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Communication Network Modeling and Simulation
for Wide Area Measurement Applications
Yi Deng, Hua Lin, Arun G. Phadke, Sandeep Shukla, James S. Thorp, Lamine Mili
Bradley Department of Electrical & Computer Engineering
Virginia Polytechnic Institute & State University
Blacksburg, Virginia, 24061, USA
{yideng56, birchlin, aphadke, shukla, jsthorp, lmili}
Abstract— In the recent years, Phasor Measurement Unit (PMU)
based Wide Area Measurement System (WAMS) has been
receiving ever increasing attention from the academia as well as
from the industry. Power utilities have been designing and
implementing WAMS to provide more intelligent monitoring,
control, and protection of the power grid. In order to achieve
real-time operations in the modern power systems, construction
of an economic and efficient communication infrastructure is a
necessity, and various utilities have been laying fiber optical
network along their transmission and distribution right of way to
leverage the abilities of PMUs in providing greater visibility over
a larger area of the grid – thereby providing opportunities for
better control and stability. The choice of network architecture,
protocols, and various measures for quality of service guarantees
must be made by the network architects at the utilities. In this
paper, we present a methodology based on profiling data traffic
for various WAMS applications according to their
communication requirements, and then creating simulation
models and scenarios to obtain various parameters for specific
architectural and protocol choices. Our simulation results are
encouraging in the sense that under modest choices all the
applications meet the timing and bandwidth requirements.
However, the main contribution of this work is the methodology
that would allow the utilities to evaluate various communication
infrastructure choices while deploying WAMS.
The Smart Grid, which is in the rapid development and
deployment process, achieves its challenging objectives using a
large number of new techniques [1]. Wide area measurement
technology, as one of the cornerstone techniques for power
system dynamic analysis, has been proven to be an effective
method for large-scale power grid protection and control. Wide
area measurement system (WAMS) which utilizes Global
Positioning Systems (GPS) based Phasor Measurement Units
(PMU) has the capacity of capturing the steady state and
transient state information in real-time [2]-[4]. When the
distributed synchrophasor data are accumulated in a centralized
monitoring and controlling data center, they eventually support
various applications in terms of state estimation, event optimal
control, systematic protection, etc. [5], [6].
A high-speed and intelligent communication infrastructure
is the key to make time-critical WAMS applications feasible in
practice. Early communication technologies like power line
carrier and microwave communication have their own
limitations in reliability, scalability and robustness [7]-[9].
However, recent adoption of optical fiber communication in
power system allows the end-to-end data transmission latency
low enough to meet the communication needs. The properties
in terms of closed transmission media, lightweight physical
composition, ultra-high bandwidth, and low-loss light
propagation make the optical fiber very attractive in WAMS
In this paper, the communication infrastructure of WAMS
is modeled subject to the communication requirements of
WAMS applications. In section II, we describe the background
of communication requirements for WAMS and depict its
potential network architecture. WAMS applications are then
classified into categories according to their communication
characteristics. SDH over IP with MPLS support is proposed as
the main protocol used in this architecture. In section III, the
WAMS network is modeled in OPNET software hierarchically,
and the simulation profiles used in OPNET for WAMS
applications are introduced. The simulation results are
presented and discussed in section IV. Finally, the full paper is
concluded in section V.
The main contributions of this work can be summarized as
follows: The choice of network architecture, protocols, and
various measures for quality of service guarantees must be
made by the network architects at the utilities. In this paper, we
present a methodology based on profiling data traffic for
various WAMS applications according to their communication
requirements, and then create simulation models and scenarios
to obtain various parameters for specific architectural and
protocol choices. Our simulation results are encouraging in the
sense that under modest choices of network medium,
architecture and protocols – all the applications meet the timing
and bandwidth requirements. However, the main contribution
of this work is the methodology that would allow the utilities to
evaluate various communication infrastructure choices while
deploying WAMS.
A. Overview of WAMS
In general, wide area measurement in power systems is a
highly distributed application. The advances in communication
technologies, protocols, and quality of service differentiated
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service models enable us to share large volumes of data across
geographically distributed power grid assets and equipments
via high-speed, low latency network. WAMS thus require
mission-critical communication infrastructure capable of
collecting synchronized real-time measurements from
distributed PMUs [10]-[12]. A fortunate confluence of the
latest technologies for high-speed data acquisition, real-time
data transmission, efficient data concentration and highperformance data processing are making real WAMS
implementations possible. A typical WAMS consists of a GPS
based high-precision synchronized clock source, multi-level
centralized or a hierarchy of compute-intensive data processing
center(s), and multi-function decision making and control
executing center(s) – which all together form an integrated
technology platform.
In Figure 1, hardware architecture of a practical WAMS is
sketched. It is composed of five main components: substations
with Phasor Data Concentrator (PDC) denoted by blue dots,
substations without PDC denoted by red dots, centralized Super
Phasor Data Concentrator (SPDC) denoted by a black dot,
relay protection office denoted by a green dot, and highperformance backbone networks denoted by a cloud shape.
In these substations, PMUs, Relays, and Intelligent
Electronic Devices (IEDs) are connected with each other using
an Ethernet. IEC61850 GOOSE is being increasingly deployed
as the communication protocol between devices within the
substation. As the data switching within a substation is modest,
such a shared media access protocol can support the
communication traffic quite well. The PDCs gather phasor
measurement data which are acquired from distributed PMUs,
and then realign and reformat them for an SPDC. In the SPDC
node, the SPDC equipment is used to calculate and analyze the
uploaded data. There are also a large amount of data storage
equipments in charge of logging the measurements. The
Fig. 1. One possible hardware architecture of WAMS
System Control Center (SCC) sends control action messages to
critical relays during urgent situations through high-bandwidth
network. The relay protection office is attended by engineers
who can manipulate the control actions in the case that the
characteristics should be modified after long period of usage or
the measurement alerts. By using high-performance routers, the
substations, SPDC, and relay protection office are all bound
together as an integrated system.
B. Communication Medium for WAMS
Communication infrastructure is essential for WAMS
applications which collect phasor measurement data from
remote locations. Channel capacity and latency are usually the
most significant performance-related factors in any
communication task. The data streams created by the PMUs are
quite modest so that with the right communication technology,
the channel capacity is rarely a limiting factor in most WAMS
applications. On the other hand, some applications may require
low latency – in particular, time-critical applications for realtime control of power systems. However, not all applications
require low latency, for example, post-mortem analysis
applications which require PMU data to analyze the power
system performance during major disturbances [13]. The
channel capacity and latency of different communication media,
such as power line, satellite, microwave, and optical fiber etc.,
vary significantly. Among these, the key features of fiber optics
that have been high channel capacity, high data transfer rate,
low transmission loss, immunity to electromagnetic
interference and low cost, make it the most suitable candidate
for WAMS applications [7], [14]-[16]. Therefore in this work,
we assumed optical fiber communication infrastructure for
WAMS application enabling. Our simulation based analysis is
realistic as per our experience with a number of electrical
utilities in the United States – the optical fiber communication
is adopted as the main technology for the WAMS backbone
networks for most of them.
C. Communication Protocols for WAMS
Considering the real-time performance and reliability, SDH
in optical fiber networks is becoming mainstream for WAMS.
Developed from Synchronous Optical Network (SONET),
SDH was adopted by International Telecommunication Union
Telecommunications Standardization Sector (ITU-T) as an
international standard for the second generation of digital
transport technology in 1988. In the frame structure of SDH,
the overhead section occupies about 2.96% of the frame size so
that the payload transmission efficiency will be quite high.
Furthermore, there are maintenance and management sections
within the frame structure which help form a robust network
for WAMS communication. On the higher level, IP-related
protocols are popular in packet-switching networks. Compared
to circuit-switching networks, packet-switching networks are
more flexible and efficient. Also, in order to introduce
differentiated Quality of Service (QoS) guarantees, the multiprotocol label switching (MPLS) scheme – an Internet
Engineering Task Force (IETF) standard based on Cisco’s tag
switching has been established. This is a connection-oriented
structure integrated into the otherwise connectionless IP
network. One major function of MPLS is Resource Reservation
Protocol – Traffic Engineering (RSVP-TE), which manages the
flowing pass for every IP package and avoids data aggregation
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at the congested node. Another function of MPLS is the
priority scheme to supply the IP precedence or label based QoS
[17]. MPLS Virtual Private Networks (VPN) helps divide the
different WAMS application services into differentiated
priority channels. Conclusively, the IP over SDH with MPLS
seems to be the most appropriate candidate for WAMS – and in
our simulation study – we employ this.
With the development of WAMS, the feasibility of several
applications on it has been proposed and proved through
theoretical analysis. The communication needs for each kind of
WAMS application have been discussed in [5]. From the recent
research studies, it is clear that with fiber optical infrastructure,
MPLS over SDH, the communication requirements for each
application are quite modest. Nonetheless, the collective
performance still could be uncertain when those applications
run simultaneously on the communication infrastructure
creating a lot of data and control traffic – possibly affecting
latency, and reliability. In order to study these, as well as to
create a methodology for evaluating various network
architecture, protocol and communication media choices – we
model the communication networks of WAMS applications in
OPNET software and do profile based simulations to compute
various timing parameters. This way, one can investigate
various communication network topologies, channel capacities,
transmission delays, link throughputs and bandwidth
A. A Short Introduction to OPNET
The networking simulation tool we use is the OPNET
Modeler [18], a powerful and comprehensive modeling and
simulation software which is dedicated to communication
network research and development. The hierarchical network
modeling architecture which corresponds to actual protocol
layer, device layer, and network layer can enable the accurate
simulation of WAMS-like, end-to-end, system-level network
B. WAMS Application Categories
WAMS supports various operations of power system such
as monitoring, protection, and control. In the literature, there
are 12 frequently used WAMS applications [5]. According to
the communication needs, these applications can be classified
into four different data transmission profiles: periodic transfer
without acknowledgements, large amount of burst transfer
without acknowledgements, small amount of burst data transfer
without acknowledgements, and burst transfer with
acknowledgement. Mapping to OPNET’s pre-defined
transaction profiles, these four communication profiles can be
modeled as: the video conference, file transfer protocol (FTP)
data transfer, print operation, and remote login with response
respectively. The corresponding relationship between WAMS
application types and OPNET application profiles is listed in
This classification can distinguish the time critical
applications from other applications. Take the application
‘supervision of backup zone’ as an example – designed to
prevent mis-operation of back-up protection zones – the PMUs
on remote buses need to monitor the apparent impedance of the
WAMS Application Types
Application Profiles
Periodic transfer without
Video conference
Large amount of burst data transfer
without acknowledgements
FTP data transfer
Small amount of burst data transfer
without acknowledgements
Print operation
Burst transfer with
acknowledgement required
Remote login with
transmission lines. When a false fault is picked up by backup
relays, the PMUs around the back-up relays should send
messages to backup relays to prevent the false trips. This action
belongs to the fourth type of applications since by acquiring the
acknowledgement the sender can make sure that the critical
action has been executed.
C. WAMS Communication Network Structure
We model the entire WAMS communication network in a
hierarchical manner from local inner-substation networks, to
last mile access networks and to wide area backbone networks.
A skeletal network structure is shown in Figure 2.
The octagon shape nodes Ri (i=0, 1, 2, .., n-1) represent one
of the n routing nodes (RN) on the backbone network which in
this case, has a ring topology. The routing nodes may consist of
various numbers of high-performance routers. There are two
kinds of circle shape nodes Si,j and S’i,j which represent the
PMU-equipped substation without PDC and with PDC
respectively. Usually, there is only one S’i,j node in a regional
area. The main function of the PDC is to aggregate and align
the PMU data based on time stamps. Substations in the same
regional area are connected to one of the routing nodes on the
backbone ring. Figure 2 only shows the subsidiary substations
for one routing nodes, Ri, but in fact, other routing nodes also
have their own group of substations. The star shape node P
represents the SPDC in WAMS. There is only one SPDC in
WAMS which is in charge of system monitoring and control at
the highest level.
Physically, this example ring topology backbone network is
an optical fiber network using SDH. The communication links
between two routing nodes (Ri ↔ Ri+1) denoted by LH are
modeled as 155.520Mbps SDH STM-1. This type of link is
also used to connect the SPDC node to the routing node (P ↔
Ri). The communication links between substations and routing
node (S ↔ Ri or S’ ↔ Ri) denoted by LL are modeled as 2Mbps
E1 links which should be sufficient for all applications.
Fig. 2. The structure of backbone network.
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SPDC. The main three monitoring applications are “state
estimation”, “seams between state estimates” of two adjoining
Independent System Operators (ISO), and “instrument
transformer calibration” for all-PMUs estimator.
Fig. 3. The system infrastructure of the entire system.
The WAMS communication infrastructure constructed in
OPNET is shown in Figure 3. In total, 120 PMUs are placed in
the system which can cover most areas of the eastern US grid.
In each substation, we place two PMUs, various relays, and
circuit breakers over a 100Mbps Ethernet. In OPNET, the
paradigms of these equipments are represented by workstations
and servers. The SPDC node is composed of server-based
SPDC and workstation-based SCC. The subnet of the routing
node is composed of six high-performance routers as shown in
Figure 4. The routers connect to substations located in its
region and to other routing nodes. The dotted line between R2
and R3 indicates that the routing node subnet is able to expand
to a larger scale by adding more routers.
Although these three monitoring applications are different
and independent, their communication characteristics are very
similar and in fact can be captured using the same network
simulation profile. In the “seams between state estimates”
application, by utilizing the uploaded all-PMUs measurement
data including boundary reference buses in both areas, the
SPDC can distinguish the two state estimators and the
differences of reference angles. As for “the instrument
transformer calibration”, all needed information has been
transmitted and stored to the SPDC periodically and these prestored data will be re-fetched every 12 hours when doing
calibration calculation. Hence, the communication data flows
in these two applications are all the same with the all-PMUs
state estimation application so that only one simulation profile
is needed.
The packet end-to-end (ETE) delays of the power system
monitoring applications are plotted in Figure 5. The entire
transmission delay can be divided into two stages. The first
stage is the aggregation delay from local PMUs to regional
PDCs. The second stage is the gathering delay from distributed
PDCs to the centralized SPDC. From the curves, we can see
that the regional transmission delays shown by color lines are
around 20ms and the wide area transmission delays denoted by
black lines are 40ms, therefore the entire transmission delays
are approximately 60ms.
Fig. 4. The architecture of subnet of routing node
First, we create simulation scenarios for various WAMS
applications individually in OPNET. We simulate all the
applications on the same network infrastructure but with
different prototype application profiles. The network statistics
for each application including data flow, transmission
throughput, real-time indicator, reaction time, and end-to-end
delay are significantly different based on the simulation results.
Some of the applications are latency critical and some are not.
Then, a hybrid scenario is simulated where all possible WAMS
applications run simultaneously on the network infrastructure.
Fig. 5. ETE delays for monitoring applications
A. Individual Simulations
1) Power System Monitoring:
As one of the most important applications of WAMS, the
power system monitoring needs all PMUs which are installed
in substations to upload the measured data to the centralized
Fig. 6. The throughput of three communication link types
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From RN to PDCs
Between Routing Nodes
Wide Area
Wide Area
From RN to SPDC
The throughput and link utilization are shown in Figure 6
and Table II. From the statistics of simulation data, the heaviest
loaded channel is the communication link between SPDC and
its nearest routing node. The number of local PMUs can be
controlled in a predictable manner. Therefore, the
communication throughput between PMUs and PDCs is also
controllable. If more PMUs are installed into the WAMS
architecture, the throughput of this communication channel will
increase accordingly and will become a bottleneck for the
2) Power System Protection:
In this category, the communication network architecture
take charge of delivering the real-time control commands.
Typical protection applications include adaptive dependability
and security (ADS), monitoring approach of apparent
impedances (MAAI), adaptive out of step (AOS), supervision
of back-up zone (SBUZ), adaptive loss of field (ALF),
intelligent load shedding (ILS), intelligent islanding (II), etc.
Depending upon the communication latency needs, they are
categorized into non critical applications which can tolerate up
to one second delay, and time-critical applications that the
maximum acceptable delay should be below 50ms.
The simulation results of the maximum end-to-end delays
and the maximum response time for all the protection
applications are listed in Table III. In the ADS application, the
system control center decides the current state of the system,
and then sends voting messages to nine distributed critical
relays asking for acknowledgements; In the MAAI application,
the apparent impedance – calculated by PMUs will be sent to
the relay engineer office without requiring acknowledgements
to warn the relay engineers regarding the change of relay
tripping characteristics; In the AOS application, the PMUs
which are installed outside the generator, upload the
measurements and track rotor angles, and to determine
coherent groups they send the information to the PDC with no
acknowledgements; In the SBUZ application, PMUs which fall
inside the back-up zones can monitor the apparent impedance
and send decisions to back-up relays with acknowledgements,
and eventually prevent mis-operations which could lead to
cascading failures and blackouts; In the ALF application, in
order to revise some drift data which are introduced by long
time usage or unexpected environment changing, the SCC will
send the adjustment orders to the loss of field relays with
acknowledgements; In the ILS application, the PMUs which
are used to monitor the tie-line power flows will take charge of
the supervisory control, and send measurement data to PDC
without acknowledgements; In the II application, since the
instability is inevitable, the system control center has to make
decisions to trip or block the related circuit breakers with
acknowledgements needed.
SCC to 9 Critical Relays
ETE Delay
Time (ms)
3 PMUs to Relay Engineer Office
2 Generator PMUs to PDC
10 PMUs to Back-up Relay
SCC to 3 Loss of field Relays
4 Tie line PMUs to PDC
SCC to 9 Circuit Breakers
As shown in Table III, after analyzing the simulation results
of all the applications and comparing with the communication
needs, the maximum end-to-end delays and response time
within the ring topology fiber-optic network are all below the
time constraints – 50ms, 100ms respectively.
3) Power System Control:
The simulations for power system control applications
mainly focus on the control of sustained oscillations and large
oscillations. The simulation results for these two applications
are shown in Figure 7.
Fig. 7. The ETE delay for power system control applications
Using PMUs to damp the low frequency inter-area
oscillations is one of the most attractive applications of WAMS.
In our simulation, we assume that there are 5 control devices
and a total of 25 remote PMUs in the system. This application
involves wide area communication where measurement data
might travel hundreds of miles. The large oscillations control
application, which is mainly used for preventing transient
instability, gathers and allocates measurement data and control
signals for data communication.
B. Hybrid Simulation
In practice, all the applications mentioned above are going
to run simultaneously no matter what the transaction types are.
In a worst scenario, there will be data traffic associated with all
these applications at the same time. In order to guarantee the
accuracy and effectiveness of PMU measurement data, these
time-critical applications must have constraints on the end-toend transmission delay. In the worst case scenario, we
simulated these independent applications all working at the
same period of time. The hybrid simulation results of end-toend delays are shown in Figure 8.
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constraints. In the future, we will integrate OPNET with a
power system simulation environment so that real data traffic
can be simulated from PMU models – providing further
accuracy to the results.
Fig. 8. The hybrid simulation results of packet end-to-end delays
The end-to-end delays between distributed PDCs and
centralized SPDC are about 40ms which is the same as the
delays of all-PMUs state estimation application. The end-toend delays from local PMUs to regional PDCs are around 20ms
which are slightly different with those individual applications.
Wide area measurements applications enabled by the wide
spread deployment of PMUs and communication networking
will be one of the most important aspect of intelligent power
grid of the near future. Rapid PMU deployments have begun in
the United States, in European Union, and in China.
Algorithms for various WAMS applications have been
proposed, and implemented in prototypes, wide area closed
loop control for stability have been proposed, and
experimented with. However, many of these applications
require real-time delivery of large volume of real-time data that
the PMUs collect 30 times a second or faster. The choice of the
proper network topology, transmission media, and protocols
will play important role in fulfilling the idea of wide area
monitoring and control. Choice of network architecture,
protocols etc., are hard to make without knowing the exact
volume of data, requirements in latency and bandwidth, quality
of service etc. A framework in which such choices can be made
via realistic simulation studies is proposed and demonstrated
here. Although, in this paper we only present results for a
specific network topology, specific data bandwidth choice, and
protocol choice – these can be easily varied in the modeling
and simulation framework of OPNET to compare and choose
by a utility company. As the WAMS applications and
deployments get more dense, and wide spread, the network
traffic associated with the various applications will grow, and
new architectural choices, newer protocols and transmission
media etc., must be chosen. This paper demonstrates one
framework to do that. Also, in this work, four traffic profiles
are used to obtain the simulation models and scenarios. The
results obtained in the simulation study presented here is quite
encouraging – all applications and the simultaneous execution
of all WAMS applications meet the latency and bandwidth
U.S. Department of Energy Office of Electricity Delivery & Energy
Reliability “Smart grid research & development multi-year program plan
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