Shifting of research trends in islanding detection method - a comprehensive survey
© The Author(s) 2018
Received: 18 September 2017
Accepted: 11 December 2017
Published: 19 January 2018
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 non-detection 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.
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 on-site distributed energy resources (DERs) generation with the main grid at distribution voltage stage. DERs primarily incorporate renewable and non-conventional energy resources such as solar photovoltaic (PV), hydro, wind, tidal, fuel cell, etc. . 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 on-grid as well as off-grid 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 bi-directionality 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 . 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. .
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 re-closure 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 . 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 . 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 . Moreover, intentional islanding will have a significant effect on the electricity prices in the dynamic market . 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 .
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 0126–1-1 and UL 1741 are some of the international standards that the IPPs and utility must comply with for effective islanding .
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.
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) .
PMS for different islanding standards
Maximum ID time (sec)
(ΔP/PDG)min (ΔP/PDG)max (ΔQ/PDG)min (ΔQ/PDG)max
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 micro-grids 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.
5.1.1 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
This technique measures THD and the main harmonics (3rd, 5th and 7th) of the PCC voltage for ID . 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 non-linearities in the transformer like magnetic hysteresis. The DG is disconnected if the measured values exceed its threshold .
Rate of change of frequency (ROCOF)
Rate of change of power output (ROCOP)
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 . This change of impedance is used to detect islanding by comparing it to a specified value .
The PLL in the inverter controller measures rate of change of voltage phase angle (ROCOVPA) at PCC to detect islanding in . In , 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  by using a small window to detect islanding. ID based on rate of change of current sequence components at PCC is developed in . Rate of change of frequency over reactive power at PCC for every half cycle is proposed for ID in . In , 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 . 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 . In islanded condition, the frequency and load phase angle varies with the curve. Islanding is detected when frequency crosses threshold .
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 .
In , 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 . 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 . In , 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 . In , 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 Q-f
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 .
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 . Hence, these are not recommended for small DGs. However, the greatest advantage of these methods is zero NDZ.
Transfer trip scheme
Comparison of classical methods
DG system considered
Multiple DG considered
Low detection time
Less than 2 s
Degrades power quality
Negative sequence current injection
PF and VU
Small NDZ and detection time
Slightly Degrades power quality
Voltage and reactive power shift
Hybrid SFS and Q-f
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 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 . Hence applications where both time and frequency is required, applies WT. Such applications include fault detection, power quality measurement, power system protection, etc. . 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).
In , 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 5th 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  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  where the negative sequence current and voltage of DGs are analyzed. Islanding is detected by detailed coefficient at level 1 within 1 cycle.  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  where islanding within less than 0.2 s is achieved. Wavelet MRA is used in  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  for ID. In  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.  employs DWT based feature extraction of negative sequence of PCC voltage signal to detect islanding in 25 ms.
ST based ID for DG hybrid system is proposed in . 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 , 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  for ID.  employs ST based feature extraction of negative sequence of PCC voltage signal to detect islanding in 26 ms.
The drawback of S-transform lies in its disability in localizing in the time domain momentary phenomenon like sag and swell . To strike out this shortcoming, HS transform is used which uses a pseudo-Gaussian 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 , 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.  employs HST based feature extraction of negative sequence of PCC voltage signal to detect islanding in 22 ms.
TTT is employed in  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  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.  employs TTT based feature extraction of negative sequence of PCC voltage signal to detect islanding in 25 ms.
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 . 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 . HHT is employed in  to obtain zero NDZ for inverter based islanding. The first component of per-unit one phase PCC voltage is found by EMD process and is used for ID. For this, one-cycle 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.
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 . 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 . 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
Multiple DG considered
Run on time
Strength of the method
Shortcoming of the method
Target DG voltage
Coefficients for all scales and transformations is obtained
Better low frequency resolutions
2.5 power freq cycle
Target DG voltage
2.5 cycle (0.05 s)
ROCOP at DG
Equal resolution for low and high frequency
Time-frequency localization decrease with increase in decomposition levels.
PV and wind
PCC voltage negative sequence
Provides simplified multiresolution
Fails in localization of momentary phenomenon
PV, fuel cell and wind
PCC voltage negative sequence
PV, fuel cell and wind
Better time and frequency resolutions for high and low frequency
Window may not incorporate all signals
PV and wind
PCC voltage negative sequence
PV, fuel cell and wind
Better understanding of time-local properties of the time series
Inappropriate low-frequency Localization
PV and wind
PCC voltage negative sequence
Less than 2 cycles
Provides physical representation of data
Less suitable for close frequency components signals
PV and wind
PCC voltage negative sequence
Less computational complexity
Reconstruction of the original signal is not possible
5.2.2 Classifier methods
ID for any possible network topology, DG operating condition and DG penetration is proposed in  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 , 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.  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  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 , 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 V-db4-D3 relay is selected for ID with 98% accuracy. In , 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.  uses DT and voltage and frequency positive feedback for ID based on 6 feature indices.
A fuzzy based relay based on multi criteria algorithm for ID is proposed in . 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 . 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  with an average accuracy of 99.8%.  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 .
A hybrid ID method based on ANN is developed in . 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  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  to obtain 0% classification error rate. Another application of ANN in ID is found in . 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), self-organizing map (SOM), probabilistic neural network (PNN) and modular probabilistic neural network (MPNN) have also found application in ID. PNN is proposed in , 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 , SOM is applied for ID. The papers propose application of SOM for classification of different islanding from non-islanding 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  is 2.08% while in  it is 1.81%.  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  for islanding and power quality disturbance classification for a hybrid DG system.  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  to detect islanding based on normalized logarithmic energy entropy. The islanding is detected within 40 ms.
SVM is used to classify islanding events in a distribution system obtained from CIGRE MV system in  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 . 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 . It is seen that MM based SVM is best among all the other with 98.7% accuracy.
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 . Similarly, wavelet based ANFIS is also used in active ID technique through d axis signal injection for a UL 1741 test configuration . A hybrid islanding technique using ANFIS is implemented in  based on probability of islanding (PoI) values on Smart grid side.
Comparison of classifier methods
Signal processing method applied
No. of input features
Multi DG considered
Nuisance tripping percentage
Strength of the classifier
Shortcoming of the classifier
Unfit for cases having lot of un-correlated variables
Huge cases required for proper training
PV, fuel cell and wind
PV, fuel cell and wind
Minimized training error
Choice of proper hyper parameters is cumbersome
ST/ HST/ TTT / MM
PV and wind
No requirement of mathematical models
Both the knowledge of ANN and Fuzzy is required
Diesel, wind and Ni-Cd battery
Comparison of IDMs
Run on time
Nuisance tripping percentage
Applicability in multiple DG system
Effect on microgrid
Injecting disturbance and analyzing the impacts
Highly degrades the power quality
Monitoring system parameters
Combination of active and passive
Degrades the power quality
Communication between DG
Extraction of features by signal processing tools
Classification based on input features
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 non-requirement 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.
PLOAD load active power.
QLOAD load reactive power.
PDG DG active power.
QDG DG reactive power.
R load resistance (Ω).
Qf load quality factor.
f main grid frequency (Hz).
L load inductance (H).
C load capacitance (F).
Cnorm normalized capacitance.
wo resonant angular frequency.
Cres C resonating with L at f (F).
Vmax maximum permissible voltage.
Vmin minimum permissible voltage.
fmax maximum permissible frequency.
fmin minimum permissible frequency.
V rated voltage.
ΔQ reactive power mismatch.
ΔP active power mismatch.
Trt run on time.
Tmcb mechanical time of islanding.
Tcom computational time of islanding.
Pnui nuisance tripping percentage.
Inui number of nuisance tripping instants.
Iisl 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.
tz dead time.
Tvgrid time period of V.
cfo cf in absence of any frequency error.
K controller gain.
fPCC PCC frequency.
VUB voltage unbalance.
V+Sq positive sequence voltage.
V-Sq negative sequence voltage.
T(Tav′, Tv)covariance value.
Tav′ mean of previous four periods of voltage.
Uav′ average of Tav′.
Tv voltage periods.
Uv mean of Tav′.
i d reactive power shift or d-axis current shift.
kd any positive value.
a scaling factor.
b shifting or translational factor.
Ѱa,b(t) continuous wavelet mother function.
C(a,b) continuous wavelet transform.
a0 dyadic dilation factor.
b0 dyadic translation factor.
Ѱq.r(t) discrete wavelet mother function.
D(q,r) discrete wavelet transform.
S(τ,f) Stockwell transform.
u(t) analyzed signal.
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.
ynn nn output.
xj jth input of nn.
wnnj connection weight between nn and j.
bnn bias or threshold of nn.
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.
The authors declare that they have no competing interests.
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- El-Khattam, W., & Salama, M. M. (2004). Distributed generation technologies, definitions and benefits. Electr Power Syst Res, 71(2), 119–128.View ArticleGoogle Scholar
- Eltawil, M. A., & Zhao, Z. (2010). Grid-connected photovoltaic power systems: Technical and potential problems—A review. Renew Sust Energ Rev, 14(1), 112–129.View ArticleGoogle Scholar
- Cossent, R., Gómez, T., & Frías, P. (2009). Towards a future with large penetration of distributed generation: Is the current regulation of electricity distribution ready? Regulatory recommendations under a European perspective. Energy Policy, 37(3), 1145–1155.View ArticleGoogle Scholar
- Walling, R. A., & Miller, N. W. (2002). Distributed generation islanding-implications on power system dynamic performance, In power engineering society summer meeting, 2002 IEEE (Vol. 1, pp. 92–96). Piscataway: IEEE.Google Scholar
- De Mango, F., Liserre, M., Dell'Aquila, A., & Pigazo, A. (2006). Overview of anti-islanding algorithms for PV systems, Part I: Passive methods. In power electronics and motion control conference, 2006. EPE-PEMC 2006. 12th international (pp. 1878–1883). Piscataway: IEEE.Google Scholar
- Basso, T. S., & DeBlasio, R. (2004). IEEE 1547 series of standards: Interconnection issues. IEEE Trans Power Electron, 19(5), 1159–1162.View ArticleGoogle Scholar
- Wang, C., & Li, P. (2010). Development and challenges of distributed generation, the micro-grid and smart distribution system. Automation of Electric Power Systems, 2, 004.Google Scholar
- Katiraei, F., Iravani, M. R., & Lehn, P. W. (2005). Micro-grid autonomous operation during and subsequent to islanding process. IEEE Transactions on power delivery, 20(1), 248–257.View ArticleGoogle Scholar
- Vachtsevanos, G. J., & Kang, H. (1989). Simulation studies of islanded behavior of grid-connected photovoltaic systems. IEEE Transactions on Energy Conversion, 4(2), 177–183.View ArticleGoogle Scholar
- Balaguer, I. J., Lei, Q., Yang, S., Supatti, U., & Peng, F. Z. (2011). Control for grid-connected and intentional islanding operations of distributed power generation. IEEE Trans Ind Electron, 58(1), 147–157.View ArticleGoogle Scholar
- Kunte, R. S., & Gao, W. (2008). Comparison and review of islanding detection techniques for distributed energy resources, In power symposium, 2008. NAPS'08. 40th north American (pp. 1–8). Piscataway: IEEE.Google Scholar
- Shahmohammadi, A., & Ameli, M. T. (2014). Proper sizing and placement of distributed power generation aids the intentional islanding process. Electr Power Syst Res, 106, 73–85.View ArticleGoogle Scholar
- Zeineldin, H. H., Bhattacharya, K., El-Saadany, E. F., & Salama, M. M. A. (2006). Impact of intentional islanding of distributed generation on electricity market prices. IEE Proceedings-Generation, Transmission and Distribution, 153(2), 147–154.View ArticleGoogle Scholar
- Ahmad, K. N. E. K., Selvaraj, J., & Rahim, N. A. (2013). A review of the islanding detection methods in grid-connected PV inverters. Renew Sust Energ Rev, 21, 756–766.View ArticleGoogle Scholar
- Yu, B., Matsui, M., & Yu, G. (2010). A review of current anti-islanding methods for photovoltaic power system. Sol Energy, 84(5), 745–754.View ArticleGoogle Scholar
- Zhang, Y., Sun, H., & Lopes, L. A. (2007). 1 an islanding detection enhancer for a system with multiple photovoltaic inverters.Google Scholar
- Ropp, M. E., Begovic, M., Rohatgi, A., Kern, G. A., Bonn, R. H., & Gonzalez, S. (2000). Determining the relative effectiveness of islanding detection methods using phase criteria and nondetection zones. IEEE transactions on energy conversion, 15(3), 290–296.View ArticleGoogle Scholar
- Sun, H. (2005). Performance assessment of islanding detection methods using the concept of non-detection zones. Montreal: Doctoral dissertation, Concordia University.Google Scholar
- Ye, Z., Kolwalkar, A., Zhang, Y., Du, P., & Walling, R. (2004). Evaluation of anti-islanding schemes based on nondetection zone concept. IEEE Trans Power Electron, 19(5), 1171–1176.View ArticleGoogle Scholar
- Vieira, J. C., Salles, D., & Freitas, W. (2011). Power imbalance application region method for distributed synchronous generator anti-islanding protection design and evaluation. Electr Power Syst Res, 81(10), 1952–1960.View ArticleGoogle Scholar
- PVPS, I. (2002). Evaluation of islanding detection methods for photovoltaic utility-interactive power systems. Report IEA PVPS T5–09.Google Scholar
- Singam, B., & Hui, L. Y. (2006). Assessing SMS and PJD schemes of anti-islanding with varying quality factor, In power and energy conference, 2006. PECon'06. IEEE international (pp. 196–201). Piscataway: IEEE.Google Scholar
- Hung, G. K., Chang, C. C., & Chen, C. L. (2003). Automatic phase-shift method for islanding detection of grid-connected photovoltaic inverters. IEEE Transactions on energy conversion, 18(1), 169–173.View ArticleGoogle Scholar
- Merino, J., Mendoza-Araya, P., Venkataramanan, G., & Baysal, M. (2015). Islanding detection in microgrids using harmonic signatures. IEEE Transactions on Power Delivery, 30(5), 2102–2109.View ArticleGoogle Scholar
- Kim, I. S. (2012). Islanding detection technique using grid-harmonic parameters in the photovoltaic system. Energy Procedia, 14, 137–141.View ArticleGoogle Scholar
- Redfern, M. A., Usta, O., & Fielding, G. (1993). Protection against loss of utility grid supply for a dispersed storage and generation unit. IEEE Transactions on Power Delivery, 8(3), 948–954.View ArticleGoogle Scholar
- Freitas, W., Xu, W., Affonso, C. M., & Huang, Z. (2005). Comparative analysis between ROCOF and vector surge relays for distributed generation applications. IEEE Transactions on power delivery, 20(2), 1315–1324.View ArticleGoogle Scholar
- Liu, N., Aljankawey, A., Diduch, C., Chang, L., & Su, J. (2015). Passive islanding detection approach based on tracking the frequency-dependent impedance change. IEEE Transactions on Power Delivery, 30(6), 2570–2580.View ArticleGoogle Scholar
- O'kane, P., & Fox, B. (1997). Loss of mains detection for embedded generation by system impedance monitoring.View ArticleGoogle Scholar
- Samet, H., Hashemi, F., & Ghanbari, T. (2015). Islanding detection method for inverter-based distributed generation with negligible non-detection zone using energy of rate of change of voltage phase angle. IET Generation, Transmission & Distribution, 9(15), 2337–2350.View ArticleGoogle Scholar
- Liu, N., Diduch, C., Chang, L., & Su, J. (2015). A reference impedance-based passive islanding detection method for inverter-based distributed generation system. IEEE Journal of Emerging and Selected Topics in Power Electronics, 3(4), 1205–1217.View ArticleGoogle Scholar
- Marchesan, G., Muraro, M. R., Cardoso, G., Mariotto, L., & De Morais, A. P. (2016). Passive method for distributed-Generation Island detection based on oscillation frequency. IEEE Transactions on Power Delivery, 31(1), 138–146.View ArticleGoogle Scholar
- Sareen, K., Bhalja, B. R., & Maheshwari, R. P. (2016). Universal islanding detection technique based on rate of change of sequence components of currents for distributed generations. IET Renewable Power Generation, 10(2), 228–237.View ArticleGoogle Scholar
- Raza, S., Mokhlis, H., Arof, H., Laghari, J. A., & Mohamad, H. (2016). A sensitivity analysis of different power system parameters on islanding detection. IEEE Transactions on Sustainable Energy, 7(2), 461–470.View ArticleGoogle Scholar
- Reigosa, D., Briz, F., Blanco, C., & Guerrero, J. M. (2017). Passive islanding detection using inverter nonlinear effects. IEEE transactions on power electronics.Google Scholar
- Lopes, L. A., & Sun, H. (2006). Performance assessment of active frequency drifting islanding detection methods. IEEE Transactions on Energy Conversion, 21(1), 171–180.View ArticleGoogle Scholar
- Liu, F., Kang, Y., Zhang, Y., Duan, S., & Lin, X. (2010). Improved SMS islanding detection method for grid-connected converters. IET renewable power generation, 4(1), 36–42.View ArticleGoogle Scholar
- Ropp, M. E., Begovic, M., & Rohatgi, A. (1999). Prevention of islanding in grid-connected photovoltaic systems. Prog Photovolt Res Appl, 7(1), 39–59.View ArticleGoogle Scholar
- Wen, B., Boroyevich, D., Burgos, R., Shen, Z., & Mattavelli, P. (2016). Impedance-based analysis of active frequency drift islanding detection for grid-tied inverter system. IEEE Trans Ind Appl, 52(1), 332–341.View ArticleGoogle Scholar
- Ropp, M. E., Begovic, M., & Rohatgi, A. (1999). Analysis and performance assessment of the active frequency drift method of islanding prevention. IEEE Transactions on Energy conversion, 14(3), 810–816.View ArticleGoogle Scholar
- Zeineldin, H. H., & Conti, S. (2011). Sandia frequency shift parameter selection for multi-inverter systems to eliminate non-detection zone. IET Renewable Power Generation, 5(2), 175–183.View ArticleGoogle Scholar
- Wang, X., Freitas, W., Xu, W., & Dinavahi, V. (2007). Impact of DG interface controls on the sandia frequency shift antiislanding method. IEEE Transactions on Energy Conversion, 22(3), 792–794.View ArticleGoogle Scholar
- Trujillo, C. L., Velasco, D., Figueres, E., & Garcerá, G. (2010). Analysis of active islanding detection methods for grid-connected microinverters for renewable energy processing. Appl Energy, 87(11), 3591–3605.View ArticleGoogle Scholar
- El-Moubarak, M., Hassan, M., & Faza, A. (2015). Performance of three islanding detection methods for grid-tied multi-inverters, In environment and electrical engineering (EEEIC), 2015 IEEE 15th international conference on (pp. 1999–2004). Piscataway: IEEE.Google Scholar
- Karimi, H., Yazdani, A., & Iravani, R. (2008). Negative-sequence current injection for fast islanding detection of a distributed resource unit. IEEE Trans Power Electron, 23(1), 298–307.View ArticleGoogle Scholar
- Gupta, P., Bhatia, R. S., & Jain, D. K. (2015). Average absolute frequency deviation value based active islanding detection technique. IEEE Transactions on Smart Grid, 6(1), 26–35.View ArticleGoogle Scholar
- Reigosa, D. D., Briz, F., Charro, C. B., & Guerrero, J. M. (2015). Islanding detection in three-phase and single-phase systems using pulsating high-frequency signal injection. IEEE Trans Power Electron, 30(12), 6672–6683.View ArticleGoogle Scholar
- Al Hosani, M., Qu, Z., & Zeineldin, H. H. (2015). A transient stiffness measure for islanding detection of multi-DG systems. IEEE Transactions on Power Delivery, 30(2), 986–995.View ArticleGoogle Scholar
- Hamzeh, M., Rashidirad, N., Sheshyekani, K., & Afjei, E. (2016). A new islanding detection scheme for multiple inverter-based DG systems. IEEE Transactions on Energy Conversion, 31(3), 1002–1011.View ArticleGoogle Scholar
- Pourbabak, H., & Kazemi, A. (2016). Islanding detection method based on a new approach to voltage phase angle of constant power inverters. IET Generation, Transmission & Distribution, 10(5), 1190–1198.View ArticleGoogle Scholar
- Sun, Q., Guerrero, J. M., Jing, T., Vasquez, J. C., & Yang, R. (2017). An islanding detection method by using frequency positive feedback based on FLL for single-phase microgrid. IEEE Transactions on Smart Grid, 8(4), 1821–1830.View ArticleGoogle Scholar
- Menon, V., & Nehrir, M. H. (2007). A hybrid islanding detection technique using voltage unbalance and frequency set point. IEEE Trans Power Syst, 22(1), 442–448.View ArticleGoogle Scholar
- Mahat, P., Chen, Z., & Bak-Jensen, B. (2008). Review of islanding detection methods for distributed generation, In electric utility deregulation and restructuring and power technologies, 2008. DRPT 2008. Third international conference on (pp. 2743–2748). Piscataway: IEEE.Google Scholar
- Yin, J., Chang, L., & Diduch, C. (2006). A new hybrid anti-islanding algorithm in grid connected three-phase inverter system, In 2006 IEEE power electronics specialists conference (pp. 1–7).Google Scholar
- Vahedi, H., Noroozian, R., Jalilvand, A., & Gharehpetian, G. B. (2010). Hybrid SFS and Qf islanding detection method for inverter-based DG, In power and energy (PECon), 2010 IEEE international conference on (pp. 672–676). Piscataway: IEEE.Google Scholar
- Mahat, P., Chen, Z., & Bak-Jensen, B. (2009). A hybrid islanding detection technique using average rate of voltage change and real power shift. IEEE Transactions on Power delivery, 24(2), 764–771.View ArticleGoogle Scholar
- Etxegarai, A., Eguía, P., & Zamora, I. (2011). Analysis of remote islanding detection methods for distributed resources. In Int. conf. Renew. Energies power quality.Google Scholar
- Walling, R. A. (2011). Application of direct transfer trip for prevention of DG islanding, In power and energy society general meeting, 2011 IEEE (pp. 1–3). Piscataway: IEEE.Google Scholar
- Ropp, M., Joshi, D., Reedy, R., Davis, K., Click, D., & Shaffer, A. (2011). New results for power line carrier-based islanding detection and an updated strengths and weaknesses discussion, In photovoltaic specialists conference (PVSC), 2011 37th IEEE (pp. 002584–002587). Piscataway: IEEE.Google Scholar
- Perlenfein, S., Ropp, M., Neely, J., Gonzalez, S., & Rashkin, L. (2015). Subharmonic power line carrier (PLC) based island detection, In applied power electronics conference and exposition (APEC), 2015 IEEE (pp. 2230–2236). Piscataway: IEEE.Google Scholar
- Chen, S. (2005). Feature selection for identification and classification of power quality disturbances, In power engineering society general meeting, 2005. IEEE (pp. 2301–2306). Piscataway: IEEE.Google Scholar
- Daubechies, I. (1990). The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory, 36(5), 961–1005.MathSciNetMATHView ArticleGoogle Scholar
- Zhu, Y., Yang, Q., Wu, J., Zheng, D., & Tian, Y. (2008). A novel islanding detection method of distributed generator based on wavelet transform, In electrical machines and systems, 2008. ICEMS 2008. International conference on (pp. 2686–2688). Piscataway: IEEE.Google Scholar
- Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans Pattern Anal Mach Intell, 11(7), 674–693.MATHView ArticleGoogle Scholar
- Pigazo, A., Liserre, M., Mastromauro, R. A., Moreno, V. M., & Dell'Aquila, A. (2009). Wavelet-based islanding detection in grid-connected PV systems. IEEE Trans Ind Electron, 56(11), 4445–4455.View ArticleGoogle Scholar
- Hsieh, C. T., Lin, J. M., & Huang, S. J. (2008). Enhancement of islanding-detection of distributed generation systems via wavelet transform-based approaches. Int J Electr Power Energy Syst, 30(10), 575–580.View ArticleGoogle Scholar
- Hanif, M., Dwivedi, U. D., Basu, M., & Gaughan, K. (2010). Wavelet based islanding detection of DC-AC inverter interfaced DG systems, In universities power engineering conference (UPEC), 2010 45th international (pp. 1–5). Piscataway: IEEE.Google Scholar
- Hanif, M., Basu, M., & Gaughan, K. (2012). Development of EN50438 compliant wavelet-based islanding detection technique for three-phase static distributed generation systems. IET renewable power generation, 6(4), 289–301.View ArticleGoogle Scholar
- Karegar, H. K., & Sobhani, B. (2012). Wavelet transform method for islanding detection of wind turbines. Renew Energy, 38(1), 94–106.View ArticleGoogle Scholar
- Ning, J., & Wang, C. (2012). Feature extraction for islanding detection using wavelet transform-based multi-resolution analysis, In power and energy society general meeting, 2012 IEEE (pp. 1–6). Piscataway: IEEE.Google Scholar
- Mohanty, S. R., Kishor, N., Ray, P. K., & Catalão, J. P. (2012). Islanding detection in a distributed generation based hybrid system using intelligent pattern recognition techniques, In innovative smart grid technologies (ISGT Europe), 2012 3rd IEEE PES international conference and exhibition on (pp. 1–5). Piscataway: IEEE.Google Scholar
- Samui, A., & Samantaray, S. R. (2013). Wavelet singular entropy-based islanding detection in distributed generation. IEEE transactions on power delivery, 28(1), 411–418.View ArticleGoogle Scholar
- Mohanty, S. R., Kishor, N., Ray, P. K., & Catalo, J. P. (2015). Comparative study of advanced signal processing techniques for islanding detection in a hybrid distributed generation system. IEEE Transactions on sustainable Energy, 6(1), 122–131.View ArticleGoogle Scholar
- Morsi, W. G., Diduch, C. P., & Chang, L. (2010). A new islanding detection approach using wavelet packet transform for wind-based distributed generation, In power electronics for distributed generation systems (PEDG), 2010 2nd IEEE international symposium on (pp. 495–500). Piscataway: IEEE.Google Scholar
- Stockwell, R. G., Mansinha, L., & Lowe, R. P. (1996). Localisation of the complex spectrum: The S transform. J Assoc Explor Geophys, 17(3), 99–114.Google Scholar
- Dehghani, M. J. (2009). Comparison of S-transform and wavelet transform in power quality analysis. World Acad Sci Eng Technol, 50(4), 395–398.Google Scholar
- Ray, P. K., Mohanty, S. R., & Kishor, N. (2011). Disturbance detection in grid-connected distributed generation system using wavelet and S-transform. Electr Power Syst Res, 81(3), 805–819.View ArticleGoogle Scholar
- Samantaray, S. R., Samui, A., & Babu, B. C. (2010). S-transform based cumulative sum detector (CUSUM) for islanding detection in distributed generations, In power electronics, drives and energy systems (PEDES) & 2010 power India, 2010 joint international conference on (pp. 1–6). Piscataway: IEEE.Google Scholar
- Ashrafian, A., Rostami, M., & Gharehpetian, G. B. (2012). Hyperbolic S-transform-based method for classification of external faults, incipient faults, inrush currents and internal faults in power transformers. IET generation, transmission & distribution, 6(10), 940–950.View ArticleGoogle Scholar
- Simon, C., Schimmel, M., & Dañobeitia, J. J. (2008). On the TT-transform and its diagonal elements. IEEE Trans Signal Process, 56(11), 5709–5713.MathSciNetView ArticleGoogle Scholar
- Khamis, A., Shareef, H., & Wanik, M. Z. C. (2012). Pattern recognition of islanding detection using tt-transform. Journal of Asian Scientific Research, 2(11), 607.Google Scholar
- Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., ... & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In proceedings of the Royal Society of London a: Mathematical, physical and engineering sciences (Vol. 454, No. 1971, pp. 903-995). London: The Royal Society.Google Scholar
- Donnelly, D. (2006). The fast Fourier and Hilbert-Huang transforms: A comparison, In computational engineering in systems applications, IMACS multiconference on (Vol. 1, pp. 84–88). Piscataway: IEEE.Google Scholar
- Niaki, A. M., & Afsharnia, S. (2014). A new passive islanding detection method and its performance evaluation for multi-DG systems. Electr Power Syst Res, 110, 180–187.View ArticleGoogle Scholar
- Maragos, P., & Schafer, R. (1987). Morphological filters--part I: Their set-theoretic analysis and relations to linear shift-invariant filters. IEEE Trans Acoust Speech Signal Process, 35(8), 1153–1169.MathSciNetView ArticleGoogle Scholar
- Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics, 21(3), 660–674.MathSciNetView ArticleGoogle Scholar
- El-Arroudi, K., Joos, G., Kamwa, I., & McGillis, D. T. (2007). Intelligent-based approach to islanding detection in distributed generation. IEEE transactions on power delivery, 22(2), 828–835.View ArticleGoogle Scholar
- Madani, S. S., Abbaspour, A., Beiraghi, M., Dehkordi, P. Z., & Ranjbar, A. M. (2012). Islanding detection for PV and DFIG using decision tree and AdaBoost algorithm, In innovative smart grid technologies (ISGT Europe), 2012 3rd IEEE PES international conference and exhibition on (pp. 1–8). Piscataway: IEEE.Google Scholar
- Lidula, N. W. A., & Rajapakse, A. D. (2010). A pattern recognition approach for detecting power islands using transient signals-part I: Design and implementation. IEEE Transactions on Power Delivery, 25(4), 3070–3077.View ArticleGoogle Scholar
- Lidula, N. W. A., & Rajapakse, A. D. (2012). A pattern-recognition approach for detecting power islands using transient signals-part II: Performance evaluation. IEEE Transactions on Power Delivery, 27(3), 1071–1080.View ArticleGoogle Scholar
- Heidari, M., Seifossadat, G., & Razaz, M. (2013). Application of decision tree and discrete wavelet transform for an optimized intelligent-based islanding detection method in distributed systems with distributed generations. Renew Sust Energ Rev, 27, 525–532.View ArticleGoogle Scholar
- Vatani, M., Amraee, T., Ranjbar, A. M., & Mozafari, B. (2015). Relay logic for islanding detection in active distribution systems. IET Generation, Transmission & Distribution, 9(12), 1254–1263.View ArticleGoogle Scholar
- Zhou, B., Cao, C., Li, C., Cao, Y., Chen, C., Li, Y., & Zeng, L. (2015). Hybrid islanding detection method based on decision tree and positive feedback for distributed generations. IET Generation, Transmission & Distribution, 9(14), 1819–1825.View ArticleGoogle Scholar
- Wang, L. X., & Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on systems, man, and cybernetics, 22(6), 1414–1427.MathSciNetView ArticleGoogle Scholar
- Rosolowski, E., Burek, A., & Jedut, L. (2007). A new method for islanding detection in distributed generation. Poljska: Wroclaw University of Technology.Google Scholar
- Samantaray, S. R., El-Arroudi, K., Joos, G., & Kamwa, I. (2010). A fuzzy rule-based approach for islanding detection in distributed generation. IEEE Transactions on Power Delivery, 25(3), 1427–1433.View ArticleGoogle Scholar
- Shi, L., & Wu, F. (2013). An islanding detection algorithm based on fuzzy adaptive phase drift control, In information and automation (ICIA), 2013 IEEE international conference on (pp. 225–229). Piscataway: IEEE.Google Scholar
- Vahedi, H., & Karrari, M. (2013). Adaptive fuzzy sandia frequency-shift method for islanding protection of inverter-based distributed generation. IEEE Transactions on Power Delivery, 28(1), 84–92.View ArticleGoogle Scholar
- Yao, X. (1999). Evolving artificial neural networks. Proc IEEE, 87(9), 1423–1447.View ArticleGoogle Scholar
- Ghazi, R., & Lotfi, N. (2010). A new hybrid intelligent based approach to islanding detection in distributed generation, In universities power engineering conference (UPEC), 2010 45th international (pp. 1–5). Piscataway: IEEE.Google Scholar
- Fayyad, Y., & Osman, A. (2010). Neuro-wavelet based islanding detection technique, In electric power and energy conference (EPEC), 2010 IEEE (pp. 1–6). Piscataway: IEEE.Google Scholar
- ElNozahy, M. S., El-Saadany, E. F., & Salama, M. M. (2011). A robust wavelet-ANN based technique for islanding detection, In power and energy society general meeting, 2011 IEEE (pp. 1–8). Piscataway: IEEE.Google Scholar
- Abd-Elkader, A. G., Allam, D. F., & Tageldin, E. (2014). Islanding detection method for DFIG wind turbines using artificial neural networks. Int J Electr Power Energy Syst, 62, 335–343.View ArticleGoogle Scholar
- Lidula, N. W. A., & Rajapakse, A. D. (2009). Fast and reliable detection of power islands using transient signals, In industrial and information systems (ICIIS), 2009 international conference on (pp. 493–498). Piscataway: IEEE.Google Scholar
- Moeini, A., Darabi, A., & Karimi, M. (2010). Clustering governor signal of distributed generation for islanding detection, In computational Technologies in Electrical and Electronics Engineering (SIBIRCON), 2010 IEEE region 8 international conference on (pp. 493–498). Piscataway: IEEE.Google Scholar
- Moeini, A., Darabi, A., Rafiei, S. M. R., & Karimi, M. (2011). Intelligent islanding detection of a synchronous distributed generation using governor signal clustering. Electr Power Syst Res, 81(2), 608–616.View ArticleGoogle Scholar
- Chao, K. H., Chiu, C. L., Li, C. J., & Chang, Y. C. (2011). A novel neural network with simple learning algorithm for islanding phenomenon detection of photovoltaic systems. Expert Syst Appl, 38(10), 12107–12115.View ArticleGoogle Scholar
- Mohanty, S. R., Ray, P. K., Kishor, N., & Panigrahi, B. K. (2013). Classification of disturbances in hybrid DG system using modular PNN and SVM. Int J Electr Power Energy Syst, 44(1), 764–777.View ArticleGoogle Scholar
- Wang, M. H., Huang, M. L., & Liou, K. J. (2015). Islanding detection method for grid connected photovoltaic systems. IET Renewable Power Generation, 9(6), 700–709.View ArticleGoogle Scholar
- Do, H. T., Zhang, X., Nguyen, N. V., Li, S. S., & Chu, T. T. T. (2016). Passive-islanding detection method using the wavelet packet transform in grid-connected photovoltaic systems. IEEE Trans Power Electron, 31(10), 6955–6967.Google Scholar
- Janik, P., & Lobos, T. (2006). Automated classification of power-quality disturbances using SVM and RBF networks. IEEE Transactions on Power Delivery, 21(3), 1663–1669.View ArticleGoogle Scholar
- Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665–685.View ArticleGoogle Scholar
- Hashemi, F., Ghadimi, N., & Sobhani, B. (2013). Islanding detection for inverter-based DG coupled with using an adaptive neuro-fuzzy inference system. Int J Electr Power Energy Syst, 45(1), 443–455.View ArticleGoogle Scholar
- Shayeghi, H., & Sobhani, B. (2014). Zero NDZ assessment for anti-islanding protection using wavelet analysis and neuro-fuzzy system in inverter based distributed generation. Energy Convers Manag, 79, 616–625.View ArticleGoogle Scholar
- Bitaraf, H., Sheikholeslamzadeh, M., Ranjbar, A. M., & Mozafari, B. (2012). Neuro-fuzzy islanding detection in distributed generation, In innovative smart grid technologies-Asia (ISGT Asia), 2012 IEEE (pp. 1–5). Piscataway: IEEE.Google Scholar
- Lin, F. J., Huang, Y. S., Tan, K. H., Chiu, J. H., & Chang, Y. R. (2013). Active islanding detection method using d-axis disturbance signal injection with intelligent control. IET Generation, Transmission & Distribution, 7(5), 537–550.View ArticleGoogle Scholar
- Kermany, S. D., Joorabian, M., Deilami, S., & Masoum, M. A. (2017). Hybrid islanding detection in microgrid with multiple connection points to smart grids using fuzzy-neural network. IEEE Trans Power Syst, 32(4), 2640–2651.View ArticleGoogle Scholar