 Review
 Open Access
Shifting of research trends in islanding detection method  a comprehensive survey
 Soham Dutta^{1},
 Pradip Kumar Sadhu^{1},
 M. Jaya Bharata Reddy^{2}Email author and
 Dusmanta Kumar Mohanta^{3}
https://doi.org/10.1186/s4160101700758
© The Author(s) 2018
 Received: 18 September 2017
 Accepted: 11 December 2017
 Published: 19 January 2018
Abstract
The augmentation in electricity demand, power system privatization as well as efficacy of renewable resources has paved the way for power system companies and researchers to exploit the field of grid connected distributed generation (DG) and its issues, islanding being a dominant one. Several research works have been conducted to mitigate the issues of islanding detection (ID). In context of this, the paper gives a comprehensive review of islanding issues, standard test systems, criteria and shifting of research trends in islanding detection methods (IDMs). The significant contributions pertain to categorization of IDMs, evaluation of nondetection zone (NDZ) for each test system, disquisition on evolution and advancement of IDMs and its comparisons based on criteria such as NDZ, run on time, nuisance tripping percentage, applicability in multi DG system and implementation cost to draw out the strength and shortcomings of individual methods that will come to aid to the companies or researchers for establishing the applicability and appropriateness of such method for their concerned domain.
Keywords
 Review
 Distributed generation
 Islanding detection
 Microgrid
1 Introduction
The depletion of conventional fossil fuels at a breakneck pace and upsurge in power demand along with power market deregulation has aided in the technical and commercial development of a new paradigm in the DG all around the globe. DG means interconnection of mini or micro onsite distributed energy resources (DERs) generation with the main grid at distribution voltage stage. DERs primarily incorporate renewable and nonconventional energy resources such as solar photovoltaic (PV), hydro, wind, tidal, fuel cell, etc. [1]. Several energy market liberations and advancement in electronics and communication techniques have facilitated the operation of these geographically dispersed DERs through improved SCADA. These interconnected DERs possess the capability of operating both ongrid as well as offgrid mode.
The classical structure of distribution system was passive in nature i.e. it has always considered power flow from higher voltage level to lower voltage level. Active distribution network includes addition of DERs that are locally integrated into the low voltage distribution system that alters the network architecture and operation, rendering the classical consideration to be less applicable. The active distribution network differentiates from the passive in terms of bidirectionality of power flow, power electronics converter based generation, high fault level variability, etc. Plethoric DG penetration as well as DER placement has notable impacts on protection, operation, reliability and control of the power system [2]. These issues must be critically dealt with before permitting DG market participation for smooth operation of existing power structure along with some additional benefits like active reserve, interruptible loads, load following, restoration, etc. [3].
Island area 1, 2 and 3 is formed by opening of circuit breaker 1, 2 and 3 respectively. In light of this, the paper gives a review of IDM available in the research literature to manifest the transition of islanding detection research strategies with time. The methods have been broadly classified into classical and modern methods. A systematic analysis of these techniques is executed to bring to surface the merits and demerits of individual IDM. The rest of the paper is categorized as follows. Section 2 deals with the technical issues and concerns of islanding. The ID standards and criteria of IDM are explored in section 3 and section 4 respectively. Section 5 lays down the underlying theory, working principle and relevant equations of individual IDM. Besides, it also examines and compares various types of IDM. Finally, the work is inferred in section 6 and the future trends are discussed in section 7.
2 Technical issues with islanding

A fault that is detected by the protection mechanism of the grid but not by the protection devices installed in the grid connected DG

Equipment failure causing accidental disconnection of the normal grid

Human mistake or malpractices

An act of nature

Distributed generations are typically “weak” supplies that are incapable to handle transients efficiently.

After reclosure of the protective relays, the DGs may not be properly synchronized with that of the main grid resulting in considerable damage to the DGs as well as the utility and consumers.

If loads do not match to the supply characteristics, then the DG’s behavior may be unpredictable.

Utility workers may be oblivious to the fact that the lines disconnected from the main grid are still energized by the DGs making them prone to health hazards.

There will be confusion between IPPs and utility for the culpability for degraded power quality.
Intentional islanding is the process of intentionally splitting the grid into separate controllable islands [10]. Deliberate islanding is primarily implemented for averting cascading and blackouts the two perils to the security of power system. Intentional islanding negates the losses of inadvertent islanding. Intentional islanding may be employed to enhance the voltage profiles and decrease power losses, improve the overall efficiency of the system by controlling the congestion of distribution and transmission system. Therefore, intentional islanding operation can be a viable option if enforced through a proper envelope of research. Various researchers are working on the issues related to intentional islanding in order to eliminate its shortcomings making it practically applicable [11]. Islanding in the correct time and in the correct manner has inspired many works. After proper islanding, the frequency and voltages should be within the prescribed limit to avoid further blackouts [12]. Moreover, intentional islanding will have a significant effect on the electricity prices in the dynamic market [13]. During intentional islanding, several islands will have their own price and the producers can take unconscionable advantage by spiking the prices. Hence these practices should be properly checked. However, they may also give choice to customers whether to buy electricity or not under this condition [13].
3 Test systems, standards and test conditions
Unfortunately, there is no specific benchmark test system for islanding operation of microgrids. Various countries have installed their own test systems to study about the reliability and feasibility of islanding which are completely distinct from each other. Some of them are based only on one type of DG and some on hybrid generation. IEEE Std. 1547–2003, IEEE Std. 929–2000, Korean Std., VDE 0126–11 and UL 1741 are some of the international standards that the IPPs and utility must comply with for effective islanding [14].
4 Criteria for ID methods
There are a number of criteria that affects the performance of IDM. Unfortunately, there are no techniques currently available which fulfill all the criteria and can be applied for detecting all types of islanding scenarios. These criteria include NDZ, implementation cost, reliability, run on time, effect on power quality, etc.
4.1 NDZ
NDZ is the operating region of an IDM in which islanding instances cannot be determined. This is the most significant criteria of an IDM. The performance of an IDM increases with a decrease in NDZ. NDZ can be detected either by load parameter space (LPS) or power mismatch space (PMS) [15].
4.1.1 LPS
4.1.2 PMS
PMS for different islanding standards
Islanding Standard  Maximum ID time (sec)  V_{max} (%)  V_{min} (%)  f_{max} (Hz)  f_{min} (Hz)  Q_{f}  PMS (%)  

(ΔP/P_{DG})_{min} (ΔP/P_{DG})_{max} (ΔQ/P_{DG})_{min} (ΔQ/P_{DG})_{max}  
IEEE 1547–2003  2  110  88  60.5  59.3  1  −17  29  28  31.7 
IEEE 929–2000  2  110  88  60.5  59.3  2.5  −17  29  72  79.2 
Korean std.  0.5  110  88  60.5  59.3  1  −17  29  28  31.7 
VDE 0126–11  0.2  110  88  50.5  47.5  2  −17  29  −27  49 
UL 1741  2  110  88  60.5  59.3  2.5  −17  29  72  79.2 
4.2 Run on time
4.3 Nuisance tripping percentage
4.4 Effect on microgrid
IDMs injecting perturbations like harmonics, currents, etc. does not produce any significant effect when the DGs are connected to the main grid but significantly reduces the power quality when in islanded condition. Therefore, IDMs with null or minimal effect on microgrid is preferred.
4.5 Applicability in multi DG system
Nowadays, most of the microgrids are composed of not only more than one DG but several types of DG connected to the same PCC in parallel. The measurement parameters of IDMs may nullify each other owing to each DGs own characteristic variation rendering decreased performance of IDMs. Therefore, the IDMs should possess applicability in multiple DG system. Moreover, with more DG integration in near future, the IDMs should be able to detect intricate islanding.
4.6 Implementation cost
Some of the IDMs involve advanced and complicated hardware for successful operation. High performance is obtained at the cost of high investment decreasing its practical application. There should be a compromise between performance and cost for its real time implementation.
5 IDM
5.1 Classical
5.1.1 Passive methods
These are the first methods to be employed for ID. These methods were introduced around 1990’s and became popular around 1995. In these techniques, certain parameters like voltage and frequency at the PCC or at DG terminals are measured to detect islanding. A threshold value is set for these parameters exceeding which indicates islanding. These methods can detect islanding quickly but suffer nuisance tripping and have a large NDZ. Some of the significant passive methods are discussed below.
Over/under voltage (OVP/UVP) and over/under frequency (OFP/UFP)
Phase jump detection
Harmonic parameters
This technique measures THD and the main harmonics (3^{rd}, 5^{th} and 7^{th}) of the PCC voltage for ID [24]. Under normal condition, grid voltage almost matches with the PCC voltage causing negligible distortion (THD ≈ 0). During islanded condition, the harmonics may increase due to transfer of inverter generated current harmonics to the load and presence of nonlinearities in the transformer like magnetic hysteresis. The DG is disconnected if the measured values exceed its threshold [25].
Rate of change of frequency (ROCOF)
Rate of change of power output (ROCOP)
Impedance variation
The impedance of a power island is considerably larger than that of the utility impedance. Upon islanding, the impedance of the islanded part will suddenly increase [28]. This change of impedance is used to detect islanding by comparing it to a specified value [29].
Miscellaneous
The PLL in the inverter controller measures rate of change of voltage phase angle (ROCOVPA) at PCC to detect islanding in [30]. In [31], the magnitude of impedance at PCC is compared with that of a set of frequency dependent reference for ID. The oscillation frequency of synchronous generator is estimated in [32] by using a small window to detect islanding. ID based on rate of change of current sequence components at PCC is developed in [33]. Rate of change of frequency over reactive power at PCC for every half cycle is proposed for ID in [34]. In [35], passive ID using non ideal characteristics of voltage source inverter is shown with detection time of 100 ms.
5.1.2 Active methods
To overcome the shortcomings of passive methods, active methods came to picture around 1997. In these techniques, a disturbance signal is provided to specific parameters at the PCC and their effect is monitored for ID. It incorporates some sort of feedback signal or control mechanism that checks any variation of certain parameters at the PCC. These methods have lower NDZ than passive methods but highly degrades the power quality of the grid. The notable methods are dealt below.
Slip mode frequency shift (SMS)
SMS uses positive feedback to detect islanding. It applies positive feedback to the phase angle of the inverter current according to the deviation of frequency at PCC [36]. A typical SMS curve is so designed such that the increase in phase of the inverter is quicker than that of load with a unity power factor around the utility frequency region. Thus the line frequency becomes an unstable operating point for the inverter [22]. In islanded condition, the frequency and load phase angle varies with the curve. Islanding is detected when frequency crosses threshold [37].
Active frequency drift (AFD)
Sandia frequency shift (SFS)
Sandia voltage shift (SVS)
This is similar to SFS where positive feedback is applied to the amplitude of voltage in PCC rather than frequency. This positive feedback alters the power and current output of the inverter. When in grid connected connection, the amplitude of voltage is not affected noticeably but in island condition, power output can expedite the voltage drift to detect islanding. SVS affects the maximum power tracking mechanism of the inverter due to alteration of power output [43, 44].
Negative sequence current injection
This technique consists of injecting negative sequence current via voltage source converter (VSC) and monitoring negative sequence voltage at the PCC with the aid of unified three phase signal processor (UTSP). When connected to grid, it will have negligible impact on the PCC voltage but will have a considerable unbalance of PCC voltage during island condition [45].
Miscellaneous
In [46], average absolute frequency deviation value of the islanded area with respect to inverter reference current is proposed for ID. High frequency signal injection of magnitude 0.01 p.u of line voltage is used in [47]. It complies to the connection standard as THD is almost 0.09%. The estimation of transient stiffness of DG system by disturbing DG at distinct frequencies is proposed for ID in [48]. In [49], harmonic current at one of the frequency of the grid voltage is injected in grid altering the equivalent parallel impedance of the inverter for ID. The deviation between nominal and instantaneous voltage phase angle of DG is applied to inverter to detect islanding in [50]. In [51], frequency locked loop of inverter and frequency positive feedback for ID is employed.
5.1.3 Hybrid methods
These techniques became prevalent around 2003 to obtain both the advantages of passive and active methods. Hybrid techniques combine both active as well as passive detection techniques. The active techniques are applied only after islanding is detected by passive techniques. These methods have lower NDZ and does not significantly affect the power quality of grid. Some of the hybrid techniques are as follows:
Positive feedback (PF) and voltage unbalance (VU)
Voltage and reactive power shift
Hybrid SFS and Qf
Voltage and real power shift (RPS)
This technique uses real power shift (active) and average rate of voltage change (passive) to detect islanding. The RPS is employed only when the passive technique is unsure about islanding scenario. This discards the need to inject disturbances frequently as in case of other active method. This method changes only the real power of DG satisfying the condition of unity power factor and also only at one DG unlike other positive feedback techniques [56].
5.1.4 Local methods
Local IDMs uses communication signals between utility and the DGs for ID. The frequency of such signal is usually kept low to avoid interference with other power system signals. The cost of practical application of these methods proves to be very high for a single DG installation. Hence, these techniques are costly for small distribution network. Another drawback of these methods is its high dependency on communication means [57]. Hence, these are not recommended for small DGs. However, the greatest advantage of these methods is zero NDZ.
Transfer trip scheme
PLCC
Comparison of classical methods
Classical IDM  Main method  Reference  Detection time  DG system considered  Multiple DG considered  Strength  Shortcoming 

Passive  PJD  22  0.1 s  Inverter based  No  Low detection time  High NDZ 
Harmonic parameters  24  Less than 2 s  Inverter based  Yes  
ROCOF  27  0.5  Synchronous  Yes  
ROCOP  26  26 ms  Not specified  No  
Impedance variation  29  –  Synchronous  No  
Active  SMS  37  0.37 s  VSC  No  Low NDZ  Degrades power quality 
AFD  38  928 ms  Inverter based  No  
SFS  36  0.10s  VSC  No  
SVS  43  0.231 s  Inverter based  No  
Negative sequence current injection  45  60 ms  VSC  No  
Hybrid  PF and VU  52  0.15 s  Synchronous  Yes  Small NDZ and detection time  Slightly Degrades power quality 
Voltage and reactive power shift  54  –  Inverter based  No  
Hybrid SFS and Qf  55  –  Inverter based  No  
RPS  56  –  Wind  Yes  
Local  Transfer trip  58  –  PV  Yes  Zero NDZ  High cost 
PLCC  60  –  PV  Yes 
5.2 Modern
The modern methods include methods which exclusively use signal processing techniques and classifiers but the backbone of these methods is the classical IDMs mostly passive. These methods improve the performance of the classical IDMs.
5.2.1 Signal processing methods
WT
WT has been extensively used for signal processing in the last two decades. Its main advantage is its ability to expand a signal in frequency domain while retaining time information [61]. Hence applications where both time and frequency is required, applies WT. Such applications include fault detection, power quality measurement, power system protection, etc. [62]. There are several variants of WT available. Depending on the type of application one method is preferred over another. There are primarily three variants continuous wavelet transform (CWT), discrete wavelet transform (DWT) and wavelet packets transform (WPT).
CWT
DWT
In [65], a hybrid ID method is used where the PV inverter introduces high frequency components and then DWT is applied. The islanding is detected between 17 and 26 ms and is revealed at 5^{th} decomposition level with biorthogonal 1.5 as the selected wavelet owing to its better resolution and response time for the considered system. Daubechies mother wavelet analysis of frequency and voltage is used in [66] to detect islanding event for a DG unit of a southern Taiwan petroleum company. The method proves to be feasible and robustness with ID time of 0.05–0.1 s. Daubechies db4 based DWT is proposed in [67] where the negative sequence current and voltage of DGs are analyzed. Islanding is detected by detailed coefficient at level 1 within 1 cycle. [68] suggests a db4 DWT based passive ID method without any NDZ. The central idea is to detect the post islanding spectral changes of the higher frequency components of PCC. ID of induction type wind turbines using db5 based DWT on a wind turbine simulator is presented in [69] where islanding within less than 0.2 s is achieved. Wavelet MRA is used in [70] for ID by decomposing the voltage signals of the DGs into various scales and generating a series of wavelet coefficients corresponding to each scale. The ratio of WCs on scales 2 and 3 are used to detect islanding. The results imply that the features extracted by MRA indicate the variations in high frequency harmonic components which can be used as a potential parameter for islanding. DWT of voltage signal at PCC is used for feature extraction in [71] for ID. In [72] ID by wavelet singular entropy (WSE). The detailed coefficients at different levels of decomposition of the three phase DG voltage are acquired for generating a singular value matrix and WSE for each phase. Islanding is detected by adding the WSE of each phase to obtain WSE index (WSEI). The developed technique has a high detection speed with detection time of half cycle. [73] employs DWT based feature extraction of negative sequence of PCC voltage signal to detect islanding in 25 ms.
WPT
ST
ST based ID for DG hybrid system is proposed in [77]. In this, the negative sequence of PCC voltage is analyzed by ST, energy content and standard deviation of S contour is computed and the results are compared with WT. The results clearly prove the advantages of ST over WT. In [78], based on spectral energy content of the negative sequence of voltage and current, ST based cumulative sum detector (CUSUM) is developed for ID. ST of voltage signal at PCC is used for feature extraction in [71] for ID. [73] employs ST based feature extraction of negative sequence of PCC voltage signal to detect islanding in 26 ms.
HST
The drawback of Stransform lies in its disability in localizing in the time domain momentary phenomenon like sag and swell [79]. To strike out this shortcoming, HS transform is used which uses a pseudoGaussian hyperbolic window to achieve better time and frequency resolutions at high and low frequency. The hyperbolic window is frequency dependent in its shape besides its height and width. Due to this asymmetrical window, better resolution is obtained. In [71], islanding in a hybrid DG system is recognized by HST. The standard deviation and energy content of HST contour is presented to show its superiority over WT and ST in case of islanding for both noise as well as noiseless scenarios. [73] employs HST based feature extraction of negative sequence of PCC voltage signal to detect islanding in 22 ms.
TTT
TTT is employed in [81] where the pattern obtained by TTT of the three phase disturbances depicts unique signatures clearly. It is observed that individual event exhibits a unique pattern which is used to detect islanding. In [71] the graphical result analysis depicts the TTT capability of ID and localization of islanding disturbances over WT, ST and HST in a hybrid DG system. The performance is analyzed by computing energy content and standard deviation of the transformed signal. [73] employs TTT based feature extraction of negative sequence of PCC voltage signal to detect islanding in 25 ms.
HHT
The HHT comprises of two stages. In first stage, by applying empirical mode decomposition (EMD), the concerned signal is decomposed into intrinsic mode functions (IMFs) to obtain meaningful instantaneous amplitudes and frequencies [82]. The IMFs are arranged in descending order of frequency. In the second stage, Hilbert transform is applied on individual IMFs to obtain instantaneous amplitude and frequency versus time. Though HHT representations are more meaningful physically, yet they are less suitable for signals possessing close frequency components as well as identifying obtaining transition times for sudden waveform changes [83]. HHT is employed in [84] to obtain zero NDZ for inverter based islanding. The first component of perunit one phase PCC voltage is found by EMD process and is used for ID. For this, onecycle data window is employed. From simulations result, the ID is obtained to be less than 2 cycles. The effectiveness is further proved for multiple DG systems under various configurations. The simplicity, robustness against noise and straightforwardness of HHT is clearly shown.
MM
Basically, morphological filters incorporate nonlinear signal transformation tools for modifying the shapes of signals. MM originates from integral geometry and set theory. The WT, HST and TTT have greater computational complexity for harmonics and transients. Moreover, the assumed periodicity of the signal degrades the detection accuracy. Unlike these, mathematical morphology simply includes addition and subtraction of signals. By employing a smaller data window, abnormalities can be quickly detected [85]. MM is used to detect islanding by analyzing the negative sequence component of voltage signal at PCC for a hybrid DG system with solar and wind integrated IEEE 30 bus system [73]. It detects islanding within 22 ms proving to be more effective and feasible than WT, ST and TT for ID under noise free as well as noisy circumstances and also under the conditions of harmonics. For comparing, an energy based technique considering a suitable threshold value is employed.
Comparison of signal processing methods
Signal processing method  Ref.  DG type  Multiple DG considered  Analyzed signal  Run on time  Strength of the method  Shortcoming of the method 

CWT  63  –  –  Target DG voltage  0.6  Coefficients for all scales and transformations is obtained  Computational burden 
DWT  65  PV  No  PCC voltage  17–26 ms  Better low frequency resolutions  Computational complexity 
67  PV  No  PCC frequency  2.5 power freq cycle  
68  PV  No  Target DG voltage  2.5 cycle (0.05 s)  
WPT  74  Wind  No  ROCOP at DG  200 ms  Equal resolution for low and high frequency  Timefrequency localization decrease with increase in decomposition levels. 
ST  73  PV and wind  Yes  PCC voltage negative sequence  26 ms  Provides simplified multiresolution  Fails in localization of momentary phenomenon 
77  PV, fuel cell and wind  Yes  PCC voltage negative sequence  –  
HST  71  PV, fuel cell and wind  Yes  PCC voltage  –  Better time and frequency resolutions for high and low frequency  Window may not incorporate all signals 
73  PV and wind  Yes  PCC voltage negative sequence  22 ms  
TTT  71  PV, fuel cell and wind  Yes  PCC voltage  –  Better understanding of timelocal properties of the time series  Inappropriate lowfrequency Localization 
73  PV and wind  Yes  PCC voltage negative sequence  25 ms  
HHT  84  Inverter based  Yes  PCC voltage  Less than 2 cycles  Provides physical representation of data  Less suitable for close frequency components signals 
MM  73  PV and wind  Yes  PCC voltage negative sequence  22 ms  Less computational complexity  Reconstruction of the original signal is not possible 
5.2.2 Classifier methods
DT
ID for any possible network topology, DG operating condition and DG penetration is proposed in [87] where 11 system parameter indices are evaluated via numerous event analyses and then DT is employed to extract feature from huge data sets of these indices to detect islanding.
The proposed method has 83.33% accuracy. In [88], islanding instances are classified with DT algorithm. Adaptive boosting technique is employed to reduce the classification error rate. Owing to this boost algorithm, 100% accuracy is reached with negligible NDZ. [89] used DWT and DT for ID based on extracted current and voltage transient features for a medium voltage distribution system incorporating multiple DGs. The detection time is 24 ms with 99.22% accuracy and 95% confidence. The work has been extended for synchronous and induction type DGs in [90] where the effect of noise is being considered. The accuracy in case of noise is 96.11% with 99% confidence and 3 cycle detection time. In [91], the classifying features for training the DT is also acquired by DWT of transient signals for ID in a CIGRE distribution system. Among 162 relay designs, a Vdb4D3 relay is selected for ID with 98% accuracy. In [92], harmonic content and ROCOF of the equivalent reactance as seen at DG location is used as input of DT for a grid connected WSCC three machine 9 bus test system. [93] uses DT and voltage and frequency positive feedback for ID based on 6 feature indices.
FL
A fuzzy based relay based on multi criteria algorithm for ID is proposed in [95]. It monitors the variations in ROCOF, ROCOP and voltage at PCC and through fuzzy logic rules islanding is detected. A DT based fuzzy rule for ID is discussed in [96]. A crisp DT does the initial classification of 11 features after which the fuzzy membership functions are developed to transform the DT into fuzzy rules. The classification rate is found to be 100% with and without noise. Features extracted by ST are fed into a fuzzy expert system (FES) for islanding classification in [96] with an average accuracy of 99.8%. [97] proposes a fuzzy adaptive phase drift ID algorithm where the nonlinear relationship between different load characteristic and frequency drift at PCC is developed for fuzzy optimization. A fuzzy load parameter estimation (FLPE) is used to tune adaptively the SFS parameters for ID in [98].
ANN
A hybrid ID method based on ANN is developed in [100]. The passive part incorporates analyzing 6 indices at target DG location while the active part incorporates alteration of positive feedback of active/reactive power. The false detection rate is 11.1%. A neuro wavelet ID technique is developed in [101] in which the transient voltage signals have first been analyzed by DWT to extract feature vectors and then fed to train an ANN. Novel voltage signals of a multiple inverter based DG were tested with the trained ANN and an accuracy of 97.55% was achieved. Similar work has been done with three phase current signal combined into one modal signal of a 9 MVA wind based test system in [102] to obtain 0% classification error rate. Another application of ANN in ID is found in [103]. Voltage and current signals at PCC of a wind farm power station are measured and processed through Fourier transform to extract second harmonic. The symmetrical components of this second harmonic are then used for training an ANN. The method detects islanding within 2 cycles with high confidence.
Besides the core application of ANN, its variants like extended neural network (ENN), back propagation neural network (BPNN), selforganizing map (SOM), probabilistic neural network (PNN) and modular probabilistic neural network (MPNN) have also found application in ID. PNN is proposed in [104], where features extracted by DWT are fed to PNN classifier for recognizing islanding events. The method was also tested for different sampling frequency and feature set. The average accuracy of the proposed method is 89.76%. In [105], SOM is applied for ID. The papers propose application of SOM for classification of different islanding from nonislanding scenarios by analyzing the input signal of automatic load frequency controller (ALFC) of a distributed resource through it. The output of SOM sends a control signal to the automatic voltage regulator (AVR) for an under voltage relay tripping. The misclassification rate of [105] is 2.08% while in [106] it is 1.81%. [107] proposes ENN with active and passive multi variable detection techniques for ID. The peak value and frequency of the output voltage of power conditioner, the phase difference between current and voltage of the power conditioner is obtained by current and voltage feedback signals and fed to ENN for islanding classification. The simulation results prove the feasibility of the method. MPNN is used in [108] for islanding and power quality disturbance classification for a hybrid DG system. [109] combines chaos synchronization and type 2 ENN for ID for a grid connected Chua’s circuit based PV system. It has 98.4% accuracy. BPNN along with WPT is used in [110] to detect islanding based on normalized logarithmic energy entropy. The islanding is detected within 40 ms.
SVM
SVM is used to classify islanding events in a distribution system obtained from CIGRE MV system in [104] From simulated current signals at PCC, features are extracted by DWT for the training of SVM. The overall accuracy of SVM is found to be 78%. The results were also compared with DT and PNN which proved its inferiority. The application of SVM in ID for a hybrid DG system is developed in [108]. S transform is used to construct a matrix containing important information like magnitude, frequency and phase. Ten features are then extracted which is then applied to SVM for classification of islanding and PQ disturbances events. The overall accuracy obtained is 97.67%. A comparative analysis of application of SVM along with ST, HST, TTT and MM to detect islanding is given in [73]. It is seen that MM based SVM is best among all the other with 98.7% accuracy.
ANFIS
The advantage of ANFIS in reducing the NDZ and keeping the power quality unchanged is clearly demonstrated in [113, 114]. A passive ID through classification of various indices like voltage, current, etc. by ANFIS is shown in [115]. Similarly, wavelet based ANFIS is also used in active ID technique through d axis signal injection for a UL 1741 test configuration [116]. A hybrid islanding technique using ANFIS is implemented in [117] based on probability of islanding (PoI) values on Smart grid side.
Comparison of classifier methods
Classifier  Ref.  Signal processing method applied  No. of input features  DG type  Multi DG considered  Nuisance tripping percentage  Strength of the classifier  Shortcoming of the classifier 

DT  87  –  2  Synchronous  Yes  16.6  Fast training  Unfit for cases having lot of uncorrelated variables 
91  DWT  6  Not specified  Yes  2  
92  –  7  Synchronous  Yes  0  
FL  95  –  2  Synchronous  No  0  Easily interpretable  Not robust 
96  –  3  Synchronous  No  0  
ANN  100  –  2  Synchronous  Yes  11.1  Easy implementation  Huge cases required for proper training 
101  DWT  3  PV  Yes  2.55  
102  DWT  4  Wind  No  1.01  
PNN  104  DWT  6  Not specified  Yes  11  
MPNN  108  ST  11  PV, fuel cell and wind  Yes  3.6  
ENN  109  –  12  PV  No  1.6  
BPNN  110  WPT  13  PV  No  1.43  
SVM  108  ST  3  PV, fuel cell and wind  Yes  2.33  Minimized training error  Choice of proper hyper parameters is cumbersome 
73  ST/ HST/ TTT / MM  4  PV and wind  Yes  4.125/ 2.775/ 1.725/ 1.3  
ANFIS  114  DWT  3  Not specified  No  0  No requirement of mathematical models  Both the knowledge of ANN and Fuzzy is required 
115  –  4  DFIG  Yes  0  
117  DWT  6  Diesel, wind and NiCd battery  Yes  0 
Comparison of IDMs
IDM  Basic principle  NDZ  Run on time  Nuisance tripping percentage  Applicability in multiple DG system  Implementation cost  Effect on microgrid 

Active  Injecting disturbance and analyzing the impacts  Small  Short  Low  Not preferred  Low  Highly degrades the power quality 
Passive  Monitoring system parameters  Large  Very Short  High  Highly preferred  Low  None 
Hybrid  Combination of active and passive  Very small  Short  Low  Not preferred  Low  Degrades the power quality 
Local  Communication between DG  Zero  High  Negligible  Preferred  Extremely High  None 
Signal processing  Extraction of features by signal processing tools  Negligible  Short  Low  Preferred  Low  None 
Classifier  Classification based on input features  Negligible  Short  Very low  Preferred  Low  None 
6 Conclusion
A comprehensive survey of available IDMs has been dealt with in this paper. A brief introduction of each method is provided so that the shifting of the research ideas of the islanding methods can be comprehended vividly. The furtherance of each IDM with time is also discussed. The appropriateness of IDMs has been analyzed in terms of NDZ, run on time, nuisance tripping percentage, applicability in multi DG system and implementation cost. The PMS of islanding test systems have also been evaluated to materialize NDZ concept. Since there is no uniform islanding test bed, the IDMs are not compared on a single platform. Therefore, the efficacy of such methods varies with test beds.
7 Future trends

Though the concept of sequence components is employed for ID, the angle difference between them is not considered till date. There is a possibility of research scope of this concept.

Smart grid components like smart meters, phasor measurement unit, etc. can be also employed for ID. The advantage of such method will be nonrequirement of any additional hardware or may be even software unit for ID. This will drastically bring down the implementation time and cost making it practically and economical viable.

Advanced digital signal processing techniques like Discrete Fractional Fourier Transform (DFrFT) combined with learning algorithm may prove to be a potential tool for islanding detection.

The IDMs can be upgraded to “smart islanding” where it will detect and accomplish islanding intelligently by employing schemes in the islanded area such as load shedding. This will prevent complete black out in the islanded area rendering the critical loads to be active.
8 Nomenclature
P_{LOAD} load active power.
Q_{LOAD} load reactive power.
P_{DG} DG active power.
Q_{DG} DG reactive power.
R load resistance (Ω).
Q_{f} load quality factor.
f main grid frequency (Hz).
L load inductance (H).
C load capacitance (F).
C_{norm} normalized capacitance.
w_{o} resonant angular frequency.
C_{res} C resonating with L at f (F).
V_{max} maximum permissible voltage.
V_{min} minimum permissible voltage.
f_{max} maximum permissible frequency.
f_{min} minimum permissible frequency.
V rated voltage.
ΔQ reactive power mismatch.
ΔP active power mismatch.
T_{rt} run on time.
T_{mcb} mechanical time of islanding.
T_{com} computational time of islanding.
P_{nui} nuisance tripping percentage.
I_{nui} number of nuisance tripping instants.
I_{isl} number of correct islanding instants.
V′ PCC voltage after islanding.
ω′ PCC angular frequency after islanding.
α threshold value.
df/dt rate of change of frequency.
H moment of inertia of DG.
G rated generation capacity of DG.
dp/dt rate of change of power output.
n sampling instant.
tx sampling window length.
cf chopping fraction.
t_{z} dead time.
T_{vgrid} time period of V.
cf_{o} cf in absence of any frequency error.
K controller gain.
f_{PCC} PCC frequency.
VUB voltage unbalance.
V_{+Sq} positive sequence voltage.
V_{Sq} negative sequence voltage.
T(T_{av′,} T_{v})covariance value.
T_{av′} mean of previous four periods of voltage.
U_{av′} average of T_{av′}.
T_{v} voltage periods.
U_{v} mean of T_{av′}.
i_{ d } reactive power shift or daxis current shift.
k_{d} any positive value.
a scaling factor.
b shifting or translational factor.
Ѱ_{a,b}(t) continuous wavelet mother function.
C(a,b) continuous wavelet transform.
a_{0} dyadic dilation factor.
b_{0} dyadic translation factor.
Ѱ_{q.r}(t) discrete wavelet mother function.
D(q,r) discrete wavelet transform.
Q integer.
r integer.
S(τ,f) Stockwell transform.
u(t) analyzed signal.
fr frequency.
t time.
τ position parameter of window function on t axis.
d time frequency resolution control parameter.
w window function.
TT(t,τ) time time transform.
ς_{ nn } transfer function.
nn neuron.
y_{nn} nn output.
x_{j} j^{th} input of nn.
w_{nnj} connection weight between nn and j.
b_{nn} bias or threshold of nn.
Declarations
Authors’ contributions
SD has carried out study of islanding along with its technical issues, test systems and standards, various detection methods and developed the criterions for their comparison. PKS, MJBR and DKM has been the technical adviser of the complete work, has supported in interpreting the basic methods and trends for islanding detection and helped in drafting the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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