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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 5, NO. 2, APRIL 2014
Real-Time Implementation of Intelligent
Reconfiguration Algorithm for Microgrid
Farshid Shariatzadeh, Student Member, IEEE, Ceeman B. Vellaithurai, Student Member, IEEE,
Saugata S. Biswas, Student Member, IEEE, Ramon Zamora, Student Member, IEEE, and
Anurag K. Srivastava, Senior Member, IEEE
Abstract—Microgrids with renewable distributed generation and
energy storage offer sustainable energy solutions. To maintain the
availability of energy to the connected loads, considering priority
and to interrupt the smallest portion of the microgrid under any
abnormal conditions, reconfiguration is critical to restore service to
a section or to meet some operational requirements of dropping
minimum loads. Reconfiguration is the process of modifying the
microgrid’s topological structure by changing the status (open/
close) of the circuit breakers or switches. In this work, constraints
are the power balance equation and power generation limits, and we
assumed that the system is designed with the entire planning and
operational control criterion to meet the voltage violation and line
overloading constraints. This paper offers novel real-time implementation of intelligent algorithm for microgrid reconfiguration.
Intelligent algorithm is based on the genetic algorithms and has been
tested on two test systems including shipboard power system and
modified Consortium for Electric Reliability Technology Solutions
(CERTS) microgrid. Real-time test bed utilizes real-time digital
simulator and commercial real-time controllers from Schweitzer
Engineering Lab. Reconfiguration algorithm has been implemented
in the real time using real-time test bed, e.g., microgrid system, and
satisfactory results were obtained.
Index Terms—Genetic algorithm (GA), graph theory, microgrid,
real-time simulation, reconfiguration, restoration.
I. INTRODUCTION
HE INCREASING installations of distributed generations
(DGs) provide an opportunity to treat DGs, local loads,
and distribution network as a self-sustainable cluster to function
as a subsystem of distribution system [1], [2]. This cluster is
considered as a microgrid that can be defined as an interconnection of DGs with possibly distributed storages on low- or
medium-voltage distribution system [3]. Due to its potential to
provide reliable, secure, efficient, environmental friendly, and
sustainable electricity from renewable energy sources, microgrid
is a very promising solution for today’s electricity problems [4].
A microgrid generally has two modes of operation,
grid-connected and islanded. In normal condition, a microgrid
is connected to the main grid and acts as a subsystem of
T
Manuscript received May 31, 2012; revised March 02, 2013; accepted July 21,
2013. Date of publication January 02, 2014; date of current version March 18,
2014. This work was supported by the School of Electrical Engineering and
Computer Science, as well as the Research Office at Washington State University.
The authors are with the School of Electrical Engineering and Computer
Science, Washington State University, Pullman, WA 99164-2752 USA (e-mail:
fshariat@eecs.wsu.edu; ceemanbrightson@gmail.com; saugatasbiswas@gmail.
com; r.zamora@email.wsu.edu; asrivast@eecs.wsu.edu).
Color versions of one or more of the figures in this paper are available online at
http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TSTE.2013.2289864
distribution systems. In this mode, local loads inside the microgrid are supplied by DGs located near the loads. The connection
to main grid is aimed to transfer the power between the microgrid
and main grid, if there is a deficiency or excess generations in
either side. When there is a problem in the main grid, microgrid
will disconnect from the main grid and work autonomously
[5], [6]. This mode of operation is called islanded mode. Not all
the microgrids have two modes of operations, some microgrids
fully operate as islanded microgrid because they do not have the
access to the main grid. This type of microgrid is also known as
isolated microgrid. Due to its similar properties, such as a cluster
of small-scale local generations and loads in a small region, a
shipboard power system (SPS) can be considered as a microgrid,
an isolated microgrid [7]–[9].
A seamless transition from grid-connected to islanded modes
and vice versa and a stable operation in both the modes are very
important for microgrid operation. Between these two modes,
islanded mode of operation is more challenging due to the
capacity limit of DG to supply local loads, and generator capacity
being closely sized to the load demand. The ratio of available
generation and load demand is a very important factor for the
success of islanded operation. This ratio will be more important
when a reconfiguration action is taken in order to isolate the
faulted area, whereas minimizing the unsupplied loads after a
fault occurs inside the microgrid. A reconfiguration is required
since protection schemes only provide fault detection and isolation functions, but do not consider the system level optimization
constraints or power balance after fault isolation. Reconfiguration can be defined as a type of emergency control including
topology changes, load and generation shedding, and other
control actions to redirect power flow to the remaining connected
loads [10], [11]. An effective and optimal reconfiguration requires intelligent and fast actions.
Reconfiguration requires fast actions to minimize the effect of
fault on the entire system. For some systems such as SPS, the fast
process is really essential. Hence, the reconfiguration needs to be
done in real time, if possible. Real-time reconfiguration requires
fast algorithm processing time and less communication delay.
In real time, the measured data of the faulted system can be
processed immediately by the reconfiguration algorithm in order
to provide control signals that define the reconfigured system.
In order to implement the real-time reconfiguration, finding an
accurate heuristic or numerical method of reconfiguration is
very essential [10].
Contrary to much research on conventional distribution system reconfiguration, research on microgrid reconfiguration is
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SHARIATZADEH et al.: REAL-TIME IMPLEMENTATION OF INTELLIGENT RECONFIGURATION ALGORITHM FOR MICROGRID
still in infancy. Conventional distribution system reconfiguration
is not suitable for the microgrid reconfiguration due to the
uniqueness of microgrid as discussed above. In addition, SPS
as an isolated microgrid has additional constraints under battle
conditions, which require high reliability and survivability [12].
A reconfiguration of IEEE standard distribution systems containing a microgrid is addressed in [13], whereas in [14] conventional
distribution system reconfiguration by creating a combination of
microgrids as the reconfigured system is discussed. Both of these
research works are not really microgrid reconfiguration, just
embedding microgrid paradigm on distribution system reconfiguration. Several reconfiguration algorithms have been proposed
for SPS. The heuristic-search approach for SPS reconfiguration
problem has been discussed in [15]. This method is a very simple
but has problems in restoration with an increasing number of
loads. In the method of network flow approach [16], [17], the
load priorities have not been considered. Self-healing approach
for SPS reconfiguration presented in [18] was based on predictive analysis in stored database causing problems for bigger
system. Several other intelligent reconfiguration methodologies
including ant colony, genetic algorithm (GA), hybrid methods,
and particle swarm optimization were discussed in [19]–[28] for
distribution system.
This paper uses evolutionary reconfiguration algorithm
based on GA [25] and developed by authors of this paper
previously. GA is a heuristic method that mimics a process of
natural evolution and is able to find optimal solution in search
space for specific cost function and constraints [29], [30]. This
algorithm has been applied by authors to 8-BUS SPS [25] and
modified CERTS [31] to find postfault optimal configuration
meeting the designated objectives and system constraints and
preliminary work reported in the earlier publication. In [25],
GA algorithm was developed for offline simulation limited to
specific operating scenarios and fault cases. In [31], the authors
reported the work using dSpace controller prototype. The work
reported in this paper is a significant improvement over these
two earlier publications with improved reconfiguration algorithm and real-time implementation using commercial grade
real-time automation controller (RTAC). In this work, optimization algorithms application to real-time reconfiguration of the
microgrid with considerations of the load priority, DGs, zonal
distribution, and islanding using GA has been presented. The
work presented here can be easily transferred to industrial
microgrid reconfiguration.
This paper is organized as follows. Section II describes the
graph representation of the system and explains the GA reconfiguration algorithm. Section III describes the simulation of the
proposed algorithm on example test cases using MATLAB.
Section IV describes the real-time test bed. Section V includes
the real-time simulation results and summary, and future work is
given in Section VI.
II. RECONFIGURATION ALGORITHM FOR MICROGRID
Reconfiguration algorithm utilized here is an improved form
of algorithms developed by the authors of this paper in [25].
In [25], GA algorithm could not generate solutions for all
possible operating conditions because of the inherent stochastic
RELATIONSHIP
OF
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TABLE I
MICROGRID COMPONENTS AND GRAPH REPRESENTATION ELEMENTS
approach of the GA algorithm. In the improved algorithms, an
inner loop is included to avoid the infeasible solutions. Reconfiguration algorithm consists of two parts: graph representation
and GA algorithm. Reconfiguration algorithm using graph
theory to describe the microgrid topology is shown in Fig. 3
[31]. The graph represents the tie-lines of microgrid as edges
and buses of microgrid as vertexes. It is assumed that all
components in this system are connected together through
circuit breakers (BRK). Table I shows the relationship between
the components of microgrid and elements of graph. In the
graphical representation of microgrid, the direction of edges
corresponds to the direction of power flow in the normal
condition. Microgrid is divided into several zones based on
the direct connection of buses to circuit breakers. The zone is
defined as a bus, which is directly connected to a circuit breaker.
Graph representation of 8-BUS SPS and modified CERTS has
been reproduced from the previous publication of authors [31]
in Figs. 4 and 5 for more clarity.
Graph theory has been utilized in reconfiguration algorithm to
provide matrices required by other parts of the algorithm. These
matrices describe microgrid topology and information about
loads, generators, and load flow. One of these matrices is called
edge-to-vertex (EtoV) that takes care of describing microgrid
topology for reconfiguration algorithm. Number of rows of EtoV
matrix is equal to the number of zones of microgrid and number
of columns of EtoV matrix is equal to the number of edges
of microgrid. Based on (1),
describes the connection
between the th zone and th edge
Also, there are other matrices: breaker types (BRK_TYPE)
were “1” means generator breaker, “2” means tie-line breaker,
and “3” means load breaker; status of breakers (BRK_STATUS),
where “1” is for closed breaker and “0” is for open breaker; power
flow on each breaker (BRK_FLOW); capacity of generators
(GEN_CAP); real power consumption of loads (LOADS); and
priority of loads in microgrid (LOAD_PRIORITY) which will be
used by GA algorithm.
When a fault is simulated in the microgrid, reconfiguration
algorithm computes the required tripping of breakers to isolate the
faulted bus and then searches the microgrid for finding zones with
negative real power based on the measurements. If there is a
negative zone, a path finder routine included in the reconfiguration
algorithm looks for all possible paths to feed the negative zone.
Based on the detected paths, GA algorithm optimizes load breaker
statuses to maximize the served MW load in the system based on
the load priority. In this fashion, path finder routine determines the
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Fig. 1. Reconfiguration process flowchart.
changes in tie-line breakers’ status and GA algorithm controls
the load breakers. GA algorithm runs for every negative zone.
Reconfiguration process has been shown in Fig. 1.
The flowchart in Fig. 1 can be described as follows.
1) Generating the graph equivalent of microgrid network
using known topology of microgrid.
2) Detecting the fault by circuit breaker statuses in comparison
with their stored value for normal condition.
3) If a fault occurs, protection system isolates the fault; the
fault location would be known and graph theory determines the surrounding breakers of the faulted bus to open
them.
4) Find negative zones using direct measurements.
5) Find path to feed the negative zone: path finder scheme
finds all possible paths to feed the negative power zone.
6) Optimization problem: GA algorithm finds out optimal
solution for possible paths to maximize the served MW
load with respect to load priority.
In reconfiguration algorithm, breakers’ statuses are design
variables with binary values of “0” or “1.” Reconfiguration
problem can be formulated as optimization problem to find the
optimum solution of maximizing the served load with respect to
load priorities after fault occurrence. GA algorithm has been
designated to solve the stated optimization problem in (2)–(4)
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 5, NO. 2, APRIL 2014
Equation (2) is an objective function, where is the number of
loads,
is the weighting factor for th load by considering the
load priority, is the breaker status corresponding to th load,
and
is the real power of th load. As mentioned, is a binary
variable that uses “0” as an open status and “1” as a closed status
for breakers. Equations (3) and (4) describe the constraints in the
optimization problem.
is the total number of generators in
microgrid.
is producing real power by th generator,
is
the minimum allowable real power for th generator, and
is
the maximum allowable real power for th generator. There are
also other operational constraints that can be included in the
optimization problem such as bus voltage constraints, line flow
constraints, and generators ramp constraints; however, in this
work, those constraints have been assumed as a part of optimal
power flow running after reconfiguration. In order to meet the
operational system requirements to quickly restore the loads,
optimal power flow (OPF) should be solved for the system after
reconfiguration. Also, it was assumed that voltage and line
constraints are taken care in some manner at planning and design
stage. In comparison with [25] and [31], GA algorithm has been
improved to deal with all possible fault cases in real time for this
work. Population and generation sizes have been changed to find
minimum allowable numbers that can find solution for all cases
to make reconfiguration algorithm faster in the real-time implementation. Also, mutation and crossover operators have been
improved to find solution for all fault cases. In [25], GA algorithm was not able to generate solution for some cases. It is
noticeable that, as GA is a stochastic process, there is always a
chance to not to converge or stuck in a local optimum; however,
in this work, GA was run five times for each test case and most
common solution was chosen as an optimal solution.
III. SIMULATION RESULTS
Developed reconfiguration algorithm has been implemented
using MATLAB for two microgrids, e.g., 8-BUS SPS and
modified CERTS. Microgrid refers to a subsystem that includes
generation and associated loads [2]. This group of generation and
loads can be islanded due to the disturbance in the system without
harming the transmission grid integrity. In this concept, 8-BUS
SPS is an islanded microgrid [25], whereas modified CERTS is a
grid-connected microgrid [31].
A. 8-BUS SPS Description
Normal operational condition of 8-BUS SPS is shown in
Fig. 2.
As shown in the figure, 8-BUS SPS has 4 generators, 18
breakers, and 6 loads. Size of the population and generation has
been taken as 10 in GA algorithm for 8-BUS SPS. Microgrid loads
SHARIATZADEH et al.: REAL-TIME IMPLEMENTATION OF INTELLIGENT RECONFIGURATION ALGORITHM FOR MICROGRID
601
Fig. 3. Graph representation of 8-BUS SPS.
TABLE III
RECONFIGURATION RESULTS FOR 8-BUS SPS
Fig. 2. 8-BUS SPS model in normal operational condition.
TABLE II
LOAD PRIORITY FOR 13-BUS SPS
have been divided into three levels of priority as nonvital,
semivital, and vital. Table II includes load priority for 8-BUS SPS.
Graph representation of 8-BUS SPS in the normal operation
condition has been shown in Fig. 3. Edge directions in this graph
are based on direction of power flow.
B. Simulation Results for 8-BUS SPS
Reconfiguration algorithm has been tested for several fault
cases and was successful to find solution for all cases. The
summarized results for 8-BUS SPS have been shown in Table III.
In this table, (O) means changing breakers status from close to
open in order to make possible path to serve negative zone and
(C) means changing from open to close. As shown in Table III,
reconfiguration algorithm tries to keep vital loads in the system.
In case 4, microgrid has lost 2 generators with 40 MW out of
80 MW generations; therefore, reconfiguration algorithm shed
one semivital load (L3) and nonvital load (L4) to serve two vital
loads (L2 and L5).
C. Modified CERTS Microgrid Description
Also, reconfiguration algorithm has been implemented on
modified CERTS as an example of grid-connected microgrid.
Normal operational condition for modified CERTS has been
shown in Fig. 4. It has one common point to main grid (BUS 1), 4
generators, 23 breakers, and 7 loads.
Also priority levels for loads are shown in Table IV. In this
microgrid, loads have been divided into four levels of priority as
critical loads, vital load, semivital loads, and nonvital loads.
The graph representation for normal operational condition of
modified CERTS has been reproduced in Fig. 5. There are some
normal open breakers in the microgrid that has been shown in
the graph representation to show possible connection between
the vertexes; however, the direction of the edges for these
breakers is arbitrary.
D. Simulation Results for Modified CERTS Microgrid
The results of implementing the developed reconfiguration
algorithm on modified CERTS have been shown in Table V. In
case 1, a semivital load has been shed (L6) and nonvital load (L5)
maintained in the system. It is because reconfiguration algorithm
maximizes the served kW load with respect to load priority,
hence it is not always shedding low priority loads. Loosing BUS
1, 330 kW generation remains in the microgrid and total load
is 360 kW which means if reconfiguration algorithm sheds L5
as 20 kW, it still needs to shed another load to meet constraint
stated in (3).
Computational time of GA reconfiguration algorithm is shown
in Table VI for 8-BUS SPS and modified CERTS. “N/A” in these
tables means no load shedding has been done with GA. As
shown, GA algorithm consumes reasonable time to reconfigure
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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 5, NO. 2, APRIL 2014
Fig. 4. Modified CERTS model in normal operational condition.
TABLE IV
LOAD PRIORITY FOR MODIFIED CERTS
the system. As it is indicated in Table VI, all the test cases can be
run in less than 0.05 s that would be our time limit for these test
cases to implement in real time.
IV. REAL-TIME TEST BED
In this work, industrial sensors and real-time controllers have
been used with real-time digital simulator (RTDS) to build a realtime test bed and test-developed reconfiguration algorithm. Here,
real time means performing a given task in a guaranteed time
limit, irrespective of the system condition [32], [33]. Commercial
real-time controllers used in this study are designed to operate in
the real time.
There are a few works that tested reconfiguration algorithm in
real time [34]–[36]. However, in this work, real-time test bed
consists of industrial sensors and actuators. The test bed comprises of RTDS, SEL-3530 or RTAC, SEL-421, and SEL-351.
The connections are made as shown in Fig. 6.
RTDS is a fully digital electromagnetic transient power system
simulator that operates in real time. RTDS is a power system
simulator designed for continuous real-time operation. For solving electromagnetic transient simulations in real time, the typical
time step used by RTDS is
. RTDS architecture enables
Fig. 5. Graph representation of modified CERTS.
users to interact with a running real-time simulation through
digital and analog I/O channels.
Due to its real-time operation, RTDS can be connected to
external devices that allow closed-loop testing of real-world
physical equipment. Simulation model can be implemented on
hardware of RTDS using RSCAD as software tool. In this work,
GTFPI card is used as digital interface, which is shown in Fig. 7.
I/O connection in GTFPI comes from RTDS front panel and
GTFPI sends signals to GPC card in RTDS. Fig. 8 shows the
front panel connection for the digital control signals coming into
simulated system by the reconfiguration algorithm in RTAC. The
signals are sent to the relays, which are in turn fed to the digital
input (GTFPI) of the RTDS.
SHARIATZADEH et al.: REAL-TIME IMPLEMENTATION OF INTELLIGENT RECONFIGURATION ALGORITHM FOR MICROGRID
603
TABLE V
RECONFIGURATION RESULTS FOR MODIFIED CERTS
EXECUTION TIME
OF
TABLE VI
GA RECONFIGURATION
ON
MATLAB
Fig. 7. GTFPI card on back panel of RTDS.
Fig. 8. Input digital signal connection on RTDS front panel.
Fig. 6. Hardware configuration of real-time test bed.
SEL-3530, RTAC, is a programmable logic controller (PLC).
It is a powerful automation platform that combines the embedded
microcomputer, embedded real-time operating system, and
secures communications with IEC-61131 compliant programmability. It includes secure communications, advanced data
concentration, high-speed logic processing, flexible engineering
access, and protocol conversion capabilities between the multiple
built-in client/server protocols. It also has a separate watchdog
microcontroller system that provides an extra level of system
reliability, which activates an alarm and halts all input/output
activity, if there is a problem with the IEC-61131 logic engine.
RTAC has been used as a controller in the loop that implements
reconfiguration algorithm. To implement GA and graph theory
code in RTAC, structured text (ST) has been used. ST is a flexible
language for PLC which is supported by IEC-61131 standard.
SEL-421 relay is a high-speed transmission line protection
relay featuring synchronism check, circuit breaker monitoring,
circuit breaker failure protection, and series-compensated line
protection logic. Wiring of SEL-421 is shown in Fig. 9 to transfer
the signal from RTAC to RTDS front panel.
SEL-351 provides complete overcurrent protection for lines
and equipment. In this work, it has been used for controlling and
monitoring the circuit breakers. SEL-351 sends signals to RTDS
front panel similar to SEL-421.
Table VII provides the technical specifications and the
interfacing information of the different hardware devices that
have been used for real-time test bed of the microgrid reconfiguration algorithm. The test bed includes RTDS, RTAC, SEL-421,
and SEL-351 connected in a closed-loop fashion. The microgrid
system is modeled in RSCAD and uploaded into the RTDS. The
RSCAD model is shown in Fig. 10.
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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 5, NO. 2, APRIL 2014
Fig. 10. Microgrid model in RSCAD.
Fig. 9. I/O connections of SEL-421 relays.
SPECIFICATION
OF
TABLE VII
HARDWARE OF REAL-TIME TEST BED
As shown in Fig. 10, all parts of the microgrid has been modeled
through RSCAD and GTFPI card sends control signals to circuit
breakers to implement reconfiguration. The parameters of the
system can be monitored in RSCAD runtime. As mentioned
earlier, “0” in the RSCAD runtime indicates an open breaker,
whereas “1” indicates a closed breaker. The reconfiguration
algorithm is loaded into the RTAC, which is executed every
100 ms. This execution time setting can be adjusted in the RTAC.
During simulation of the system in RTDS, the RTAC continuously monitors the status of the breakers and changes its output if
any of the circuit breakers change their state. The communication
from SEL-3530 to SEL-421/SEL-351 is accomplished through
SHARIATZADEH et al.: REAL-TIME IMPLEMENTATION OF INTELLIGENT RECONFIGURATION ALGORITHM FOR MICROGRID
605
Fig. 11. Transition of nine breakers’ statuses after fault case 1.
Fig. 12. Transition of nine breakers’ statuses after fault case 4.
DNP3 Ethernet protocol, whereas switch control signals from
SEL-421/SEL-351 to RTDS is hardwired as shown in Figs. 8
and 9. When a fault occurs in the microgrid power system being
simulated in the RTDS through RSCAD runtime, the algorithm
running in the RTAC senses the fault and sends switch status
control signals to the relays through Ethernet to carry out the
opening/closing of breakers necessary to keep the critical loads
supplied based on the priority of the load. The internal logic for
close and open switch status signal from relay to respective circuit
breakers in the microgrid are set up to open/close based on the
RTAC input to the relay through hardwired connections. These
changes are captured in the RTDS through the front panel digital
interface using GTFPI card in RTDS.
been observed and results showed satisfactory performance after
fault occurrence. After reconfiguration, all the three remaining
generators (NEGP, SEGP, and SWGP) are generating power on
their maximum capacity and all the loads have been restored
except load 1 and load 4 in the system.
In the case of fault case 4, fault occurs on two buses simultaneously, BUS 1 and BUS 7. Hence two generators of 36 MW and
4 MW capacities would be isolated from the system. Transition
of load circuit breakers has been shown in Fig. 12. Similar to the
previous fault case, only half of the breakers have been shown in
this figure. As shown in Fig. 12, status of breakers 8, 9, and 14
changed from “0” to “1” to create a new path to serve the vital
load (load 2). Loads 1 and 6 are affected by fault and loads 3
and 4 are shed to keep enough power in the system to serve vital
load as expected to give satisfactory results. These results
show successful operation of real-time reconfiguration for the
microgrid system.
V. REAL-TIME SIMULATION RESULTS
Using the real-time test bed, developed reconfiguration algorithm has been tested on modeled 8-BUS SPS as an islanded
microgrid in RTDS. Two fault cases have been selected for realtime tests as case 1 and case 4 in Table III. In order to implement
the reconfiguration algorithm in real-time fashion, developed
algorithm has been modified to be compatible with RTAC
hardware. The size of an array variable can be changed dynamically in MATLAB. However, in ST, it is not possible to have
dynamic sized arrays.
RTAC monitors the fault signal and returns the initial statuses of
circuit breakers to RTDS, in case of no fault. In fault case 1, fault
occurs on BUS 1 and North West (NW) generator with 36 MW is
disconnected by relay for fault isolation. Due to lack of power, the
system needs to shed some loads to maintain the high priority loads
in the circuit. As fault occurred on BUS 1, load 1 is affected and as
load 2 is a vital load, GA reconfiguration algorithm generates
signal to shed nonvital load 4. Circuit breakers status changes are
shown in Fig. 11; however, only half of the breaker’s statuses have
been shown in this figure. Real power of generators and loads have
VI. CONCLUSION
A combined GA and graph theory reconfiguration algorithm
has been implemented in the real time using commercial controllers for simulated microgrid systems. The proposed algorithm
shows a high ability to maintain the maximum loads with respect
to priority in microgrid systems including the islanded-mode
operation. The developed algorithm has been tested on shipboard
system and CERTS microgrid using MATLAB in the offline
mode and satisfactory results have been obtained.
The proposed algorithm produces successful results, e.g., test
system using real-time test bed. The reconfiguration algorithm
was coded in the ST to be implemented in the commercial RTAC.
The proposed algorithm implementation is simple and flexible
and can be easily extended to industrial microgrid system. The
results show that developed algorithm can run for different fault
cases in real time to reconfigure the microgrid network.
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ACKNOWLEDGMENT
The authors are thankful to Schweitzer Engineering Lab
and RTDS for their support to successfully complete this
project.
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Farshid Shariatzadeh (S’10) received the B.S. degree in electrical engineering
from Ferdowsi University of Mashhad, Mashhad, Iran, in 2005, and the M.S.
degree from Khajeh Nasir Toosi University of Technology, Tehran, Iran, in 2008.
He is currently pursuing the Ph.D. degree in electrical engineering from
Washington State University, Pullman, WA, USA.
His research interests include applications of intelligent algorithms to microgrid power systems, smart grid power systems, real-time implementation for
power systems, and the electricity market.
Ceeman B. Vellaithurai (S’12) received the B.E. degree in electrical and
electronics engineering from Anna University Tiruchirappalli, India, in 2011.
He received an award for the best outgoing student for his academic achievements
from Anna University Tiruchirappalli. He received the M.S. degree in electrical
and computer engineering with specialization in power systems from Washington
State University, in 2013.
His research interests include real-time modeling and simulation of cyberpower system.
Saugata S. Biswas (S’11) received the B.E. degree in electrical engineering from
Nagpur University, Nagpur, India, in 2007. He is currently pursuing the Ph.D.
degree in electrical engineering from Washington State University.
SHARIATZADEH et al.: REAL-TIME IMPLEMENTATION OF INTELLIGENT RECONFIGURATION ALGORITHM FOR MICROGRID
He worked as the Executive Design Engineer in the Design and Development
Department of a leading switchgear manufacturing company in India from 2007
to 2009. His current research interests include real-time power system analysis
at the transmission level, testing and analyzing synchrophasor device performance and applications, and substation automation.
Mr. Biswas is the recipient of the Gold Medal award from Nagpur University
for his academic achievements during 2003–2007.
Ramon Zamora (S’09) received the B.S. degree in electrical engineering
from Bandung Institute of Technology, Indonesia, in 1998, and the M.S. degree
in electrical engineering from the University of Arkansas, Fayetteville,
in 2008. He is currently working toward the Ph.D. degree in electrical engineering
from Washington State University.
He has been a Lecturer with the Electrical Engineering Department of
Syiah Kuala University, Banda Aceh, Indonesia, since 1999. He is currently
on leave to pursue the Ph.D. degree. His current research interests include
distributed generations, microgrid controllers, and multiagent system applications in power systems.
607
Anurag K. Srivastava (S’97–SM’08) received the Ph.D. degree from
Illinois Institute of Technology (IIT), Chicago, in 2005, the M.Tech. degree
from Indian Institute of Technology, Kanpur, India, in 1999, and the B.Tech.
degree in electrical engineering from Harcourt Butler Technological Institute,
Kanpur, India, in 1997.
He is working as an Assistant Professor at Washington State University,
Pullman, WA, USA, since 2010. In the past, he worked as an Assistant Research
Professor at Mississippi State University, from 2005 to 2010. Before that,
he worked as a Research Assistant and Teaching Assistant at IIT, Chicago, IL,
USA, and as a Senior Research Associate with the Electrical Engineering
Department, the Indian Institute of Technology, and as a Research Fellow wirh
the Asian Institute of Technology, Bangkok, Thailand. His research interests
include power system security, smart grid, real-time simulation, electricity
market, and artificial intelligent application in power systems.
Dr. Srivastava is a member of IEEE Power and Energy Society (PES). He
serves as a past-chair of the IEEE PES career promotion committee and a chair of
the student activities subcommittee. He is a recipient of several awards, serves as a
reviewer for several international journals, and is an editor of IEEE TRANSACTIONS
ON SMART GRID.