered for such wireless networks; reactive (on-

demand), proactive (table-driven) and geographical. Main

routing algorithms are compared in Table 2.


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Based on studies and comparisons among routing

protocols; there are several challenges that should be if lowcost wireless networks is to be used. Since the nodes are fixed

in the smart grid application, regular exchange of routing

tables is not required. And also for geographical based routingalgorithm a Global Positioning System (GPS) or similar

locating systems is not necessary.

TABLE 2:

RELATIVE COMPARISON OF AD HOC ROUTING ALGORITHMSOn-Demand

(AODV2)

Table-Driven

(DSDV3)

Geographical

(LAR4)

Routing Criteria

Freshness and

route

constructionspeed

Immediateroute

availability

Route speed

RoutingOverhead

High Almost high Less

Storage

OverheadLess Medium Less

Scalability Yes Yes No

Directional

Sending SupportYes No No

Increasing transmit power of an antenna of a node would

be possible in the event of failure of any node along a route.Typically we envision using directional antennas for nodes to

allow maximum range, and reduced interference among nodes

and with other wireless networks in the vicinity whencompared to Omni-directional antennas.

IV. REQUIREMENTS FORROUTING PROTOCOLS IN THESMARTGRID

The envision of using collection of cost effective, fixed

wireless nodes dynamically forming an ad hoc network is

evaluated. In ad hoc networks nodes can be connected

dynamically in an arbitrary manner, to increase the

transmission range of low range. To communicate with nodes

outside its transmission range, a host needs to enlist the aid ofthe nodes/gateways in forwarding packets to the destination.

AODV is chosen in this work because it is well researched and

performs better than the others with significant differences.

Shah et. al. show these performance differentials that are

analyzed using varying network load, scenario, and network

size [23]. Also varying protocol parameters is easier and more

flexible as we will talk about those changes in protocol later.

Simulation Environment: Simulations are carried out in the

open-source network simulator-2 (NS ver-2.31) [24] that

allows abstraction of all communication protocols and their

performance evaluation for different network topologies and

configuration of various network traffic types.Traffic Model: Continuous bit rate (CBR) traffic sources are

used. The source-destination pairs are the first and last nodes

of the network. Two data rates (0.5 Mbps, and 0.01Mbps) for

data packets are used. It should be noted that the data rate for

smart grid communication could be in the order of few

2 Ad Hoc On-Demand Distance Vector Routing3 Destination-Sequenced Distance-Vector4 Location-Aided Routing

hundred kbps. The measurement data (e.g. synchrophasor

measurements) may send continuous data but packet size

would be small. Any assent management data which has

moderate packet size would not be continuous and such

transmission could be delayed.

Simulation Scenarios: Long distribution line simulation:

Nodes are placed using radial topology on a 10 Km long

scenario as shown in figure 5. The first node (Node_ (0)) is thecontrol center which is located in the most left part of the

topology. Last node (Node_ (n)) is assumed to be the node

from which control center is requesting the information (for

example a smart meter). Performance of the message delivery

is analyzed in this work. For all the simulations fixed antenna

model is used. Transmit power is set to be fixed providing a

range of 100 meters consistent with practical values for WiFi.

Figure 5: Simulation Scenario Schematic

Performance Measures: The following are used as

performance measures for the routing protocol.

Packet Delivery Fraction:Packet delivery fraction is the ratio

of packets delivered to that generated by the traffic generator.

Average End-to-End Packet Delay: The average end to end

delay is the average of delays of each successfully received

packet from the source node to destination.

Node Density: This term is used to describe the number of

nodes within a certain geographic length.

A. Simulation ResultsSince distribution feeders are much longer than 100 meters,

a long chain of radial nodes are required. Simulation results

showed that although the results are satisfying in small chains

(two to ten nodes scenarios), greater increase in the number ofnodes results in a decrease in performance.

Motivated from this simulation results, a further study was

conducted to determine optimum data rate, node density, and

length of a node chain. As a first step it was decided to

determine the node density. Data rate of 0.01 and 0.5 Mbps

were used. Actual distribution level data communication

would be in this range as the requirement is to transmit only

the power system operation information in a secured manner.

Packet delivery faction and delay for these two cases are

plotted inFigure 6.

(a) Delay (b) Packet Delivery Fraction

Figure 6: Comparison 0.01 Mbps and 0.5 Mbps Communication


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The spike seen for node densities of 20 to 21 in Figure 6

(a) is due to a sudden decrease in the number of hops taken by

the AODV routing protocol from source to the destination..

The AODV protocol considers all possible paths from the

source to the destination and picks the one which can reach the

destination with the least delay, which typically is the route

which is composed of the shortest number of hops. As node

density increases, there is a certain point at which the routes

taken can skip over some nodes on the path to the destination.

From Figure 6, it could be seen that node density of 10 nodes

per km performs better for both 0.01 Mpbs and 0.5 Mbps

cases. The smaller the node density, the feeder level

communication is cheaper in terms of number of nodes that

need to be deployed. Therefore this work suggests using the

minimum possible node density of 10 nodes per km as the best

option. 100 % Data delivery faction is a critical component

for reliable communication. From Figure 6 (b) it could be

noted that when the data rate is 0.01 Mbps this could be

achieved but not with a data rate of 0.5 Mbps.

Further analysis was done for delay and packet delivery for

data rates between 0.01 and 1 Mbps. To compare the effect ofnode densities both 10 and 15 nodes per km were simulated.

Figure 7 shows the simulation results for these cases.


Figure 7: Simulation results for different data rates

From Figure 7, it could be seen that when the data rate

exceeds 0.15 Mbps the delay increases significantly and the

packet delivery fraction would decrease from 100 %. The

selection of 10 nodes per km is validated in this plot, as

increasing the node density to 15 nodes per km would

decrease the performance. Based on this simulation for a

reliable communication data rate should not exceed 0.15 Mbps

with the node density of 10 nodes per km.

As the final step it is important to determine the optimum

length of a single WiFi chain. Using node density of 10 and 15

nodes per km and data rate of 0.15 Mbps the length of the

feeder is varied from 1 to 25.5 km. The simulation results are

plotted in Figure 8.


Figure 8: Simulation Results for Varying Feeder Length

From the simulation results in Figure 8 it could be seen that

both 0.15 Mbps and 0.1 Mbps have almost the same delay ad

packet delivery fraction when the feeder length is the same.

Further, once the length on the feeder exceeds 25 km the

packet delivery fraction is less than 100%. Therefore the

maximum length of a WiFi chain is 25 km.

Based on the simulations, it could be seen that even when

the ideal communication scenario (no interference and only

one node is sending the information) there is a limit for the

data rate, node density and the length of the WiFi chain. Table

3 presents the limits on the WiFi multi-hop network for

distribution feeders.

TABLE 3:OPTIMUM WIRELESSNETWORK FORIDEAL CONDITION

Property Optimum Value

Node Density 10 nodes / km

Data Rate 0.15 Mbps

WiFi chain length (radial Topology) 25 km

V. EXAMPLE APPLICATIONFault location at the distribution feeder is used as an

example application of the proposed three layer

communication model. Figure 9 shows a section of a

distribution system. Layer 1 communication is between the

current and voltage sensors located at each pole and their

master nodes. This needs continuous communication, and at

every cycle (1/60 sec) all the samples collected during that

time are transmitted to the master node.

Figure 9: A Section of a Distribution System

Assume there is a single line to ground fault between the

poles A12 and A13. The fault will increase the line currentbetween the substation and the pole A12. The master nodes

located at the poles between these two points would see the

abnormality and activate to report the information when they

process the data from the sensor nodes using layer 1

communication. If all master nodes then try to communicatewith the control center, it will be overwhelmed with

information, and data collisions may result in delays.

To avoid multiple nodes transmitting the information to the

control center, layer 2 communication is introduced. Based on

the increase in current and ideas from selective protection

coordination and Media Access Control (MAC) level

contention techniques [22], a protocol can be developed such

that the pole close to the fault (for this example A12) will be


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identified. Since this is the closest pole to the fault, the

location of this pole would be considered the fault location.

Therefore only this node would communicate with the control

center. This is recognized as the second layer of

communication. Pre-fault, fault, and post-fault current signals

and the location of the pole would be included in the

information sent to control center.

Layer 3 communication would be between the selected

node in the second layer and the control center using the

geography-based routing protocol. This protocol will seek the

shortest path to the control center with a minimum number of

hops.

VI. CONCLUSIONThis work identifies requirements for distribution level

communication and proposes three-layer wireless

communication architecture. Various design choices made for

this architecture are explained in terms of cost, reliability, and

smart grid applicability.

VII. ACKNOWLEDGMENTThe authors gratefully acknowledge the support of the

Power Systems Engineering Research Center (PSERC) and

the contributions from the industrial and academic members of

the PSERC T-39 project, Communication Requirements and

Integration Options for Smart Grid Deployment, and from the

US Department of Energy Project DE-FG36-08GO88149,

Sustainable Energy Solutions.

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[19].Subir Kumar Sarkar, T. G. Basavaraju, C. Puttamadappa Ad hocmobile wireless networks: principles, protocols, and applications

[20].G. S. V. Radha Krishna Rao,G. Radhamani WiMAX, A WirelessTechnology Revolution

[21].Don Moskaluk, Wireless Mesh Topology January 2004 and updateJuly 2008

[22].Kyle Jamieson, Hari Balakrishnan, Y. C. Tay. Sift: a MAC Protocol forEvent-Driven Wireless Sensor Networks. MIT LCS Technical ReportMIT-LCS-TR-894, May 2003.

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[24].NS-2, The ns Manual (formally known as NS Documentation) avai lableat http: //www.isi.edu/nsnam/ ns/doc

IX. BIOGRAPHIESVisvakumar Aravinthan (S04, M10) received his BS degree in ElectricalEngineering from University of Moratuwa, Sri Lanka in 2002 and received hisMS and Ph.D. in electrical engineering from Wichita State University in 2006and 2010 respectively. Currently, he is a visiting lecturer at ClemsonUniversity teaching power system courses. His research interests include

distribution automation, smart grid applications, electric vehicles and controls.

Babak Karimi is a Ph.D. student and currently is working on applyingwireless communication to The Smart Grid. In 2008 he received his M.Sc. inInformation Technology from Amirkabir University of Technology of Iran.His research interests are designing new MAC layer protocols and working onsecurity issues of ad hoc networks. His bachelors degree is in ComputerSoftware Engineering that he received in 2005.

Vinod Namboodiri teaches communications and networking courses as anAssistant Professor of Electrical Engineering and Computer Science atWichita State University. He performs research in energy related aspects ofwireless networking that includes optimizing energy consumption of portablewireless networking devices and the application of wireless technologies forthe Smart Grid.

Ward Jewell teaches electric power systems and electric machinery as aProfessor of Electrical Engineering at Wichita State University. Dr. Jewell isSite Director for the Power Systems Engineering Research Center (PSerc). Heis a Fellow of the IEEE. He performs research in electric power systems andadvanced energy technologies. He has been with Wichita State since 1987.

PESGM2011 Feeder Communication

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