Islanding Detection and Controlled Islanding in Emerging Power Systems Key Issues and Challenges

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072
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Islanding Detection and Controlled Islanding In Emerging Power
Systems Key issues and challenges
Srinu Babu Matta1, Seethalekshmi K2
1 Research Scholar, Electrical Engineering Department, Babu Banarasi Das University,
Lucknow, Uttar Pradesh, India
2 Professor, Electrical Engineering Department, Babu Banarasi Das University,
Lucknow, Uttar Pradesh, India
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Abstract - Power system is continuously growing and
becoming a system that is more complex day-by-day and
throwing a different challenge every day. With the evolution
of interconnected large and complex power grid, addition of
large number of micro grids and with the ever-increasing
diversity of power transmission systems due to changing
power market competition, ageing infrastructure, the
stability and safe operation of such power systems are
stretched to their limits. This calls for an effective islanding
technique of a power system to prevent the occurrence of a
bulk-area blackout and/or a cascading failure. An Islanding
detection of the power system, its controlled and adaptive
islanding with a minimum power mismatch between
separated islands are of utmost importance for reliable
power system operation and its control.
This paper describes the various aspects of power system
islanding, islanding detection techniques (IDT) and
controlled islanding schemes (CIS). This report also
describes several test methods, key issues and challenges of
IDT and CIS.
Key Words: Controlled Islanding, Smart Grid, WAM
systems, Islanding Detection, Adaptive Islanding, and
Emergency Control.
1. INTRODUCTION
Power system, in today’s scenario, includes large
power plants operating in the synchronized mode and the
transmission of power through long transmission and
distribution network. With the evolution of interconnected
large and complex power grid, addition of large number of
micro grids and with the ever-increasing diversity of
power transmission systems due to changing power
market competition, ageing infrastructure, the stability
and safe operation of such power systems are stretched to
their limits and is facing major challenges. Specially,
natural calamities or severe disturbances, like
earthquakes, tsunamis, hurricanes, human operational
errors, ransom ware, ageing infrastructure and spam ware
attacks, tend to unstabilize the power system and can even
trigger cascading power outages. Such cascading outages
collapses the power system rapidly, resulting into a
significant disruption to people, industries, institutions,
emergency services and other users at large. India
witnessed two back-to-back severe blackouts on 30th and
31st July 2012, which affected the largest number of people
in the history of power systems across the world. These
blackouts were caused by the tripping of a certain
transmission line between the northern and western
power system networks of India and led to the breakdown
of most of the Indian power system. [38]
The application of decentralized approach, distributed
Energy Resources (DER) i.e. small or medium capacity
power plants, micro grids, multiple Distributed Generators
(DG) etc., in the transmission and distribution system is
becoming a common practice with the increase in size of
power system. The basic advantages of DER’s is
environmental beneﬁts, increased efﬁciency, avoiding
upgrading of transmission and distribution (T&D) capacity,
good power quality, effective protection and control and
reduced T&D line losses. However, several problems
should be tackled prior to the application of DER units to
the networks. These problems include frequency
stabilization, voltage stabilization, intermittency of the
renewable resources and micro grids and power quality
issues.
A thorough review of detection of various out-of-step
condition (OOS) in power transmission system is dealt with
in detail in [39]. This review paper covers,
- Basic understanding of power system islanding, its
associated hazards and benefits
- Islanding detection techniques. Key challenges and
advantages
- Controlled Islanding schemes
The micro grid (MG), which is caused by the
disconnection from the main grid without stopping the
energy generation from the DG sources. This kind of
disconnection from main source is called islanding, which
can be either intentional or unintentional. The very need of
intentional islanding is to create a “power island” in the
event of a fault. To localize fault to the possible extent.
However, the active part of the remaining main source
would stabilize at a new operating point.

The “Unintentional islanding” is caused due to heavy
power swings or unstable faults in the system. This paper
highlights various techniques to identify these kind of
unintentional islands and various schemes for controlled
and adaptive islanding to keep the mismatch in active
power between the separated islands and the main grid
and the to the reasonable and optimal level. The gist of the
literature survey of last two decades on the IDT and CIS are
presented in this paper.
1.1 What is Islanding?
A fault in the power system is sensed and cleared by the
protective relays and switchgear that are located nearest to
the fault. In case of a fault, a DG supplies its power to the
separated part of the distribution system. Mostly, DG
experiences an overloaded condition, under or over voltage
or frequency condition and it finally leads to stoppage, in
turn, causing brown out or black out of the system.
However, it is a rare case. A DG feeding this islanded
system has a generating capacity that is adequate to cater
to the loads connected to the islanded system. The
situation in which the power to the loads is fed only from
the DGs even after isolation of supply from the main power
source is called an "islanded operation" or "islanding".
Controlled islanding is a process of dividing an
interconnected power grid into smaller electrically
independent systems having fair balance between
generation and load. It is the last-resort to cope up with
many technical disturbances including unstable systems,
undamped oscillations, voltage collapse, cascading trips,
etc. [41, 42]. The rationale behind the process of
controlled islanding is that a smaller power system is easy
to control and stabilize islands. Also, the islanding
operation can isolate faulty part of the system from
healthy part of the system.
1.2 Hazards of Islanding
There are potential hazards associated with Islanding
in power systems.
 The voltage and frequency may not remain within
a permissible range due to sudden mismatch is
active power between the main power source and
islanded network. 
 If proper LOTO (Lock out and Tag out) system is
not followed, the safety of line workers can be
compromised by the availability of DG power
supply after opening and tagging out of primary
sources. 
 The islanded system may be inadequately
grounded with the new network and the
associated DG interconnections.
 If Islanding is not controlled properly and
instantaneous reclosing is opted out, it could result
in out of phase reclosing of DG, which may cause
damage to the generators due to the development
of excess mechanical torques and currents.
If a condition of islanding operation is initiated because
of some fault in the system then actually it is supposed to
be isolated from any power supply to attend the fault. But,
the fault could get continuously powered and ultimately
lead to aggravating the fault, equipment failure, unsafe
situation or some cascading effects because of auto
islanding or MG power availability to the islanded
network,.
The Islanding system should be smart enough to trip
further if the power quality and power condition within the
islanding system is not as per acceptable limits. Early
detection of islanding condition helps in deriving better
control measures and, in turn, reduce and mitigate the
hazards associated with islanding,
2. ISLANDING DETECTION METHODS
The main philosophy behind detecting an islanding
condition is to determine the occurrence of an islanding
situation by monitoring the DG output parameters,
transmission line’s system parameters and their changes.
 In the literature, various techniques been presented to
detect islanding situation. All these techniques are
systematically organized for better understanding in this
review paper. These IDT is broadly classified into central
or remote and local methods. Figure. 1 shows the
classification of the IDT. The details of all techniques are
explained in subsequent subsections in detail.
Figure. 1 : The classification of the IDT
2.1 Remote or Central Islanding Detection
Techniques
2.1.1 System State Monitoring
The method of determining system states through
monitoring of the system state by modelling the power
system network having lesser number of state

measurements is called System State Monitoring.
Unintentional islanding can also be detected effectively
through this method by devising proper instrumentation,
measurement and controlling systems [1]. Once the
parameters such as voltage, current and frequency are
detected from the disconnected area, the islanding
situation is detected. This method is derived from a
complementary of the supervisory control and data
acquisition (SCADA) systems, called Distribution
Management System (DMS).
This technique was applied and tested in bulk power
system networks, Micro grids, PV system etc. The
requirement of separate instrumentation and
communication equipment for each of the strategic power
system equipment makes this method expensive. The
problem of the high implementation cost, especially for
small systems, calls for using other techniques. As the
number of DGs connected the distribution network
increases, the real-time monitoring of voltage of each
generator becomes difficult [2]. SCADA is also used to
monitor system state parameter and is used for
controlling either through manual intervention or
automatically.
2.1.2 Transfer Trip Scheme
The transfer trip scheme requires monitoring of the
status of the circuit breakers and recloses that are
involved in islanding a distribution system. SCADA
systems can be used for such an application [3]. An
effective communication system between the power
system grid and DGs is required in this method. Therefore,
the cost of implementing this system is, often, high.
However, the method has a complexity cost because of the
growth of the system complexity, where the transfer trip
becomes outdated and requires relocation or updates.
2.1.3 Intertripping
The method comprises of transmitting of the signal of
opening of a contact at the points of disconnection to the
generating units supporting the respective islanded zones.
Intertripping also requires communication between the
measurement system and generating units. The reliability
of the system is high because it provides accurate
solutions but it is, as such, uneconomical [4, 5].
Table 1 : Remote or Central Islanding Detection Techniques – Key issues and challenges
2.2 Local detection techniques
Local techniques use measurement of the system
parameters taken from the local power system or DG site
to detect islanding. The required system parameters are,
 Voltage
 Frequency
 Current
 Harmonics
Lot of research work done using local parameters is
reported in the literature. An overview on local islanding
techniques is detailed in this subsection. The statistics
reveals that active and passive techniques have rapidly
increased their practical implementation over the years.
2.2.1 Passive Detection Techniques
Passive techniques involve monitoring of the system
parameters such as voltage, frequency, harmonic
distortion, and current on the DG end at the point of
common coupling (PCC) with the main power grid or with
the strategic locations of the main power grid. These
parameters undergo significant change during islanding.
Generally, the parameters like frequency, voltage, and its
rate of change [6-12] are used to detect islanding
Methods Advantages Disadvantages Improvements
SCADA system
(System State
Monitoring)
 Communicate
with all DG
 Cost implementation
high.
 2.Difficult to fix
--
Transfer Trip
Scheme
 System
Concept
 Avoid NDZ
 Expensive
 Complicated
 Direct transfer trip can
avoid islanding
Wired cable or
Non wired
 Easy to
implement
 Cost concern for DG
below 2 MW (Cost
impact can goes up to
50% of total connection
cost.
 Needs any possible media
to transfer the signals
 Concerns in remote inter
trip signals, where the
communication media is
outside the control of user.

conditions. Various conventional and popular passive
islanding detection techniques are mentioned in Figure 2.
Figure 2 : Classification of Passive Techniques of
IDT
2.2.1.1 Under/Over voltage and Under/Over
Frequency:
The under/ over voltage (UVP/OVP) and under/over
frequency (UFP/OFP) protection relays used in this
method are installed on a distribution feeder to determine
the abnormalities in these parameters. These relays are
used to monitor the deviation of the grid
voltage/frequency beyond the permissible limits [13]. This
technique is usually used at all grid connected DGs and
critical nodes of power system network. These protection
methods are dependent on the power flow between the
main power grid and the DGs or strategic nodes of power
system, which refers to active power and reactive power.
Strengths: The OVP/UVP and OFP/UFP relays are, in
any case, required otherwise also for power system
protection apart from islanding detection. This is a low-
cost technique. Also, this method prevents damage to the
equipment from out of operational specification
conditions [39].
Weakness: The primary weakness of this method is
its comparatively large no detection zone (NDZ), as
discussed below. Also, this method has the drawback of
variable or unpredictable reaction times of these
protective devices.
NDZ: If (1)
The change in the amplitude or frequency, in the event
of disconnection of the main power grid, is not sufficient to
actuate any of the standard over/under voltage or
frequency relays. In such a case, the DG power generation
matches the load power requirement, and the load has a
unity power factor at the line frequency, P and Q being the
active and reactive power of the load, respectively.
There is high probability that and ∆Q will fall into
the NDZ of the OVP/UVP and OFP/UFP. The normal
over/under voltage and frequency relays alone will be
ineffective for anti-islanding protection. space
can be found in the literature. As an example, tThis
literature explains the NDZ of the over/under frequency
protective devices in the RLC load space, which is shown
in the figure below. ∆Q can be calculated using the
equation for reactive load and resonant frequency:
{ } (2)
The fundamental frequency is given as:
√
⁄ (3)
The NDZ for changes in voltage and frequency is
shown in Figure 3.
Figure 3 : NDZ for changes of voltage and
frequency.
The NDZ is mapped in versus ∆Q Space as,
(4)
(5)
2.2.1.2 Voltage Phase Jump detection
In Phase jump detection (PJD) method, the phase
difference between the terminal voltage and output
current of the DGs, PV systems etc. for a sudden “jump” [1]
is monitored. If islanding occurs, the inverter and local
load gets disconnected from the main system. The PJD
technique detects islanding through a rapid change in
phase angle. This method has easy implementation in case
of PV, Wind turbine based DERs because only modifying
the phase locked loop (PLL) used in the inverters for main
power grid synchronization is needed. The capability to
deactivate the inverter is only required when the phase
errors exceed some threshold set value set wrt the system
conditions. The power quality of the inverter remains
unaffected in this method and it can be used in DER’s
based on PV (Photo Voltaic), Wind turbines (WT) systems,
etc. [14].

Figure 4 : Basic operation of Phase Jump Detection
Strength: A major strength of PJD is its ease of
implementation. The effectiveness of PJD is effective in
case of multiple inverter based DGs connected to the
island.
Weakness: The thresholds that provide effective
islanding detection are difficult to determine. The starting
of certain highly inductive and dynamic loads like heavy
motors, may cause significant transient phase jumps, and
may lead to nuisance tripping of the PV, WT based DGs if
the thresholds are set too low
NDZ: When the utility is disconnected, a phase error
will not be generated in case of a load with a zero phase
angle at the main power grid frequency. Thus, PJD has an
NDZ within the passive standard of main power system
over/under protection devices.
2.2.1.3 Harmonic Measurement
The detection of harmonics requires measurement of
the harmonics (line voltages and currents) [15, 16]. The
change of total harmonic distortion (THD) at the Point of
coupling of the DGs and strategic points in the main power
system [17] is monitored in this method. If the THD
exceeds a defined threshold, the protection system will
disconnect the DGs from the main power grid. However,
selecting a trip threshold, which does prevents nuisance
tripping of the DGs, is easy because the distortion level
rapidly changes as the nonlinear load switches on and off
particularly in case of DGs based on PV, WT etc.[18].
Strength: Islanding can be detected effectively under a
wide range of conditions [19] through this method. This
method works well in case of multiple-DGs in the system.
Weakness: The thresholds that provide effective
islanding detection are difficult to determine. It is clear
that a threshold must be adaptive and selective that is
(a) Higher than the THD, that may exist in the grid
voltage.
(b) Lower than the THD that may be developed
during islanding by either of two mechanism
described above.
NDZ: This method fails in case of loads having strong
low-pass characteristics, i.e., loads with a high quality
factor Q, and for loads that may occur with a service
entrance disconnect that do not include a transformer
inside the island.
2.2.1.4 Voltage Unbalance
The voltage unbalance (VU) generally varies because of
the topographical changes of the networks and the load
despite the small changes in the DG loading [21-23].
Therefore, effectively detecting the islanding operation is
possible if the unbalance of the three-phase output
voltages of the DG and strategic node of main power grid
are continuously monitored. This technique is useful in
the situation where disturbances occurs in the main
power grid [24].

Table 2: Islanding Detection – Passive Techniques: Key issues and challenges
2.2.2 Active Detection Techniques
These
techniques are mostly preferred these days because of
their real time based approach. Various active islanding
detection techniques with non-artiﬁcial intelligent
techniques exist, some of the popular techniques are
discussed in this subsection. [21, 22, 25].
Figure 5: Classification of Active Techniques of IDT
2.2.2.1 Impedance Measurement
In the impedance measurement based technique, it
measures the system impedance changes caused by
islanding. An active direct method, however, involves
connecting a shunt inductor across the supply voltage,
intermittently, and the power system source impedance,
detailed in [21, 22] is calculated from the short circuit
current and supply voltage reduction. A large number of
impedance detection methods been proposed because of
the belief that this method has no NDZ in the single PV or
WT based DG case.
√ (6)
Given the parallel RLC load, however, the major trouble
for impedance detection is caused by high-Q loads. Rate of
change of Impedance and difference in rate of change of
impedance at sending end and receiving end of the
transmission line is used as a basis for deciding upon the
islanding situation. [40].
2.2.2.2 Resistance Measurement
In this technique, swing detection algorithm is used to
detect the islanding situation [40]. The Figure 6 depicts
the working flow chart of the Resistance measurement
based technique.

Figure 6. Flowchart for the resistance measurement
based IDT
This Swing detection element, checking criteria to
ascertain the out of step condition during stable or
unstable power swings. The algorithm comprises of four
types of sub-elements as shown here, namely “Magnitude
of change detection element “, “Rate of change detection
element”, “differential magnitude of change detection
element” and “Differential rate of change detection
element”.
All the parameters like T1 to T4, K1 to K4, R1set, R2set,
Tmax and T are precisely set after conducting thorough
study of the system configuration & its transient analysis.
If either of the “Magnitude of change detection element
“and “Rate of change detection element” sets along with
either of the “differential magnitude of change detection
element” and “Differential rate of change detection
element”, then only the Swing Detection Element gives a
positive output, this signal can be used for controlled
tripping or for any power shedding application etc.
Figure 7. Algorithm of Swing Detection Element
The response of this technique is very fast and reliable.
However, the arriving on a certain threshold values and
timer setting is challenging with the changing power
system conditions.
2.2.2.3 Slip Mode Frequency shift (SMS)
SMS uses positive feedback for islanding detection,
namely, amplitude, frequency and phase. The method
comprises of shifting the phase and the short-term
frequency by applying a positive feedback to the phase of
the voltage. The SMS is used to detect the islanding
condition because of the easy implementation of the
method caused by the involvement of only a slight
modiﬁcation of a required component. SMS also prevents
islanding effectively and it is having small NDZ compared
with other active techniques [26].
2.2.2.3.1 Active Frequency Drift (AFD):
The principle of the active frequency drift (AFD) or
frequency bias method is used in PV, WT based DG system.
In this method, the frequency of the inverter current is
accelerated [2, 27] by forcing variations in the inverter
output using positive feedback. The AFD uses the
waveform of the inverter current, which is injected into
the PCC with the main power grid. The waveform drifts of
the grid is not present as a stabilizing inﬂuence. The
advantage of AFD is the ease of implementation in PV or

WT based DGs. However, all inverters of the PV based
micro grid should possess identical AFD, otherwise, it
would fail to detect islanding conditions in the multiple
inverter case [6].
2.2.2.3.2 Sandia Frequency Shift (SFS)
The SFS method, normally termed as AFDPF, is derived
from the AFD and it uses positive feedback for islanding
detection [17]. SFS is also used to detect the deterioration
of the islanding condition. This method’s performance of
the SFS method is notably dependent on the optimal
design of its parameter. The optimal setting of the SFS
islanding detection parameters with a multi DG system is
vital to eliminate the NDZ condition.
2.2.2.3.3 Sandia Voltage Shift (SVS):
The SVS, based on positive feedback, is a method of
islanding detection, which applies positive feedback to the
amplitude of the voltage. SVS is most effective in islanding
prevention among AFD and SFS methods. However, SVS
may produce minimal effects on the main power grid
system’s transient response and power quality. An active
technique was used by adding the disturbance current
through the voltage-source control (VSC) to detect the
islanding condition effectively. The variation of the time-
varying amplitude of the PCC voltage is also determined
when the disturbance remains constantly unchanged [10].
This method is faster in detecting islanding and has a
smaller NDZ than the normal technique if the suitable
accelerating factor is selected, the only gap with this
technique is selection of rightful accelerating factor under
dynamic power system conditions.
Table 3: Islanding Detection – Active Techniques: Key issues and challenges
2.2.3 Hybrid Techniques
The hybrid technique is a combination of the active and
passive IDT’s. Various Hybrid techniques are discussed in
this subsection.
Figure 8. Classification of Hybrid Techniques

2.2.3.1 Voltage unbalance and frequency set point
Hybrid method is based on the positive feedback (PF)
and the voltage unbalance and total harmonic distortion
(VU/THD) techniques, wherein, the drawback of both
techniques are cancelling each other when simultaneously
applied [28]. The calculation of the VU for each of the DG is
used instead of the THD, because the VU is more sensitive
to disturbance than THD. Therefore, any disturbance
applied to DGs could produce a spike in the VU. This
technique also efficiently discriminates between the
islanding condition and the event of switching of load.
2.2.3.2 Technique based on voltage and real
power shift
This technique uses an average rate of the voltage
change ROCOV (passive technique) and a real power shift
(active technique) to overcome the limitations of the
active and passive techniques in islanding detection [29].
The technique can detect the islanding condition with
multiple DG units operating at unity power factor.
However, the real power shift (RPS) is applied to the
system if islanding condition is not detected by the passive
technique. RPS can eliminate the injecting disturbance
from time to time to detect islanding similar to other
active techniques. However, RPS only can change the real
power of the DG at the unity power factor. In the proposed
method, only one DG changes the real power compared
with the positive feedback technique, where all the DGs
work together to inject perturbation in the system [29].
2.2.3.3 Voltage Fluctuation Injection
As the name of this technique goes, it is based on the
voltage fluctuation injection, using a high impedance load.
The islanding detection correlation factor (CF) is proposed
for small-scale DGs. The two-stage method, which is the
passive technique (rate of change of frequency
(ROCOF)/rate of change of voltage (ROCOV)) for the
protection scheme, and the active technique (CF) as a
backup is applied to attain higher effectiveness of the CF of
the distribution synchronous generator and to accurately
decide between islanding and non-island disturbances
[30].
2.2.3.4. Hybrid SFS and Q–f islanding technique:
The SFS and Q–f curves are proposed based on
islanding detection to improve the SFS and reduce the NDZ
[31].
The optimum SFS is calculated using the analytic
formula, where the bacterial foraging algorithm is being
used to search the optimal gain of the SFS to eliminate
NDZ. The Q–f droop curve technique is then added to
increase the effectiveness and the responsiveness of the
SFS- based islanding detection.
Key issues and advantages with various Islanding
detection techniques tabulated in Table 4.
2.3 SIGNAL PROCESSING METHODS
It is used for identification of islanding situation. Quick
and accurate detection of the islanding condition is of
utmost importance. The implementation of the signal
processing technique assists in detecting the islanding
condition. The hidden features extracted through signal
processing can then serve as inputs to the artificial
intelligent (AI) classifier, which helps in detecting the
islanding and non-islanding conditions.
Common AI classifiers or machine learning based
classifiers used in islanding detection include the decision
tree (DT), rule based techniques, artificial neural network
(ANN), probabilistic neural network (PNN), fuzzy logic
(FL), and support vector machines (SVM). The signal
processing methods present various features, such as
time-frequency distribution (TFD) of a time series, which
eases the analysis and quantification of the signals
regardless of the succeeding classification technique. The
linear TFD techniques are popularly implemented in
determining the islanding condition because such
implementation is faster than those of non-linear methods.
Some of the SP tools used in islanding detection are
described in the following sections The islanding detection
techniques that use signal processing and the succeeding
AI classifiers or machine learning classifiers are discussed
further in the following subsections.
Figure 9. Classification of Signal Processing Methods

Table 4: Islanding Detection techniques: Key issues and challenges
2.3.1. Wavelet-transform (WT)
The wavelet theory is the mathematical model for non-
stationary signals with a set of components in the form of
small waves called wavelets or Burst mode transform [32].
The wavelets are generated from one original wavelet
called the mother wavelet, which is then further extended
to allow the wavelet to analyze the non-stationary signals
in the frequency band. WT can be either continuous (CWT)
or discrete (DWT). Using a wavelet is advantageous
because the wavelet does not need to assume the
stationary or periodicity of signal. The WT carries
information about time and frequency simultaneously is
capable of simultaneously comprehending time and
frequency information because wavelet has long windows
at low frequencies and short windows in high frequencies.
Therefore, WT can be used for islanding detection by
analyzing the discontinuities and transients in time-
varying signals. CWT is applied using an online measuring
technique for the voltage in DG units and strategic nodes
of the main power grid [33].

2.3.2. S-transform (ST)
WT suffers from the problem that it is unable to detect
islanding conditions under a noisy condition. [34-36]. In S-
transform, which is a modified form of WT, the real and
imaginary spectra of a frequency-dependent resolution
are localized by producing a time-frequency
representation of a time series. . S-transform eliminates
the presence of noise by providing multi-resolution but
the absolute phase of each frequency component is
retained.
The S-transform was used to extract the negative
sequence voltage during an islanding event. The energy
content and standard deviation of the S-transform contour
was clearly shown in detecting islanding events and
disturbance because of load rejection in [37]. Calculation
of the negative sequence voltage and current through the
S-transform and spectral energy content was done.. S-
transform requires more computation time and memory
for signal processing, which is a drawback 35-36]
3. CONTROLLED ISLANDING
Controlled islanding of power system prevents wide-
area blackouts and/or cascading failures, this helps in
maintaining the stability of power systems under
contingencies. Its main purpose is to localise the faulty
zones, avoid cascading failures and reduce the impact of
disturbances by limiting out-of-step conditions. WAMS in
power networks provide time stamped measurements and
which can be used for faster and reliable controlled
islanding.
Several approaches proposed for controlled islanding
in the literature.
Figure 10. Classification of Controlled Islanding
schemes (CIS)
The system is split into self-sufficient islands through a
slow coherency approach [43-45]. The rate of change of
frequency (ROCOF) is monitored to arrive upon a load-
shedding scheme, thus preventing frequency fluctuation in
heavily loaded islands..
Peponides et al. [46] proposed to identify slow
coherence in the power system through the singular
perturbation technique. The system’s working mode can
be classified broadly into slow mode and fast mode. When
a disturbance occurs, the dynamic system is in the fast
mode, which gradually gives way to the slow mode.
Incorporating external constraints like limited island
zone or constructing some simpler criteria like degree of
generators’ coherency or dynamic coupling in the pre-
islanding system state are detailed in [47-49]. In [50-51]
papers, stability constraints and other key parameters
were not considered and mostly focused on minimization
of side effects of the islanding process and only a single
aspect like load shedding or combination of other system
aspects.
In [52], an improved slow coherence method was
described and used to obtain an islanding strategy and in
[53-54], the problem of controlled islanding was resolved
through the graph-theoretic method. This type of method
involves identification of the minimal cut-set and the
weak link with graph simplification approaches [55-56]
and depth-first searching methods.
a two-step controlled islanding algorithm was
proposed in [47],, where spectral clustering was used to
find a favourable islanding solution, but the generator
coherency after disturbances is not considered, as the
coherency changes before and after a major disturbance.
Here, the drawback is that, it only considers the absolute
value of active power, ignoring the direction of power
flows and the influence of electrical distances between
generators, whereas these are important parameters for
optimal controlled islanding.
There are several research papers on different
methods for determination of the optimal islanding
boundaries on real-time basis apart from the slow
coherency-based islanding strategy. Sun et al. [43, 57]
introduced and extended the ordered binary decision
diagram (OBDD) method using power system transient
simulation to split the power system into stable islands to
identify the islanding cut-set and analyse the minimum
power mismatch. In [58-60], researchers used intelligent
optimization algorithms, such as the ant searching
mechanism and particle swarm optimization to search for
proper islanding solutions with a minimum mismatch of
power flow. In [62], Trajectory similarity based coherency
approach is dealt with along with entropy weight and used
in controlled islanding. In [61], WAM based controlled
islanding has been proposed, which has Variable
Neighbourhood. Heuristic approaches are used
additionally for optimised power mismatch. Proposed

approach has been compared with this approach and it
was found that the power mismatch in the proposed
approach is lower and faster in response time.
The advent of Phasor Measurement Unit (PMU) with
time stamped Global Positioning System (GPS) enabled
Wide Area Measurement System (WAM) eliminated
challenges from the geographic spread, separation of
power systems and also measurements are able to give
real time power system phasors at a rate of 20-60 phasors
/ second [63-64]. WAM has been broadly deployed in
power systems worldwide these days. WAM enables the
dynamic measurements of current’s, voltages, parameters
of generators etc. and helps developing fasters and reliable
solution for controlled islanding problem.
A new Adaptive and WAMS-based three stage
controlled islanding strategy for large interconnected
power systems is proposed in [65]. In the First stage,
signal-characteristic-analysis is undertaken to
discriminate a power fault condition from other power
quality issues. Four similarity indexes associated with the
generators’ trajectories were used to find out coherent
Generators. In the second stage, an improved-Laplacian-
Eigen-map algorithm (ILEA) to identify the distance
measured in kernel space between generators has been
used. Hierarchical clustering used for easy representation
of Generators coherency. In the third stage, utilized k-way-
partitioning (KWP) and Graphical-Spectral-Partitioning
algorithms to obtain separation surfaces and least power-
mismatching islanding lines. This scheme was tested on
IEEE 39-bus system, its results are better than OBDD and
WAM based coherency-based scheme and it is
automatically adapting to different fault modes and giving
optimum islanding.
4. CONCLUSIONS
In this paper, an extensive analysis and review of
literature (more than 110 papers) of last two decades on
the Power system islanding, Islanding detection and
controlled islanding are presented in a comprehensive and
easily understandable manner. The various systems of
islanding detection techniques including centralised or
remote and local, conventional and modern techniques in
a power system transmission and distribution system are
presented in detail. Various intelligent system applications
in islanding detection have also been discussed along with
their key challenges and advantages. An overview of
controlled islanding schemes (CIS) is also presented.
This paper provides an unbiased description of each of
the islanding detection techniques (IDT) and controlled
islanding schemes (CIS) along with discussions of the
strengths, weaknesses and non-detection zone
characteristics for the convenience of readers and also to
create a broader spectrum of understanding.
REFERENCES
[1] Balaguer-álvarez I and Ortiz-rivera E., “Survey of
distributed generation islanding detection methods”,
IEEE Latin America Transactions 2010,vol. 8, pp.565–
570.
[2] Yin, J, Chang, L and Diduch, C., “Recent developments
in islanding detection for distributed power
generation”, Power engineering, 2004, pp. 124–128.
[3] M. A. Refern, O. Usta, and G. Fielding, “Protection
against loss of utility grid supply for a dispersed
storage and generation unit,” IEEE Transaction on
Power Delivery, vol. 8, no. 3, pp. 948-954, July 1993.
[4] Chowdhury S, Chowdhury S and Crossley P., “Islanding
protection of active distribution networks with
renewable distributed generators: a comprehensive
survey”, Electric Power Systems Research, 2009, pp.
984–92.
[5] Ecconnect, “Assessment of islanded operation of
distribution networks and measures for protection”,
Polymercontents, vol.18, 2001, pp.830–877.
[6] Kunte, R, and Gao, W, “Comparison and review of
islanding detection techniques for distributed energy
resources”, 40th North American power symposium,
2008, pp.1–8.
[7] Moradzadeh, M, Rajabzadeh, and M, Bathaee, M, “A
novel Hybrid islanding detection method for
distributed generations”, Third international
conference on electric utility deregulation and
restructuring and power technologies, 2008, pp.2290–
2295.
[8] Zeineldin H., and Salma M., “Impact of load frequency
dependence on the NDZ and performance of the SFS
islanding detection method”, IEEE Transaction on
Industrial Electronics 2011, pp.139–46.
[9] Yoo, C, Jang, D, Han, S, Oh, D and Hong, S., “A new
phase drift anti-islanding method for grid-connected
inverter system”, Eighth international conference on
power electronics—ECCE Asia, 2011, pp.902–906.
[10] Li P, Sheng Y, Zhang L, Yang X and Zhao Y., “A
novel active islanding detection method based on
current disturbing”, Electrical Machines and Systems
2009, vol.1, pp.1–5.
[11] Lee S., and Park J., “New islanding detection
method for inverter-based distributed generation
considering its switching frequency”, IEEE
Transaction on Industry Applications 2010, 46,
pp.2089–2098.
[12] Yu B, Matsui M, and Yu G., “ A correlation based
islanding detection method using current magnitude
disturbance for PV system”, IEEE Transaction on
Industrial Electronics 2010,58, pp.2935–43.
[13] Timbus, A, Oudalov, A, and Ho, C., “Islanding
detection in smart grids”, Energy conversion congress
and exposition (ECCE), 2010, pp.3631–3637.
[14] Hu W, and Sun Y., “A compound scheme of
islanding detection according to inverter”, Asia-Pacific

power and energy engineering conference, 2009,
pp.1–4.
[15] Khamis A, Armstrong M, Sulaiman M, and Rahman
A., “Location of embedded generation installation at
long distribution line feeder due to harmonic
performances”, IJSSST, 2009, Vol. 10.,pp.26–33.
[16] Khamis, A, Armstrong, M., and Sulaiman, M, “The
impact of embedded generation due to harmonic
performance”, Third Asia international conference on
modelling and simulation, 2009, pp.514–519.
[17] Teoh W., and Tan C., “An overview of islanding
detection methods In photovoltaic systems”, World
Academy of Science Engineering and Technology,
2011, pp.674–82
[18] Yin, GA, “Distributed generation islanding
detection method based on artificial immune system”,
IEEE/PES transmission & distribution conference&
exposition -Asia and Pacific,2005, pp.1–4.
[19] Kobayashi, H., Takigawa, K., and Hashimoto, E.,
“Method for Preventing Islanding Phenomenon on
Utility Grid with a Number of Small Scale PV Systems”,
Proceedings of the 21st IEEE Photovoltaic Specialists
Conference, 1991, pp.695-700.
[20] Ropp, M., “Design Issues for Grid-Connected
Photovoltaic Systems”, Ph.D. dissertation, Georgia
Institute of Technology, Atlanta, GA, 1998.
[21] Mahat P., and Bak-Jensen B., “Review of islanding
detection methods for distributed generation”, Third
international conference on electric utility
deregulation and restructuring and power
technologies,2008, pp.2743–2748.
[22] Mahat P, Chen Z., and Bak-jensen B., “Review on
islanding operation of distribution system with
distributed generation”, Power and energy society
general meeting,2011, pp.1–8.
[23] Jang S, and Kim K., “An islanding detection method
for distributed generations using voltage unbalance
and total harmonic distortion of current”, IEEE
Transaction on Power Delivery,2004,19, pp.745–752.
[24] Thomas M, and Terang P., “Islanding detection
using decision tree approach”, Power Electronics,
Drives and Energy Systems (PEDES),2010, pp.1–6.
[25] Bae B, Jeong J, Lee J, and Han B., “Islanding
detection method for inverter-based distributed
generation systems using a signal cross-correlation
scheme”, Journal of Power Electronics 2010,10,
pp.762–768.
[26] Zeineldin H, El-Saadany E, and Salama M., “Impact
of DG interface control on islanding detection and non
detection zones”, IEEE Transactions on Power
Delivery,2006,21, pp.1515–1523.
[27] Hanif M, Street K, and Gaughan K., “ A discussion
of anti-islanding protection schemes in corporate in a
inverter based DG”, Environment and Electrical
Engineering (EEEIC),2011,pp.1–5.
[28] Menon V, and Nehrir M., “A hybrid islanding
detection technique using voltage unbalance and
frequency set point”, IEEE Transactions on Power
Systems, 2007,22, pp.442–448.
[29] Mahat P, Chen Z, and Bak-jensen B., “A hybrid
islanding detection technique using average rate of
voltage change and real power shift”, IEEE
Transactions on PowerDelivery,2009,24,pp.764–771.
[30] Chang WA., “Hybrid islanding detection method
for distributed synchronous generators”, The In
ternational power electronics
conference,2010,pp.1326–1330.
[31] Vahedi H, Noroozian R, Jalilvand A, and
Gharehpetian G., “Hybrid SFS and Q–f islanding
detection method for inverter-based DG”, IEEE
international conference on power and energy
(PECon2010),2010, pp.672–676.
[32] Polikar R., “The story of wavelets”, IMACS/IEEE
CSCC'99 Proceedings, 1999, pp.5481-5486
[33] Zhu Y, Yang Q ,Wu J, Zheng D, and Tian Y., “A novel
islanding detection method of distributed generator
based on wavelet transform”, Electrical Machines and
Systems-ICEMS2008, 2008, pp.2686–2688.
[34] Ray P, Mohanty S, Kishor S, and Dubey H.,
“Coherency determination in grid- connected
distributed generation based hybrid system under
islanding scenarios”, IEEE international conference on
power and energy- PECon2010,2010,pp.85–88.
[35] Ray P, Kishor N, and Mohanty S., “S-Transform
based islanding detection in grid- connected
distributed generation based power system”, IEEE
international energy conference, 2010, pp.612–617.
[36] Ray P, Mohanty S, and Kishor N., “ Disturbance
detection in grid-connected distributed generation
system using wavelet and S-Transform”, Electric
Power Systems Research,2011, pp.800-805
[37] Samantaray S, Samui A, and Babu B., “S-Transform
based cumulative sum detector (CUSUM) for islanding
detection in distributed generations”, Power
Electronics, Drives and Energy Systems-
PEDES,2010,pp.1–6.
[38] Report on the Grid Disturbances on 30th July and
31st July 2012, Ministry of Power, Government of
India
[39] Srinu Babu Matta and Seethalekshmi K, “Out-of-
step detection in emerging power systems - key issues
and challenges”, The Journal of CPRI (Central Power
Research Institute ),India, Vol. 10, No. 3, September
2014, pp. 427-440.
[40] Srinu Babu Matta and K. Seethalekshmi, “Out-of-
step detection using Wide Area Measurements”,
CIGRE International conference, Global Learning
Centre-Infosys, Mysore, India, 13th-15th November
2013.
[41] Ahmed, S.S., Sarker, N.C., Khairuddin, A.B et al.,
(2003), A scheme for controlled islanding to prevent
subsequent blackout. Power Systems, IEEE
Transactions on 18(1), 136-143.

[42] Sun, K., Zheng, D.Z. and Lu, Q. (2003), Splitting
strategies for islanding operation of large-scale power
systems using OBDD-based methods. Power Systems,
IEEE Transactions on 18(2), 912-923.
[43] X. Wang and V. Vittal, (2004), System Islanding
Using Minimal Cut sets with Minimum Net Flow,
Proceedings of the 2004 IEEE PES Power System
Conference and Exposition, New York.
[44] You, H., V. Vittal, and Z. Yang, (2003), Self-Healing
in Power Systems, An Approach using Islanding and
Rate of Frequency Decline-Based Load Shedding, IEEE
Trans. on Power Systems, vol.18, No.1, pp 174-181.
[45] You, H., V. Vittal, and X. Wang, (2004), Slow
Coherency-Based Islanding, IEEE Trans. on Power
Systems, vol. 19, No. 1, pp 483-491.
[46] Peponides, G., Kokotovic, P.V and Chow, J.H.
(1982), Singular perturbations and time scales in
nonlinear models of power systems. IEEE Trans.
Circuits Systems, 29, 758–767.
[47] Ding, L., Gonzalez-Longatt and F.M., Wall et al.,
(2013). Two-step spectral clustering controlled
islanding algorithm. Power Systems, IEEE
Transactions on 28(1), 75-84.
[48] Quir_os-Tort_os, J., Wall, P. and Ding, L. et al.,
(2014), Determination of sectionalising strategies for
parallel power system restoration: A spectral
clustering-based methodology. Electric Power
Systems Research 116, 381-390
[49] Trodden, P.A., Bukhsh, W.A. and Grothey, A. et al.
(2014), Optimization-based islanding of power
networks using piecewise linear ac power ow. Power
Systems, IEEE Transactions on 29(3), 1212-1220.
[50] Pahwa, S., Youssef, M. and Schumm, P., (2013),
Optimal intentional islanding to enhance the
robustness of power grid networks. Physica A:
Statistical Mechanics and its Applications 392(17),
3741-3754
[51] You, H., V. Vittal, and Z. Yang, (2003), Self-Healing
in Power Systems, An Approach using Islanding and
Rate of Frequency Decline-Based Load Shedding, IEEE
Trans. on Power Systems, vol.18, No.1, pp 174-181.
[52] Wang, X. (2005), Slow Coherency Grouping Based
Islanding Using Minimal Cut-Sets and Generator
Coherency Index Tracing Using the Continuation
Method. Ph.D. Thesis, Iowa State University, Ames, IA,
USA
[53] Kumar, M.M. and Swarup, K.S., (2010), Graph
theoretic approach for preventive control of power
system. International Journal of Electrical Power
Energy Systems, 32, 254–261.
[54] Sen, A., Ghosh, P. and Vittal, V., (2009), A new min-
cut problem with application to electric power
network partitioning. Europian Transmission
Electrical Power, 19, 778–797.
[55] Quir_os-Tort_os, J., Wall, P. and Ding, L. et al.,
(2014), Determination of sectionalising strategies for
parallel power system restoration: A spectral
clustering-based methodology. Electric Power
Systems Research 116, 381-390
[56] Report on the Grid Disturbances on 30th July and
31st July 2012,(2012), Ministry of Power, Government
of India
[57] Sun, K., Zheng, D.Z. and Lu, Q., (2005), A
simulation study of OBDD-based proper splitting
strategies for power systems under consideration of
transient stability. IEEE Trans. Power Syst., 20, 389–
399.
[58] El-Zonkolym, A., Saad, M. and Khalil, R., (2013),
New algorithm based on clpso for controlled islanding
of distribution systems. Int. J. Electrical Power Energy
Systems, 45, 391–403.
[59] Aghamohammadi, M.R. and Shahmohammadi,
(2012), A. Intentional islanding using a new algorithm
based on ant search mechanism. International Journal
of Electrical Power Energy Systems, 35, 138–147.
[60] Li Liu a, Wenxin Liu and David A. Cartes., (2009),
Slow coherency and Angle Modulated Particle Swarm
Optimization based islanding of large-scale power
systems, Advanced Engineering Informatics 23 45–56
[61] Honglei, S., Junyong, W. and Kui, W. (2014), A
Wide-Area Measurement Systems-Based Adaptive
Strategy for Controlled Islanding in Bulk Power
Systems, Energies 2014, 7, 2631-2657.
[62] Zhenzhi, L., Fushuan, W. and Junhua, Z.,et al.
(2016), Controlled islanding schemes for
interconnected power systems based on coherent
generator group identification and wide-area
measurements, Journal of Modern Power Systems and
Clean Energy 4(3), 440–453
[63] Nourizadeh, S., Sarmadi, S.A. and Karimi, M.J.
(2012), Power system restoration planning based on
wide area measurement system. International Journal
of Electrical Power Energy Systems, 43, 526–530.
[64] Sun, K, Hur, K. and Zhang, P., (2011), A new
unified scheme for controlled power system
separation using synchronized phasor measurements.
IEEE Trans. Power Syst., 26, 1544–1554.
[65] Srinu Babu Matta and Seethalekshmi K, “An
Adaptive Islanding – Three Step Controlled Islanding
Strategy”, International Journal of Engineering Science
and Technology (IJEST), Vol. 9, Issue 05, May 2017, pp.
516-527.

BIOGRAPHIES
Srinu Babu Matta, Research Scholar
in Electrical Engineering Department,
BBD University, Lucknow, India.
Smart Grid, Adaptive Islanding,
Optimal Controlled Islanding, and
WAM systems are areas of research
interest.
Dr. Seethalekshmi K., Professor in
Electrical Engineering Department of
BBD University, Lucknow, India.
Smart Grid, Adaptive Distance
Protection, WAM systems, and
Adaptive islanding are areas of
research interest.

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