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Amin Damanjani Department of Electrical and Computer Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran

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Mohamad Hosseini Abardeh Department of Electrical and Computer Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran

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Azita Azarfar Department of Electrical and Computer Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran

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Mehrdad Hojjat Department of Electrical and Computer Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran

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Abstract

In this paper, a comprehensive statistics-based review of islanding detection methods (IDMs) in microgrids (MGs) is presented. Islanding detection is the situation of isolating the MG from the main grid whether programmed as a result of load managing purposes or un-programmed due to the occurrence of faults. Islanding detection is a vital issue in MG's analyses due to the prevention of subsequent protection problems in the power system. In other words, when the MG's operation mode changes, the current passing through the protective devices changes subsequently and the protection system should be able to adapt the new settings to the protective devices. So, IDMs are vital for electrical engineers to overcome the abovementioned protection issue. This review paper surveys the existing literature in IDMs by concentration on total publications, type of publications (journal, conference paper, or book), five authors with the highest number of publications (including the affiliations), and five most published sources. Also, the five most cited publications and state-of-the-art IDMs are investigated in detail, utilizing some known and novel categorizations. This paper will be useful for the MG's researchers to know the most desirable IDMs, especially in recent years, and provides an insightful overview for future studies.

Abstract

In this paper, a comprehensive statistics-based review of islanding detection methods (IDMs) in microgrids (MGs) is presented. Islanding detection is the situation of isolating the MG from the main grid whether programmed as a result of load managing purposes or un-programmed due to the occurrence of faults. Islanding detection is a vital issue in MG's analyses due to the prevention of subsequent protection problems in the power system. In other words, when the MG's operation mode changes, the current passing through the protective devices changes subsequently and the protection system should be able to adapt the new settings to the protective devices. So, IDMs are vital for electrical engineers to overcome the abovementioned protection issue. This review paper surveys the existing literature in IDMs by concentration on total publications, type of publications (journal, conference paper, or book), five authors with the highest number of publications (including the affiliations), and five most published sources. Also, the five most cited publications and state-of-the-art IDMs are investigated in detail, utilizing some known and novel categorizations. This paper will be useful for the MG's researchers to know the most desirable IDMs, especially in recent years, and provides an insightful overview for future studies.

1 Introduction

Microgrids (MGs) are designed as small low voltage subsections in the power systems for feeding the electrical loads. MGs are capable of working on two operation modes known as grid-connected and islanded utilizing the circuit breaker (CB) located between the main grid and MG named point of common coupling (PCC) [1]. This CB isolates the main grid from the MG. Grid-connected mode is a status in which the MG has the capability to receive the injected power from the grid [2]. On the other hand, islanding is a situation in which a section of the power system, which is separated from the main grid and includes electrical loads and distributed generations (DGs), can be supplied by the DGs. Photovoltaic (PV) units, diesel generators, energy storage systems (ESSs), and wind energy farms (WEFs) are some examples of DGs that can feed the electrical loads or transfer the surplus power to the main grid (see Fig. 1).

Fig. 1.
Fig. 1.

The structure of a sample MG

Citation: International Review of Applied Sciences and Engineering 14, 2; 10.1556/1848.2022.00467

The power system benefits from the advantages of MGs including better system efficiency, cost reduction (due to utilizing the small-scaled DGs), improvement of power quality, and increased both power system reliability and flexibility (due to the capability of working at two operation modes) [3]. Moreover, enhancement in the stability of the power system, and reduction in global warming and pollution by the implementation of technologies with low (or without) carbon utilization can be mentioned as the significant advantages of the MGs [4]. Regardless of the abovementioned benefits, the most important issue in MGs is the detection of the islanded mode and protection actions to be adapted to this condition. In comparison to the islanded mode, the fault current is very high in the grid-connected mode, which affects the power system protection due to the significant changes in the settings of the protective relays [5, 6]. As long as a MG is connected to the main grid, if a fault occurs within the MG, a large contribution of the fault current flows from the main grid to the fault point. On the other hand, when MG separates from the main grid, this current will no longer exist and the fault current in MG is reduced. Therefore, due to the necessity of coordination among the overcurrent relays (which are the most common relays in the power system), in a MG with one group of setting for overcurrent relays, one can not expect protection with the high speed and sensitivity of the protection system that relies only on the level of fault current. Consequently, the settings of protective relays should be updated by the change in the operation mode of MG. So, there is a need for methods to detect the islanded mode and adapt the corresponding protection orders to the protective relays. These methods are known as islanding detection methods (IDMs).

Islanding mode must be detected in less than 2 s based on the standards such as IEEE 929-2000, IEEE 1547, and IEC 62116 [7]. So, islanding detection in MGs is an important issue for the control and protection of the power system. Achieving an efficient IDM with the capability of fast and accurate detection is one of the essential requirements in power systems.

In this study, a detailed investigation of existing, most cited, and state-of-the-art publications on IDMs is presented. The presented investigations will be helpful for the power system engineers and researchers to identify the most desirable IDMs.

After the introduction section, the overview of the paper is addressed in section 2. Overall statistics corresponding to islanding detection are provided in section 3. The fourth section categories the IDMs and performs a comprehensive investigation of the most cited publications and state-of-the-art methods. Finally, the fifth section presents the conclusions.

2 Overview of the paper

2.1 Database

In this paper, the bibliographic records associated with the selected issue are obtained from DOAJ, Nature Index journals, Norwegian register, VABB-SHW, UGC Journal List, China Journal Initiative, PubMed, ERIH PLUS, J-STAGE, SciELO, and ERA.

2.2 The searching procedure

As the main structure of this paper, data about the selected issue with concertation on most cited publications, and state-of-the-art methods are highlighted, and related statistical analyses are presented. The search procedure is started by the selection of keywords that are separated by the term “and” (see Fig. 2). Then, the overall investigation of the issue includes the total publications just for the last decade (i.e., 2012–2021), type of publications (journal, conference, book, or chapter), five most published authors with their affiliations along with the number of publications, and five most published sources are presented. Finally, the most cited and state-of-the-art publications of the selected issue are analyzed with some known and unique categorizations to obtain the concentration and concern of researchers on the selected issue.

Fig. 2.
Fig. 2.

The overview and the procedure of searching

Citation: International Review of Applied Sciences and Engineering 14, 2; 10.1556/1848.2022.00467

To obtain the exact results and prevent investigating irrelevant studies, the search is applied in the title and abstract of the aforementioned databases.

3 A statistics-based search on islanding detection issues

To make an accurate investigation of existing researches on IDMs, firstly, a search is performed by keyword “Microgrid and islanding detection”. The statistical analyses are available in Fig. 3. It can be seen from Fig. 3(a) that 302 studies are published for islanding detection in the pre-defined period. The distributions of publications are 54% journal, 40% conference, and 5% in the type of book or chapters (see Fig. 3(b)). It is observed from Fig. 3(c), “P. Jena” from “Indian Institute of Technology Roorkee” is that the author with the most publications (i.e., 13 publications) in islanding detection issue. Also, “P. K. Dash”, “V. Kumar”, and “S. R. Samantaray” placed second to fourth place respectively. Moreover, “M. Biswal” with eight publications is the fifth most published author on this list. Besides, as can be seen from Fig. 3(d), “IEEE Transactions on Smart Grid”, “IET Generation, Transmission & Distribution”, and “Energies”, with fifteen, twelve, and ten publications respectively, and “International Journal of Electrical power and Energy Systems”, and “Lecture Notes in Electrical Engineering” both with nine publications stand on next stages of the five most published sources.

Fig. 3.
Fig. 3.

The search of the keyword “Microgrid and islanding detection” (a) Total publications (b) Publications type (c) Five most published authors (d) Five most published sources

Citation: International Review of Applied Sciences and Engineering 14, 2; 10.1556/1848.2022.00467

4 Island detection methods

4.1 Comprehensive categorizations of islanding detection methods

IDMs can be categorized into two main types: remote, and local (including active, passive, and hybrid) [8]. Remote or communication-based IDMs detect the islanding mode when the signal receiving from DGs is cut off. Therefore, regardless of the reliability and fast performance, this type of IDMs is not cost-effective due to expensive implementations [9]. Supervisory control and data acquisition (SCADA) and power line carrier (PLC) are the systems for transferring data in remote IDMs.

In the active IDMs, disturbances are injected into the power system and then the islanding mode is detected depending on the reactions of the systems [10]. These methods have a negative impact on the network's power quality and stability due to the injected disturbances [11, 12].

Some of active IDMs are Sandia frequency shift (SFS) [13], active frequency drift (AFD) [14], current disturbance injection and reactive power variation (RPV) [15], active frequency deviation with positive feedback (AFDPF) [16, 17], voltage positive feedback (VPF) [18, 19], injection of a negative-sequence current [20, 21], phase shift of current [22], virtual inductor [23], virtual capacitor [24], and slip-mode frequency shift (SMS) [25].

On the other hand, in passive IDMs, a parameter (or some parameters) of the system is considered as an index and is analyzed by determining the threshold value. If the desired parameter exceeds the value of the pre-determined threshold, the system is in islanded mode [26].

Despite, no effect on the power quality, passive IDMs suffer from the high computational burden. Moreover, running numerous simulations to identify the appropriate threshold value of islanding mode, and having large nondetection zones (NDZs) are some of the disadvantages of passive IDMs [26].

Some of the passive IDMs are under or over voltage [2728], rate of change of frequency (ROCOF) [29, 30], rate of change of reactive power [31], voltage signal [32–35], rate of change of positive and negative sequence of current [36], impedance monitoring [37], and total harmonic distortion (THD) [38]. Also, there are some passive IDMs that utilize two parameters of the network for decision-making such as the average rate of change of reactive power and load shift [39], rate of changes of voltage to frequency [40, 41], frequency and voltage [42, 43], current and voltage [44], harmonics and voltage [45], and power and voltage [46, 47]. Moreover, some studies utilized the monitoring and analysis of three or more parameters of the network such as the relationship of the load on frequency and voltage [48], and under or overvoltage, under or over frequency, and phase jump [49].

The hybrid IDMs integrate the passive and active methods, which increase the accuracy of detection, overcome the drawbacks of both passive and active IDMs, improve the islanding detection capability, and reduce the NDZ [50].

As an example of hybrid IDMs, there is a study in which first the perturbation is injected (as the active mode) and then, active power, voltage harmonics, current harmonics, reactive power, and the rates of change of frequency are measured (as the passive mode) for final decision-making about islanding or non-islanding mode [51]. Also, in [52] a hybrid method is proposed that employs voltage unbalance (VU) (as the passive mode) and changes in the voltage phase angle (VPA) as noise (as the active mode). Moreover, in [53], a hybrid IDM is proposed that is based on the injection of the current disturbance (as the active mode), and monitoring the under or over voltage (as the passive mode). In another hybrid IDM, the connection of a reactive impedance at PCC is considered as the active mode and the measurement of the rate of change of voltage (ROCOV) is considered as the passive mode [54].

As the other categorization, the IDMs can be divided into artificial intelligence (AI)-based methods including fuzzy inference system (FIS), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), etc., and as the second type, the mathematics-based methods including Fourier transform (FT), S-transform (ST), wavelet transform (WT), Sparse correlation, etc. Mathematics is used in some IDMs for extracting the features from the parameters of the network for subsequent detection purposes. The mathematics-based IDMs give reliable results but as a disadvantage, the computational burden is high. On the other hand, the AI-based IDMs regardless of the acceptable results suffer from complexity and time-consuming procedures for training data. It is to be mentioned that there are some IDMs that are just based on monitoring the behaviour of the parameters of the network and the final decision is just taken based on the modelling of the network.

Moreover, as a novel categorization, the IDMs can be divided into phasor measurement-based and non-phasor measurement-based methods. In phasor measurement-based type the angle of the network's parameters such as active power, voltage, current, and reactive power are utilized in analyses for decision-making. On the other hand, in the non-phasor measurement-based type, the amplitude of the network's parameters is used for islanding detection purposes. The drawback of non-phasor measurement-based IDMs is the high computational burden to decide about islanding or non-islanding conditions. On the other hand, the phasor-measurement-based type is fast and accurate but suffers from complexity in analyzing data and expensive devices for the sampling of the phasor data.

A comprehensive comparison between the abovementioned categorizations of IDMs is given in Table 1.

Table 1.

Comparison between the IDMs types

IDMsCharacteristic
Less computational burdenNo need for training dataCost-effectiveLess complexity
Non-phasor measurement-based
Phasor measurement-based
AI-based
Mathematics-based

✓: In accord with the characteristic ✗: Not in accord with the characteristic.

4.2 Most cited papers on islanding detection issue

Investigation of most cited researches helps the researchers to identify the technique that is most applicable and desirable on a special issue. References [55–59] are the five most cited papers in the IDM issue (see Fig. 4).

Fig. 4.
Fig. 4.

Five most cited papers for IDMs

Citation: International Review of Applied Sciences and Engineering 14, 2; 10.1556/1848.2022.00467

Reference [55] by “G. Hernandez-Gonzalez” and “R. Iravani” from “University of Toronto” is cited 327 times. It is an active IDM (since the injection of current disturbance), and utilized the amplitude of voltage (i.e., non-phasor measurement-based) for detection purposes. Also, it is based on network modelling and monitoring.

Reference [56], with 305 citations is a hybrid, non-phasor measurement-based IDM that utilized positive feedback as the active mode and subsequently, THD and VU as the passive mode.

The third one is reference [57] with 186 citations that is an active IDM based on measurements of impedance after injection of the voltage (i.e., non-phasor measurement-based).

Reference [58] as the fourth most cited paper with 179 citations utilized the rate of change of phase angle difference of current and voltage signals (i.e., passive and phasor measurement-based IDM) along with network modelling and monitoring for detection purposes.

Finally, reference [59] that is a passive IDM based on WT technique (i.e., mathematics-based) and voltage signal (i.e., non-phasor measurement-based) is known as the fifth most cited paper.

It can be concluded that among the most cited papers, hybrid methods are the least desirable among the researchers, and network modeling and monitoring technique with non-phasor data are the most desirable categories among the IDMs. Moreover, it is remarkable that the most cited papers were published from 2006 to 2012.

4.3 State-of-the-art island detection methods

Investigation of state-of-the-art methods helps the researchers to find the recent approaches in the special field and make an insightful overview for future directions of the issue. So, considering the aforementioned definitions for categorizations related to IDMs, a comprehensive search of state-of-the-art IDMs is given in Table 2. It is to be noted that only the “original research” papers are utilized to be investigated as state-of-the-art IDMs in Table 2. Moreover, authors with the corresponding country, detection tool, technique or network's parameter, detection time, and accuracy of the researches are highlighted in Table 2.

Table 2.

State-of-the-art publications related to IDMs

Authors/ReferenceCountryActive/Passive/HybridIntelligence-based/Mathematics-based/Both//Network modelling and monitoringDetection toolPhasor measurement-based/Non-phasor measuremet-basedNetwork parameter/TechniqueDetection time (ms)Accuracy (%)
Y. A. Elshrief et al., [60]EgyptPIDMNMMNPHRate of change of power based on the terminal voltage (ROCOP-TV)8NM
M. Karimi et al., [61]FinlandPIDMNMMPHVoltage and current phasorsLess than 2000NM
S. Barczentewicz et al., [62]PolandPIDMNMMNPHVoltage amplitude, frequency, and rate of change of frequency (ROCOF)Less than 300NM
R. Bakhshi-Jafarabadi and M. Popov, [63]IranHIDMNMMNPHInject the disturbance into the voltage source as the active mode, measure the drop of voltage at PCC and active power as the passive mode300NM
E. N. Prasad and P. K. Dash, [64]IndiaPIDMMA & AIDetrended fluctuation analysis (DFA), adaptive variational mode decomposition

(AVMD), and improved particle swarm optimization (IPSO)
NPHCurrent signalsNM97.33–99.33
N. V. Eluri et al., [65]IndiaPIDMMA & AIVariational mode decomposition (VMD), and weighted kurtosis index, and modified particle swarm optimization (MPSO)NPHVoltage and current36 and 4299
M. Seyedi et al., [66]IranHIDMNMMNPHRate of change of voltage (ROCOV) as the passive mode, and rate of change of active power (ROCOAP) as the active mode2013–2042NM
Q. Huang et al., [67]ChinaAIDMNMMNPHVoltage positive feedback of selected frequency (VPFOSF)1,351NM
A. Shukla et al., [68]IndiaPIDMMAFortescue transform (FTT)PHAngle difference between positive and negative sequence components/phase angle of voltage10NM
A. Damanjani et al., [69]IranPIDMAIFuzzy c-means (FCM) clusteringNPHFault current level20NM
S. K. Singh et al., [70]IndiaHIDMNMMNPHDisturbance (load change) as the active mode

, and voltage unbalance and rate of change of frequency (ROCOF) as the passive mode
22NM
L. Ma et al., [71]ChinaAIDMNMMNPHVoltage/Active frequency drift with positive feedback (AFDPF)60NM
P. P. Tikar et al., [72]IndiaPIDMMA & AIDiscrete wavelet transform (DWT), K-nearest neighbor (KNN), and support vector machine (SVM)NPHVoltage and current120–15095.23–100
R. Zamani et al., [73]IranPIDMMAHilbert-Huang transform (HHT)NPHOscillation of frequency370–450NM
X. Xie et al., [74]ChinaPIDMNMMPHRate of change of power factor angle (RCPFA) and rate of change of frequency (ROCOF)68NM
A. Ezzat et al., [75]EgyptPIDMMA & AIDiscrete Fourier transform (DFT), and K-nearest neighbor (KNN)NPHVoltage and current5–1599.69
M. Mohiti et al., [76]IranHIDMNMMNPHInjection of voltage as the active mode, and monitoring the total harmonic distortion (THD) as the passive mode1,000NM
O. A.Allan and W. G. Morsi, [77]CanadaPIDMMA & AIContinuous wavelet transform (CWT), and convolution neural network (CNN)NPHVoltage and current21098.6
R. Bakhshi-Jafarabadi et al., [78]IranHIDMNMMMaximum power point tracking (MPPT)NPHVoltage deviation as the passive mode, and disturnabce voltage as the active mode137NM
A. Serrano-Fontova et al., [79]SpainHIDMMAState variableNPHVoltage and rate of change of frequency as the passive mode, and switching the load as the active mode120NM
H. Khosravi et al., [80]IranPIDMNMMSwing equationNPHVoltage and current/Rate of change of kinetic energy over reactive power (ROCOKORP)50–58.399.28
S. V. Kulkarni et al., [81]IndiaAIDMNMMPark synchronous reference frame based phase-locked

loop (PSRF-PLL)
PHActive power, reactive power, frequency, voltage, and grid phase angle response10NM
A. Kumar et al., [82]IndiaAIDMNMMNPHFrequency/Estimation of signal parameter via rotational invariance technique (ESPRIT)120NM
R. Nale et al., [83]IndiaPIDMNMMPHVoltage and current/Phase angle difference information of superimposed impedanceLess than 2,000NM
A. K. Özcanlı and M. Baysal, [84]TurkeyPIDMAINPHVoltage and current

/

Multi-long short-term memory (LSTM) architecture
5097.93

AIDM: Active IDM

PIDM: Passive IDM

HIDM: Hybrid IDM

AI: AI-based

MA: Mathematics-based

PH: Phasor measurement-based

NPH: Non-phasor measurement-based

NMM: Network modelling and monitoring

NM: Not mentioned

As presented in Table 2, most IDMs are focused on detection time rather than accuracy which illustrates the significance of quick detection to perform subsequent proper protection actions. Also, the ranges in the “detection time” column indicate the different scenarios or cases. Moreover, reference [72] provided the most accurate IDM among the recent studies, and the proposed methods in references [60], [68], [69], [75], and [81] are reported to detect the islanded mode within 20 ms that can be known as fast detection IDMs.

Also, according to the results that are given in Table 2, the concentrations of recent IDMs are on passive methods (see Fig. 5(a)). Furthermore, 60% of the recent IDMs are specialized to network modelling and monitoring procedures (see Fig. 5(b)). Moreover, the combination of both mathematics-based methods and artificial intelligence-based methods contributes 20% of the methods that shows the researchers' desire in using AI tools along with mathematics techniques (see Fig. 5(b)).

Fig. 5.
Fig. 5.

Categorizations contribution on state-of-the-art IDMS (a) Type of method (b) Detection tool (c) Usage/not usage of phasor measurements data

Citation: International Review of Applied Sciences and Engineering 14, 2; 10.1556/1848.2022.00467

In addition, due to the expensive implementation and complexity of phasor measurement-based IDMs, non-phasor measurement-based IDMs are utilized more for detection purposes (see Fig. 5(c)).

Finally, it can be concluded from Table 2, among the recent studies on IDMs, India, and Iran totally with more than 50% of publications play a vital role in publishing the research papers among the other countries.

5 Conclusions

In this study, a review of IDMs is presented that is based on statistics obtained from known databases. Considering the known categorizations (i.e., active, passive, hybrid, AI-based, and mathematics-based), and a novel categorization (i.e., phasor/non-phasor measurement-based), a detailed investigation of the most cited methods and state-of-the-art IDMs is performed. According to the analyses, most of the researches both on most cited and recent studies on IDMs utilized network modelling and monitoring for detection purposes. Besides, among state-of-the-art IDMs, it can be concluded that most of the researchers tend to retain the power quality by providing more passive methods. Moreover, it is illustrated that the researchers tend to reduce the complexity and cost in recent studies by utilization of non-phasor data. The findings of this review paper are essential and practical for the researchers of MG issue and especially for the electrical engineers of the power system, for future programming and improvement of the protection system.

References

  • [1]

    M. Sadoughi, M. Hojjat, and M. Hosseini Abardeh, “Smart overcurrent relay for operating in islanded and grid-connected modes of a micro-grid without needing communication systems,” Energy Syst, vol. 30, pp. 121, 2020.

    • Search Google Scholar
    • Export Citation
  • [2]

    M. Hosseinzadeh and F. Rajaei Salmasi, “Islanding fault detection in microgrids—A survey,” Energies, vol. 13, no. 13, pp. 128, 2020.

    • Search Google Scholar
    • Export Citation
  • [3]

    B. Patnaik, M. Mishra, R. C. Bansal, and R. K. Jena, “AC microgrid protection–a review: Current and future prospective,” Appl. Energy, vol. 271, pp. 115210115237, 2020.

    • Search Google Scholar
    • Export Citation
  • [4]

    S. Choudhury, “A comprehensive review on issues, investigations, control and protection trends, technical challenges and future directions for Microgrid technology,” Int. Trans. Electr. Energy Syst., vol. 30, no. 9, pp. 1244612461, 2020.

    • Search Google Scholar
    • Export Citation
  • [5]

    M. Sadoughi, M. Hojjat, and M. Hosseini Abardeh, “An intelligent adaptive overcurrent protection system for an automated microgrid in islanded and grid-connected operation modes,” Int. J. Nonlinear Anal. Appl., vol. 11, no. 1, pp. 381394, 2020.

    • Search Google Scholar
    • Export Citation
  • [6]

    A. Damanjani, M. H. Abardeh, A. Azarfar, and M. Hojjat, “A statistics-based review of microgrid protection with a concentration on adaptive protection,” Int. Rev. Appl. Sci. Eng., vol. 12, no. 3, pp. 312323, 2021.

    • Search Google Scholar
    • Export Citation
  • [7]

    J. R. Lim, H. M. Hwang, W. G. Shin, H. J. Song, Y. C. Ju, Y. S. Jung, G. H. Kang, and S. W. Ko, “Case studies for non-detection of islanding by grid-connected in-parallel photovoltaic and electrical energy storage systems inverters,” Appl. Sci., vol. 9, no. 5, pp. 817827, 2019.

    • Search Google Scholar
    • Export Citation
  • [8]

    R. Bakhshi-Jafarabadi and J. Sadeh, “New voltage feedback-based islanding detection method for grid-connected photovoltaic systems of microgrid with zero non-detection zone,” IET Renew. Power Gener., vol. 14, no. 10, pp. 17101719, 2020.

    • Search Google Scholar
    • Export Citation
  • [9]

    H. Samet, F. Hashemi, and T. Ghanbari, “Islanding detection method for inverter-based distributed generation with negligible non-detection zone using energy of rate of change of voltage phase angle,” IET Gener. Transm. Distrib., vol. 9, no. 15, pp. 23372350, 2015.

    • Search Google Scholar
    • Export Citation
  • [10]

    A. A. Abdelsalam, A. A. Salem, E. S. Oda, and A. A. Eldesouky, “Islanding detection of microgrid incorporating inverter based DGs using long short-term memory network,” IEEE Access, vol. 8, pp. 106471106486, 2020.

    • Search Google Scholar
    • Export Citation
  • [11]

    J. S. Kim, C. H. Kim, Y. S. Oh, G. J. Cho, and J. S. Song, “An islanding detection method for multi-RES systems using the graph search method,” IEEE Trans. Sustain. Energy, vol. 11, no. 4, pp. 27222731, 2020.

    • Search Google Scholar
    • Export Citation
  • [12]

    E. J. Estébanez, V. M. Moreno, A. Pigazo, M. Liserre, and A. Dell'Aquila, “Performance evaluation of active islanding-detection algorithms in distributed-generation photovoltaic systems: two inverters case,” IEEE Trans. Industr. Electron., vol. 58, no. 4, pp. 11851193, 2010.

    • Search Google Scholar
    • Export Citation
  • [13]

    H. H. Zeineldin and S. Kennedy, “Sandia frequency-shift parameter selection to eliminate nondetection zones,” IEEE Trans. Power Deliv., vol. 24, no. 1, pp. 486487, 2008.

    • Search Google Scholar
    • Export Citation
  • [14]

    M. E. Ropp, M. Begovic, and A. Rohatgi, “Analysis and performance assessment of the active frequency drift method of islanding prevention,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 810816, 1999.

    • Search Google Scholar
    • Export Citation
  • [15]

    X. Xie, C. Huang, and D. Li, “A new passive islanding detection approach considering the dynamic behavior of load in microgrid,” Int. J. Electr. Power Energy Syst., vol. 117, pp. 105619105629, 2020.

    • Search Google Scholar
    • Export Citation
  • [16]

    Y. Jung, J. Choi, B. Yu, J. So, and G. Yu, “A novel active frequency drift method of islanding prevention for the grid-connected photovoltaic inverter,” in 36th Power Electron. Specialists Conf., 2005, pp. 19151921.

    • Search Google Scholar
    • Export Citation
  • [17]

    A. Yafaoui, B. Wu, and S. Kouro, “Improved active frequency drift anti-islanding detection method for grid connected photovoltaic systems,” IEEE Trans. Power Electron., vol. 27, no. 5, pp. 23672375, 2011.

    • Search Google Scholar
    • Export Citation
  • [18]

    F. J. Lin, Y. S. Huang, K. H. Tan, J. H. Chiu, and Y. R. Chang, “Active islanding detection method using d–axis disturbance signal injection with intelligent control,” IET Gener. Transm. Distrib., vol. 7, no. 5, pp. 537550, 2013.

    • Search Google Scholar
    • Export Citation
  • [19]

    A. Samui and S. R. Samantaray, “An active islanding detection scheme for inverter–based DG with frequency dependent ZIP–Exponential static load model,” Int. J. Electr. Power Energy Syst., vol. 78, pp. 4150, 2016.

    • Search Google Scholar
    • Export Citation
  • [20]

    B. Bahrani, H. Karimi, and R. Iravani, “Nondetection zone assessment of an active islanding detection method and its experimental evaluation,” IEEE Trans. Power Deliv., vol. 26, no. 2, pp. 517525, 2009.

    • Search Google Scholar
    • Export Citation
  • [21]

    P. K. Ganivada and P. Jena, “Frequency disturbance triggered d-axis current injection scheme for islanding detection,” IEEE Trans. Smart Grid, vol. 11, no. 6, pp. 45874603, 2020.

    • Search Google Scholar
    • Export Citation
  • [22]

    G. K. Hung, C. C. Chang, and C. L. Chen, “Automatic phase-shift method for islanding detection of grid-connected photovoltaic inverters,” IEEE Trans. Energy Convers., vol. 18, no. 1, pp. 169173, 2003.

    • Search Google Scholar
    • Export Citation
  • [23]

    H. L. Jou, W. J. Chiang, and J. C. Wu, “Virtual inductor-based islanding detection method for grid-connected power inverter of distributed power generation system,” IET Renew. Power Gener., vol. 1, no. 3, pp. 175181, 2007.

    • Search Google Scholar
    • Export Citation
  • [24]

    W. J. Chiang, H. L. Jou, and J. C. Wu, “Active islanding detection method for inverter-based distribution generation power system,” Int. J. Electr. Power Energy Syst., vol. 42, no. 1, pp. 158166, 2012.

    • Search Google Scholar
    • Export Citation
  • [25]

    F. Liu, Y. Kang, Y. Zhang, S. Duan, and X. Lin, “Improved SMS islanding detection method for grid-connected converters,” IET Renew. Power Gener., vol. 4, no. 1, pp. 3642, 2010.

    • Search Google Scholar
    • Export Citation
  • [26]

    M. Sadoughi, M. Hojjat, and M. Hosseini Abardeh, “Detection of islanding, operation and reconnection of microgrids to utility grid using local information,” Int. Trans. Electr. Energy Syst., vol. 30, no. 8, pp. 1247212491, 2020.

    • Search Google Scholar
    • Export Citation
  • [27]

    J. C. Vieira, W. Freitas, W. Xu, and A. Morelato, “Performance of frequency relays for distributed generation protection,” IEEE Trans. Power Deliv., vol. 21, no. 3, pp. 11201127, 2006.

    • Search Google Scholar
    • Export Citation
  • [28]

    J. C. Vieira, D. S. Correa, W. Freitas, and W. Xu, “Performance curves of voltage relays for islanding detection of distributed generators,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 16601662, 2005.

    • Search Google Scholar
    • Export Citation
  • [29]

    D. Bejmert and T. S. Sidhu, “Investigation into islanding detection with capacitor insertion-based method,” IEEE Trans. Power Deliv., vol. 29, no. 6, pp. 24852492, 2014.

    • Search Google Scholar
    • Export Citation
  • [30]

    F. Hashemi, M. Mohammadi, and A. Kargarian, “Islanding detection method for microgrid based on extracted features from differential transient rate of change of frequency,” IET Gener. Transm. Distrib., vol. 11, no. 4, pp. 891904, 2017.

    • Search Google Scholar
    • Export Citation
  • [31]

    S. Nikolovski, H. R. Baghaee, and D. Mlakić, “Islanding detection of synchronous generator-based DGs using rate of change of reactive power,” IEEE Syst. J., vol. 13, no. 4, pp. 43444354, 2019.

    • Search Google Scholar
    • Export Citation
  • [32]

    J. Merino, P. Mendoza-Araya, G. Venkataramanan, and M. Baysal, “Islanding detection in microgrids using harmonic signatures,” IEEE Trans. Power Deliv., vol. 30, no. 5, pp. 21022109, 2014.

    • Search Google Scholar
    • Export Citation
  • [33]

    S. R. Mohanty, N. Kishor, P. K. Ray, and J. P. Catalo, “Comparative study of advanced signal processing techniques for islanding detection in a hybrid distributed generation system,” IEEE Trans. Sustain. Energy, vol. 6, no. 1, pp. 122131, 2014.

    • Search Google Scholar
    • Export Citation
  • [34]

    A. M. Niaki and S. Afsharnia, “A new passive islanding detection method and its performance evaluation for multi-DG systems,” Electric Power Syst. Res., vol. 110, pp. 180187, 2014.

    • Search Google Scholar
    • Export Citation
  • [35]

    A. Khamis, Y. Xu, Z. Y. Dong, and R. Zhang, “Faster detection of microgrid islanding events using an adaptive ensemble classifier,” IEEE Trans. Smart Grid, vol. 9, no. 3, pp. 18891899, 2016.

    • Search Google Scholar
    • Export Citation
  • [36]

    K. Sareen, B. R. Bhalja, and R. P. Maheshwari, “Universal islanding detection technique based on rate of change of sequence components of currents for distributed generations,” IET Renew. Power Gener., vol. 10, no. 2, pp. 228237, 2016.

    • Search Google Scholar
    • Export Citation
  • [37]

    P. O'kane and B. Fox, “Loss of mains detection for embedded generation by system impedance monitoring,” 6th Int. Conf. Dev. Power Syst. Prot., 1997, pp. 9598.

    • Search Google Scholar
    • Export Citation
  • [38]

    S. I. Jang and K. H. Kim, “An islanding detection method for distributed generations using voltage unbalance and total harmonic distortion of current,” IEEE Trans. Power Deliv., vol. 19, no. 2, pp. 745752, 2004.

    • Search Google Scholar
    • Export Citation
  • [39]

    J. A. Laghari, H. Mokhlis, M. Karimi, A. H. Bakar, and H. Mohamad, “An islanding detection strategy for distribution network connected with hybrid DG resources,” Renew. Sustain. Energy Rev., vol. 45, pp. 662676, 2015.

    • Search Google Scholar
    • Export Citation
  • [40]

    S. J. Huang and F. S. Pai, “A new approach to islanding detection of dispersed generators with self-commutated static power converters,” IEEE Trans. Power Deliv., vol. 15, no. 2, pp. 500507, 2000.

    • Search Google Scholar
    • Export Citation
  • [41]

    F. S. Pai and S. J. Huang, “A detection algorithm for islanding-prevention of dispersed consumer-owned storage and generating units,” IEEE Trans. Energy Convers., vol. 16, no. 4, pp. 346351, 2001.

    • Search Google Scholar
    • Export Citation
  • [42]

    X. Chen, Y. Li, and P. Crossley, “A novel hybrid islanding detection method for grid-connected microgrids with multiple inverter-based distributed generators based on adaptive reactive power disturbance and passive criteria,” IEEE Trans. Power Electron., vol. 34, no. 9, pp. 93429356, 2018.

    • Search Google Scholar
    • Export Citation
  • [43]

    C. N. Papadimitriou, V. A. Kleftakis, and N. D. Hatziargyriou, “A novel method for islanding detection in DC networks,” IEEE Trans. Sustain. Energy, vol. 8, no. 1, pp. 441448, 2016.

    • Search Google Scholar
    • Export Citation
  • [44]

    N. W. Lidula and A. D. Rajapakse, “A pattern recognition approach for detecting power islands using transient signals-Part I: design and implementation,” IEEE Trans. Power Deliv., vol. 25, no. 4, pp. 30703077, 2010.

    • Search Google Scholar
    • Export Citation
  • [45]

    M. E. Ropp, M. Begovic, A. Rohatgi, G. A. Kern, R. H. Bonn, and S. Gonzalez, “Determining the relative effectiveness of islanding detection methods using phase criteria and nondetection zones,” IEEE Trans. Energy Convers., vol. 15, no. 3, pp. 290296, 2000.

    • Search Google Scholar
    • Export Citation
  • [46]

    Q. Sun, J. M. Guerrero, T. Jing, J. C. Vasquez, and R. Yang, “An islanding detection method by using frequency positive feedback based on FLL for single-phase microgrid,” IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 18211830, 2015.

    • Search Google Scholar
    • Export Citation
  • [47]

    S. K. Salman, D. J. King, and G. Weller, “New loss of mains detection algorithm for embedded generation using rate of change of voltage and changes in power factors,” 7th Int. Conf. Dev. Power Syst. Prot., 2001, pp. 8285.

    • Search Google Scholar
    • Export Citation
  • [48]

    R. Bekhradian, M. Davarpanah, and M. Sanaye-Pasand, “Novel approach for secure islanding detection in synchronous generator based microgrids,” IEEE Trans. Power Deliv., vol. 34, no. 2, pp. 457466, 2018.

    • Search Google Scholar
    • Export Citation
  • [49]

    Z. Ye, A. Kolwalkar, Y. Zhang, P. Du, and R. Walling, “Evaluation of anti-islanding schemes based on nondetection zone concept,” IEEE Trans. Power Electron., vol. 19, no. 5, pp. 11711176, 2004.

    • Search Google Scholar
    • Export Citation
  • [50]

    S. C. Paiva, R. L. de Araujo Ribeiro, D. K. Alves, F. B. Costa, and T. D. Rocha, “A wavelet-based hybrid islanding detection system applied for distributed generators interconnected to AC microgrids,” Int. J. Electr. Power Energy Syst., vol. 121, pp. 106032106041, 2020.

    • Search Google Scholar
    • Export Citation
  • [51]

    G. Wang, J. Wang, Z. Zhou, Q. Wang, Q. Wu, X. Jiang, and E. Santana, “State variable technique islanding detection using time-frequency energy analysis for DFIG wind turbine in microgrid system,” ISA Trans., vol. 80, pp. 360370, 2018.

    • Search Google Scholar
    • Export Citation
  • [52]

    M. Seyedi, S. A. Taher, B. Ganji, and J. M. Guerrero, “A hybrid islanding detection technique for inverter-based distributed generator units,” Int. Trans. Electr. Energy Syst., vol. 29, no. 11, pp. 1211312133, 2019.

    • Search Google Scholar
    • Export Citation
  • [53]

    G. S. Seo, K. C. Lee, and B. H. Cho, “A new DC anti-islanding technique of electrolytic capacitor-less photovoltaic interface in DC distribution systems,” IEEE Trans. Power Electron., vol. 28, no. 4, pp. 16321641, 2012.

    • Search Google Scholar
    • Export Citation
  • [54]

    A. Rostami, A. Jalilian, S. Zabihi, J. Olamaei, and E. Pouresmaeil, “Islanding detection of distributed generation based on parallel inductive impedance switching,” IEEE Syst. J., vol. 14, no. 1, pp. 813823, 2019.

    • Search Google Scholar
    • Export Citation
  • [55]

    G. Hernandez-Gonzalez and R. Iravani, “Current injection for active islanding detection of electronically-interfaced distributed resources,” IEEE Trans. Power Deliv., vol. 21, no. 3, pp. 16981705, 2006.

    • Search Google Scholar
    • Export Citation
  • [56]

    V. Menon and M. H. Nehrir, “A hybrid islanding detection technique using voltage unbalance and frequency set point,” IEEE Trans. Power Syst., vol. 22, no. 1, pp. 442448, 2007.

    • Search Google Scholar
    • Export Citation
  • [57]

    D. Reigosa, F. Briz, C. B. Charro, P. García, and J. M. Guerrero, “Active islanding detection using high-frequency signal injection,” IEEE Trans. Ind. Appl., vol. 48, no. 5, pp. 15881597, 2012.

    • Search Google Scholar
    • Export Citation
  • [58]

    A. Samui and S. R. Samantaray, “Assessment of ROCPAD relay for islanding detection in distributed generation,” IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 391398, 2011.

    • Search Google Scholar
    • Export Citation
  • [59]

    A. Samui and S. R. Samantaray, “Wavelet singular entropy-based islanding detection in distributed generation,” IEEE Trans. Power Deliv., vol. 28, no. 1, pp. 411418, 2012.

    • Search Google Scholar
    • Export Citation
  • [60]

    Y. A. Elshrief, D. H. Helmi, S. Abd-Elhaleem, B. A. Abozalam, and A. D. Asham, “Fast and accurate islanding detection technique for microgrid connected to photovoltaic system,” J. Radiat. Res. Appl. Sci., vol. 14, no. 1, pp. 210221, 2021.

    • Search Google Scholar
    • Export Citation
  • [61]

    M. Karimi, M. Farshad, Q. Hong, H. Laaksonen, and K. Kauhaniemi, “An islanding detection technique for inverter-based distributed generation in microgrids,” Energies, vol. 14, no. 1, pp. 130147, 2021.

    • Search Google Scholar
    • Export Citation
  • [62]

    S. Barczentewicz, T. Lerch, A. Bień, and K. Duda, “Laboratory evaluation of a phasor-based islanding detection method,” Energies, vol. 14, no. 7, pp. 19531969, 2021.

    • Search Google Scholar
    • Export Citation
  • [63]

    R. Bakhshi-Jafarabadi and M. Popov, “Hybrid islanding detection method of photovoltaic-based microgrid using reference current disturbance,” Energies, vol. 14, no. 5, pp. 13901405, 2021.

    • Search Google Scholar
    • Export Citation
  • [64]

    E. N. Prasad and P. K. Dash, “Islanding detection in different configurations of multiple photovoltaic distributed generation‐based direct current microgrid using improved mode decomposition technique,” Int. Trans. Electr. Energy Syst., vol. 31, no. 3, pp. 1279612815, 2021.

    • Search Google Scholar
    • Export Citation
  • [65]

    N. V. Eluri, P. K. Dash, and S. Dhar, “Islanding detection in photovoltaic based DC micro grid using adaptive variational mode decomposition and detrended fluctuation analysis,” IET Gener. Transm. Distrib., vol. 15, no. 4, pp. 631644, 2021.

    • Search Google Scholar
    • Export Citation
  • [66]

    M. Seyedi, S. A. Taher, B. Ganji, and J. Guerrero, “A hybrid islanding detection method based on the rates of changes in voltage and active power for the multi-inverter systems,” IEEE Trans. Smart Grid, vol. 12, no. 4, pp. 28002811, 2021.

    • Search Google Scholar
    • Export Citation
  • [67]

    Q. Huang, H. Chen, X. Xiang, C. Li, W. Li, and X. He, “Islanding detection with positive feedback of selected frequency for DC microgrid systems,” IEEE Trans. Power Electron., vol. 36, no. 10, pp. 1180011817, 2021.

    • Search Google Scholar
    • Export Citation
  • [68]

    A. Shukla, S. Dutta, and P. K. Sadhu, “An island detection approach by μ-PMU with reduced chances of cyber attack,” Int. J. Electr. Power Energy Syst., vol. 126, pp. 106599106611, 2021.

    • Search Google Scholar
    • Export Citation
  • [69]

    A. Damanjani, M. Hosseini Abardeh, A. Azarfar, and M. Hojjat, “A novel scheme for island detection in microgrids based on fuzzy c-means clustering technique,” Int. Rev. Appl. Sci. Eng., vol. 12, no. 2, pp. 157165, 2021.

    • Search Google Scholar
    • Export Citation
  • [70]

    S. K. Singh, M. Rawal, M. S. Rawat, and T. N. Gupta, “Hybrid islanding detection technique for inverter based microgrid,” in 2nd Int. Conf. Emerg. Technol., 2021, pp. 15.

    • Search Google Scholar
    • Export Citation
  • [71]

    L. Ma, X. Guo, and L. Wei, “An improved islanding detection algorithm based on AFDPF,” Int. Workshop Adv. Energy Sci. Environ. Eng., pp. 20492054, 2021.

    • Search Google Scholar
    • Export Citation
  • [72]

    P. P. Tikar, R. S. Kankale, and S. R. Paraskar, “A novel islanding detection technique for grid-connected distributed generation using KNN and SVM,” Adv. Clean. Energy Technol., pp. 819831, 2021.

    • Search Google Scholar
    • Export Citation
  • [73]

    R. Zamani, M. E. Golshan, H. H. Alhelou, and N. Hatziargyriou, “A novel synchronous DGs islanding detection method based on online dynamic features extraction,” Electr. Power Syst. Res., vol. 195, pp. 107180107191, 2021.

    • Search Google Scholar
    • Export Citation
  • [74]

    X. Xie, W. Xu, C. Huang, and X. Fan, “New islanding detection method with adaptively threshold for microgrid,” Electr. Power Syst. Res., vol. 195, pp. 107167107177, 2021.

    • Search Google Scholar
    • Export Citation
  • [75]

    A. Ezzat, B. E. Elnaghi, and A. A. Abdelsalam, “Microgrids islanding detection using Fourier transform and machine learning algorithm,” Electr. Power Syst. Res., vol. 196, pp. 107224107248, 2021.

    • Search Google Scholar
    • Export Citation
  • [76]

    M. Mohiti, S. Sabzevari, and P. Siano, “Two-stage islanding detection method via high frequency impedance and harmonic distortion evaluation in multi-DG networks,” Iran. J. Electr. Electron. Eng., vol. 17, no. 3, pp. 19451955, 2021.

    • Search Google Scholar
    • Export Citation
  • [77]

    O. A. Allan and W. G. Morsi, “A new passive islanding detection approach using wavelets and deep learning for grid-connected photovoltaic systems,” Electr. Power Syst. Res., vol. 199, pp. 107437107447, 2021.

    • Search Google Scholar
    • Export Citation
  • [78]

    R. Bakhshi-Jafarabadi, J. Sadeh, E. Rakhshani, and M. Popov, “High power quality maximum power point tracking-based islanding detection method for grid-connected photovoltaic systems,” Int. J. Electr. Power Energy Syst., vol. 131, pp. 107103107113, 2021.

    • Search Google Scholar
    • Export Citation
  • [79]

    A. Serrano-Fontova, J. A. Martinez, P. Casals-Torrens, and R. Bosch, “A robust islanding detection method with zero-non-detection zone for distribution systems with DG,” Int. J. Electr. Power Energy Syst., vol. 133, pp. 107247107262, 2021.

    • Search Google Scholar
    • Export Citation
  • [80]

    H. Khosravi, H. Samet, and M. Tajdinian, “Robust islanding detection in microgrids employing rate of change of kinetic energy over reactive power,” IEEE Trans. Smart Grid, vol. 13, no. 1, pp. 505515, 2021.

    • Search Google Scholar
    • Export Citation
  • [81]

    S. V. Kulkarni, D. N. Gaonkar, and J. M. Guerrero, “Operation of the microgrid with improved droop control strategy and an effective islanding detection technique for automatic mode switching,” Electr. Power Compon. Syst., vol. 49, nos 4-5, pp. 517531, 2021.

    • Search Google Scholar
    • Export Citation
  • [82]

    A. Kumar, R. K. Panda, A. Mohapatra, S. N. Singh, and S. C. Srivastava, “Mode of oscillation based islanding detection of inverter interfaced DG using ESPRIT,” Electr. Power Syst. Res., vol. 200, pp. 107479107487, 2021.

    • Search Google Scholar
    • Export Citation
  • [83]

    R. Nale, M. Biswal, and N. Kishor, “A passive communication based islanding detection technique for AC microgrid,” Int. J. Electr. Power Energy Syst., vol. 137, pp. 107657107668, 2021.

    • Search Google Scholar
    • Export Citation
  • [84]

    A. K. Özcanlı and M. Baysal, “A novel Multi-LSTM based deep learning method for islanding detection in the microgrid,” Electr. Power Syst. Res., vol. 202, pp. 107574107585, 2022.

    • Search Google Scholar
    • Export Citation
  • [1]

    M. Sadoughi, M. Hojjat, and M. Hosseini Abardeh, “Smart overcurrent relay for operating in islanded and grid-connected modes of a micro-grid without needing communication systems,” Energy Syst, vol. 30, pp. 121, 2020.

    • Search Google Scholar
    • Export Citation
  • [2]

    M. Hosseinzadeh and F. Rajaei Salmasi, “Islanding fault detection in microgrids—A survey,” Energies, vol. 13, no. 13, pp. 128, 2020.

    • Search Google Scholar
    • Export Citation
  • [3]

    B. Patnaik, M. Mishra, R. C. Bansal, and R. K. Jena, “AC microgrid protection–a review: Current and future prospective,” Appl. Energy, vol. 271, pp. 115210115237, 2020.

    • Search Google Scholar
    • Export Citation
  • [4]

    S. Choudhury, “A comprehensive review on issues, investigations, control and protection trends, technical challenges and future directions for Microgrid technology,” Int. Trans. Electr. Energy Syst., vol. 30, no. 9, pp. 1244612461, 2020.

    • Search Google Scholar
    • Export Citation
  • [5]

    M. Sadoughi, M. Hojjat, and M. Hosseini Abardeh, “An intelligent adaptive overcurrent protection system for an automated microgrid in islanded and grid-connected operation modes,” Int. J. Nonlinear Anal. Appl., vol. 11, no. 1, pp. 381394, 2020.

    • Search Google Scholar
    • Export Citation
  • [6]

    A. Damanjani, M. H. Abardeh, A. Azarfar, and M. Hojjat, “A statistics-based review of microgrid protection with a concentration on adaptive protection,” Int. Rev. Appl. Sci. Eng., vol. 12, no. 3, pp. 312323, 2021.

    • Search Google Scholar
    • Export Citation
  • [7]

    J. R. Lim, H. M. Hwang, W. G. Shin, H. J. Song, Y. C. Ju, Y. S. Jung, G. H. Kang, and S. W. Ko, “Case studies for non-detection of islanding by grid-connected in-parallel photovoltaic and electrical energy storage systems inverters,” Appl. Sci., vol. 9, no. 5, pp. 817827, 2019.

    • Search Google Scholar
    • Export Citation
  • [8]

    R. Bakhshi-Jafarabadi and J. Sadeh, “New voltage feedback-based islanding detection method for grid-connected photovoltaic systems of microgrid with zero non-detection zone,” IET Renew. Power Gener., vol. 14, no. 10, pp. 17101719, 2020.

    • Search Google Scholar
    • Export Citation
  • [9]

    H. Samet, F. Hashemi, and T. Ghanbari, “Islanding detection method for inverter-based distributed generation with negligible non-detection zone using energy of rate of change of voltage phase angle,” IET Gener. Transm. Distrib., vol. 9, no. 15, pp. 23372350, 2015.

    • Search Google Scholar
    • Export Citation
  • [10]

    A. A. Abdelsalam, A. A. Salem, E. S. Oda, and A. A. Eldesouky, “Islanding detection of microgrid incorporating inverter based DGs using long short-term memory network,” IEEE Access, vol. 8, pp. 106471106486, 2020.

    • Search Google Scholar
    • Export Citation
  • [11]

    J. S. Kim, C. H. Kim, Y. S. Oh, G. J. Cho, and J. S. Song, “An islanding detection method for multi-RES systems using the graph search method,” IEEE Trans. Sustain. Energy, vol. 11, no. 4, pp. 27222731, 2020.

    • Search Google Scholar
    • Export Citation
  • [12]

    E. J. Estébanez, V. M. Moreno, A. Pigazo, M. Liserre, and A. Dell'Aquila, “Performance evaluation of active islanding-detection algorithms in distributed-generation photovoltaic systems: two inverters case,” IEEE Trans. Industr. Electron., vol. 58, no. 4, pp. 11851193, 2010.

    • Search Google Scholar
    • Export Citation
  • [13]

    H. H. Zeineldin and S. Kennedy, “Sandia frequency-shift parameter selection to eliminate nondetection zones,” IEEE Trans. Power Deliv., vol. 24, no. 1, pp. 486487, 2008.

    • Search Google Scholar
    • Export Citation
  • [14]

    M. E. Ropp, M. Begovic, and A. Rohatgi, “Analysis and performance assessment of the active frequency drift method of islanding prevention,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 810816, 1999.

    • Search Google Scholar
    • Export Citation
  • [15]

    X. Xie, C. Huang, and D. Li, “A new passive islanding detection approach considering the dynamic behavior of load in microgrid,” Int. J. Electr. Power Energy Syst., vol. 117, pp. 105619105629, 2020.

    • Search Google Scholar
    • Export Citation
  • [16]

    Y. Jung, J. Choi, B. Yu, J. So, and G. Yu, “A novel active frequency drift method of islanding prevention for the grid-connected photovoltaic inverter,” in 36th Power Electron. Specialists Conf., 2005, pp. 19151921.

    • Search Google Scholar
    • Export Citation
  • [17]

    A. Yafaoui, B. Wu, and S. Kouro, “Improved active frequency drift anti-islanding detection method for grid connected photovoltaic systems,” IEEE Trans. Power Electron., vol. 27, no. 5, pp. 23672375, 2011.

    • Search Google Scholar
    • Export Citation
  • [18]

    F. J. Lin, Y. S. Huang, K. H. Tan, J. H. Chiu, and Y. R. Chang, “Active islanding detection method using d–axis disturbance signal injection with intelligent control,” IET Gener. Transm. Distrib., vol. 7, no. 5, pp. 537550, 2013.

    • Search Google Scholar
    • Export Citation
  • [19]

    A. Samui and S. R. Samantaray, “An active islanding detection scheme for inverter–based DG with frequency dependent ZIP–Exponential static load model,” Int. J. Electr. Power Energy Syst., vol. 78, pp. 4150, 2016.

    • Search Google Scholar
    • Export Citation
  • [20]

    B. Bahrani, H. Karimi, and R. Iravani, “Nondetection zone assessment of an active islanding detection method and its experimental evaluation,” IEEE Trans. Power Deliv., vol. 26, no. 2, pp. 517525, 2009.

    • Search Google Scholar
    • Export Citation
  • [21]

    P. K. Ganivada and P. Jena, “Frequency disturbance triggered d-axis current injection scheme for islanding detection,” IEEE Trans. Smart Grid, vol. 11, no. 6, pp. 45874603, 2020.

    • Search Google Scholar
    • Export Citation
  • [22]

    G. K. Hung, C. C. Chang, and C. L. Chen, “Automatic phase-shift method for islanding detection of grid-connected photovoltaic inverters,” IEEE Trans. Energy Convers., vol. 18, no. 1, pp. 169173, 2003.

    • Search Google Scholar
    • Export Citation
  • [23]

    H. L. Jou, W. J. Chiang, and J. C. Wu, “Virtual inductor-based islanding detection method for grid-connected power inverter of distributed power generation system,” IET Renew. Power Gener., vol. 1, no. 3, pp. 175181, 2007.

    • Search Google Scholar
    • Export Citation
  • [24]

    W. J. Chiang, H. L. Jou, and J. C. Wu, “Active islanding detection method for inverter-based distribution generation power system,” Int. J. Electr. Power Energy Syst., vol. 42, no. 1, pp. 158166, 2012.

    • Search Google Scholar
    • Export Citation
  • [25]

    F. Liu, Y. Kang, Y. Zhang, S. Duan, and X. Lin, “Improved SMS islanding detection method for grid-connected converters,” IET Renew. Power Gener., vol. 4, no. 1, pp. 3642, 2010.

    • Search Google Scholar
    • Export Citation
  • [26]

    M. Sadoughi, M. Hojjat, and M. Hosseini Abardeh, “Detection of islanding, operation and reconnection of microgrids to utility grid using local information,” Int. Trans. Electr. Energy Syst., vol. 30, no. 8, pp. 1247212491, 2020.

    • Search Google Scholar
    • Export Citation
  • [27]

    J. C. Vieira, W. Freitas, W. Xu, and A. Morelato, “Performance of frequency relays for distributed generation protection,” IEEE Trans. Power Deliv., vol. 21, no. 3, pp. 11201127, 2006.

    • Search Google Scholar
    • Export Citation
  • [28]

    J. C. Vieira, D. S. Correa, W. Freitas, and W. Xu, “Performance curves of voltage relays for islanding detection of distributed generators,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 16601662, 2005.

    • Search Google Scholar
    • Export Citation
  • [29]

    D. Bejmert and T. S. Sidhu, “Investigation into islanding detection with capacitor insertion-based method,” IEEE Trans. Power Deliv., vol. 29, no. 6, pp. 24852492, 2014.

    • Search Google Scholar
    • Export Citation
  • [30]

    F. Hashemi, M. Mohammadi, and A. Kargarian, “Islanding detection method for microgrid based on extracted features from differential transient rate of change of frequency,” IET Gener. Transm. Distrib., vol. 11, no. 4, pp. 891904, 2017.

    • Search Google Scholar
    • Export Citation
  • [31]

    S. Nikolovski, H. R. Baghaee, and D. Mlakić, “Islanding detection of synchronous generator-based DGs using rate of change of reactive power,” IEEE Syst. J., vol. 13, no. 4, pp. 43444354, 2019.

    • Search Google Scholar
    • Export Citation
  • [32]

    J. Merino, P. Mendoza-Araya, G. Venkataramanan, and M. Baysal, “Islanding detection in microgrids using harmonic signatures,” IEEE Trans. Power Deliv., vol. 30, no. 5, pp. 21022109, 2014.

    • Search Google Scholar
    • Export Citation
  • [33]

    S. R. Mohanty, N. Kishor, P. K. Ray, and J. P. Catalo, “Comparative study of advanced signal processing techniques for islanding detection in a hybrid distributed generation system,” IEEE Trans. Sustain. Energy, vol. 6, no. 1, pp. 122131, 2014.

    • Search Google Scholar
    • Export Citation
  • [34]

    A. M. Niaki and S. Afsharnia, “A new passive islanding detection method and its performance evaluation for multi-DG systems,” Electric Power Syst. Res., vol. 110, pp. 180187, 2014.

    • Search Google Scholar
    • Export Citation
  • [35]

    A. Khamis, Y. Xu, Z. Y. Dong, and R. Zhang, “Faster detection of microgrid islanding events using an adaptive ensemble classifier,” IEEE Trans. Smart Grid, vol. 9, no. 3, pp. 18891899, 2016.

    • Search Google Scholar
    • Export Citation
  • [36]

    K. Sareen, B. R. Bhalja, and R. P. Maheshwari, “Universal islanding detection technique based on rate of change of sequence components of currents for distributed generations,” IET Renew. Power Gener., vol. 10, no. 2, pp. 228237, 2016.

    • Search Google Scholar
    • Export Citation
  • [37]

    P. O'kane and B. Fox, “Loss of mains detection for embedded generation by system impedance monitoring,” 6th Int. Conf. Dev. Power Syst. Prot., 1997, pp. 9598.

    • Search Google Scholar
    • Export Citation
  • [38]

    S. I. Jang and K. H. Kim, “An islanding detection method for distributed generations using voltage unbalance and total harmonic distortion of current,” IEEE Trans. Power Deliv., vol. 19, no. 2, pp. 745752, 2004.

    • Search Google Scholar
    • Export Citation
  • [39]

    J. A. Laghari, H. Mokhlis, M. Karimi, A. H. Bakar, and H. Mohamad, “An islanding detection strategy for distribution network connected with hybrid DG resources,” Renew. Sustain. Energy Rev., vol. 45, pp. 662676, 2015.

    • Search Google Scholar
    • Export Citation
  • [40]

    S. J. Huang and F. S. Pai, “A new approach to islanding detection of dispersed generators with self-commutated static power converters,” IEEE Trans. Power Deliv., vol. 15, no. 2, pp. 500507, 2000.

    • Search Google Scholar
    • Export Citation
  • [41]

    F. S. Pai and S. J. Huang, “A detection algorithm for islanding-prevention of dispersed consumer-owned storage and generating units,” IEEE Trans. Energy Convers., vol. 16, no. 4, pp. 346351, 2001.

    • Search Google Scholar
    • Export Citation
  • [42]

    X. Chen, Y. Li, and P. Crossley, “A novel hybrid islanding detection method for grid-connected microgrids with multiple inverter-based distributed generators based on adaptive reactive power disturbance and passive criteria,” IEEE Trans. Power Electron., vol. 34, no. 9, pp. 93429356, 2018.

    • Search Google Scholar
    • Export Citation
  • [43]

    C. N. Papadimitriou, V. A. Kleftakis, and N. D. Hatziargyriou, “A novel method for islanding detection in DC networks,” IEEE Trans. Sustain. Energy, vol. 8, no. 1, pp. 441448, 2016.

    • Search Google Scholar
    • Export Citation
  • [44]

    N. W. Lidula and A. D. Rajapakse, “A pattern recognition approach for detecting power islands using transient signals-Part I: design and implementation,” IEEE Trans. Power Deliv., vol. 25, no. 4, pp. 30703077, 2010.

    • Search Google Scholar
    • Export Citation
  • [45]

    M. E. Ropp, M. Begovic, A. Rohatgi, G. A. Kern, R. H. Bonn, and S. Gonzalez, “Determining the relative effectiveness of islanding detection methods using phase criteria and nondetection zones,” IEEE Trans. Energy Convers., vol. 15, no. 3, pp. 290296, 2000.

    • Search Google Scholar
    • Export Citation
  • [46]

    Q. Sun, J. M. Guerrero, T. Jing, J. C. Vasquez, and R. Yang, “An islanding detection method by using frequency positive feedback based on FLL for single-phase microgrid,” IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 18211830, 2015.

    • Search Google Scholar
    • Export Citation
  • [47]

    S. K. Salman, D. J. King, and G. Weller, “New loss of mains detection algorithm for embedded generation using rate of change of voltage and changes in power factors,” 7th Int. Conf. Dev. Power Syst. Prot., 2001, pp. 8285.

    • Search Google Scholar
    • Export Citation
  • [48]

    R. Bekhradian, M. Davarpanah, and M. Sanaye-Pasand, “Novel approach for secure islanding detection in synchronous generator based microgrids,” IEEE Trans. Power Deliv., vol. 34, no. 2, pp. 457466, 2018.

    • Search Google Scholar
    • Export Citation
  • [49]

    Z. Ye, A. Kolwalkar, Y. Zhang, P. Du, and R. Walling, “Evaluation of anti-islanding schemes based on nondetection zone concept,” IEEE Trans. Power Electron., vol. 19, no. 5, pp. 11711176, 2004.

    • Search Google Scholar
    • Export Citation
  • [50]

    S. C. Paiva, R. L. de Araujo Ribeiro, D. K. Alves, F. B. Costa, and T. D. Rocha, “A wavelet-based hybrid islanding detection system applied for distributed generators interconnected to AC microgrids,” Int. J. Electr. Power Energy Syst., vol. 121, pp. 106032106041, 2020.

    • Search Google Scholar
    • Export Citation
  • [51]

    G. Wang, J. Wang, Z. Zhou, Q. Wang, Q. Wu, X. Jiang, and E. Santana, “State variable technique islanding detection using time-frequency energy analysis for DFIG wind turbine in microgrid system,” ISA Trans., vol. 80, pp. 360370, 2018.

    • Search Google Scholar
    • Export Citation
  • [52]

    M. Seyedi, S. A. Taher, B. Ganji, and J. M. Guerrero, “A hybrid islanding detection technique for inverter-based distributed generator units,” Int. Trans. Electr. Energy Syst., vol. 29, no. 11, pp. 1211312133, 2019.

    • Search Google Scholar
    • Export Citation
  • [53]

    G. S. Seo, K. C. Lee, and B. H. Cho, “A new DC anti-islanding technique of electrolytic capacitor-less photovoltaic interface in DC distribution systems,” IEEE Trans. Power Electron., vol. 28, no. 4, pp. 16321641, 2012.

    • Search Google Scholar
    • Export Citation
  • [54]

    A. Rostami, A. Jalilian, S. Zabihi, J. Olamaei, and E. Pouresmaeil, “Islanding detection of distributed generation based on parallel inductive impedance switching,” IEEE Syst. J., vol. 14, no. 1, pp. 813823, 2019.

    • Search Google Scholar
    • Export Citation
  • [55]

    G. Hernandez-Gonzalez and R. Iravani, “Current injection for active islanding detection of electronically-interfaced distributed resources,” IEEE Trans. Power Deliv., vol. 21, no. 3, pp. 16981705, 2006.

    • Search Google Scholar
    • Export Citation
  • [56]

    V. Menon and M. H. Nehrir, “A hybrid islanding detection technique using voltage unbalance and frequency set point,” IEEE Trans. Power Syst., vol. 22, no. 1, pp. 442448, 2007.

    • Search Google Scholar
    • Export Citation
  • [57]

    D. Reigosa, F. Briz, C. B. Charro, P. García, and J. M. Guerrero, “Active islanding detection using high-frequency signal injection,” IEEE Trans. Ind. Appl., vol. 48, no. 5, pp. 15881597, 2012.

    • Search Google Scholar
    • Export Citation
  • [58]

    A. Samui and S. R. Samantaray, “Assessment of ROCPAD relay for islanding detection in distributed generation,” IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 391398, 2011.

    • Search Google Scholar
    • Export Citation
  • [59]

    A. Samui and S. R. Samantaray, “Wavelet singular entropy-based islanding detection in distributed generation,” IEEE Trans. Power Deliv., vol. 28, no. 1, pp. 411418, 2012.

    • Search Google Scholar
    • Export Citation
  • [60]

    Y. A. Elshrief, D. H. Helmi, S. Abd-Elhaleem, B. A. Abozalam, and A. D. Asham, “Fast and accurate islanding detection technique for microgrid connected to photovoltaic system,” J. Radiat. Res. Appl. Sci., vol. 14, no. 1, pp. 210221, 2021.

    • Search Google Scholar
    • Export Citation
  • [61]

    M. Karimi, M. Farshad, Q. Hong, H. Laaksonen, and K. Kauhaniemi, “An islanding detection technique for inverter-based distributed generation in microgrids,” Energies, vol. 14, no. 1, pp. 130147, 2021.

    • Search Google Scholar
    • Export Citation
  • [62]

    S. Barczentewicz, T. Lerch, A. Bień, and K. Duda, “Laboratory evaluation of a phasor-based islanding detection method,” Energies, vol. 14, no. 7, pp. 19531969, 2021.

    • Search Google Scholar
    • Export Citation
  • [63]

    R. Bakhshi-Jafarabadi and M. Popov, “Hybrid islanding detection method of photovoltaic-based microgrid using reference current disturbance,” Energies, vol. 14, no. 5, pp. 13901405, 2021.

    • Search Google Scholar
    • Export Citation
  • [64]

    E. N. Prasad and P. K. Dash, “Islanding detection in different configurations of multiple photovoltaic distributed generation‐based direct current microgrid using improved mode decomposition technique,” Int. Trans. Electr. Energy Syst., vol. 31, no. 3, pp. 1279612815, 2021.

    • Search Google Scholar
    • Export Citation
  • [65]

    N. V. Eluri, P. K. Dash, and S. Dhar, “Islanding detection in photovoltaic based DC micro grid using adaptive variational mode decomposition and detrended fluctuation analysis,” IET Gener. Transm. Distrib., vol. 15, no. 4, pp. 631644, 2021.

    • Search Google Scholar
    • Export Citation
  • [66]

    M. Seyedi, S. A. Taher, B. Ganji, and J. Guerrero, “A hybrid islanding detection method based on the rates of changes in voltage and active power for the multi-inverter systems,” IEEE Trans. Smart Grid, vol. 12, no. 4, pp. 28002811, 2021.

    • Search Google Scholar
    • Export Citation
  • [67]

    Q. Huang, H. Chen, X. Xiang, C. Li, W. Li, and X. He, “Islanding detection with positive feedback of selected frequency for DC microgrid systems,” IEEE Trans. Power Electron., vol. 36, no. 10, pp. 1180011817, 2021.

    • Search Google Scholar
    • Export Citation
  • [68]

    A. Shukla, S. Dutta, and P. K. Sadhu, “An island detection approach by μ-PMU with reduced chances of cyber attack,” Int. J. Electr. Power Energy Syst., vol. 126, pp. 106599106611, 2021.

    • Search Google Scholar
    • Export Citation
  • [69]

    A. Damanjani, M. Hosseini Abardeh, A. Azarfar, and M. Hojjat, “A novel scheme for island detection in microgrids based on fuzzy c-means clustering technique,” Int. Rev. Appl. Sci. Eng., vol. 12, no. 2, pp. 157165, 2021.

    • Search Google Scholar
    • Export Citation
  • [70]

    S. K. Singh, M. Rawal, M. S. Rawat, and T. N. Gupta, “Hybrid islanding detection technique for inverter based microgrid,” in 2nd Int. Conf. Emerg. Technol., 2021, pp. 15.

    • Search Google Scholar
    • Export Citation
  • [71]

    L. Ma, X. Guo, and L. Wei, “An improved islanding detection algorithm based on AFDPF,” Int. Workshop Adv. Energy Sci. Environ. Eng., pp. 20492054, 2021.

    • Search Google Scholar
    • Export Citation
  • [72]

    P. P. Tikar, R. S. Kankale, and S. R. Paraskar, “A novel islanding detection technique for grid-connected distributed generation using KNN and SVM,” Adv. Clean. Energy Technol., pp. 819831, 2021.

    • Search Google Scholar
    • Export Citation
  • [73]

    R. Zamani, M. E. Golshan, H. H. Alhelou, and N. Hatziargyriou, “A novel synchronous DGs islanding detection method based on online dynamic features extraction,” Electr. Power Syst. Res., vol. 195, pp. 107180107191, 2021.

    • Search Google Scholar
    • Export Citation
  • [74]

    X. Xie, W. Xu, C. Huang, and X. Fan, “New islanding detection method with adaptively threshold for microgrid,” Electr. Power Syst. Res., vol. 195, pp. 107167107177, 2021.

    • Search Google Scholar
    • Export Citation
  • [75]

    A. Ezzat, B. E. Elnaghi, and A. A. Abdelsalam, “Microgrids islanding detection using Fourier transform and machine learning algorithm,” Electr. Power Syst. Res., vol. 196, pp. 107224107248, 2021.

    • Search Google Scholar
    • Export Citation
  • [76]

    M. Mohiti, S. Sabzevari, and P. Siano, “Two-stage islanding detection method via high frequency impedance and harmonic distortion evaluation in multi-DG networks,” Iran. J. Electr. Electron. Eng., vol. 17, no. 3, pp. 19451955, 2021.

    • Search Google Scholar
    • Export Citation
  • [77]

    O. A. Allan and W. G. Morsi, “A new passive islanding detection approach using wavelets and deep learning for grid-connected photovoltaic systems,” Electr. Power Syst. Res., vol. 199, pp. 107437107447, 2021.

    • Search Google Scholar
    • Export Citation
  • [78]

    R. Bakhshi-Jafarabadi, J. Sadeh, E. Rakhshani, and M. Popov, “High power quality maximum power point tracking-based islanding detection method for grid-connected photovoltaic systems,” Int. J. Electr. Power Energy Syst., vol. 131, pp. 107103107113, 2021.

    • Search Google Scholar
    • Export Citation
  • [79]

    A. Serrano-Fontova, J. A. Martinez, P. Casals-Torrens, and R. Bosch, “A robust islanding detection method with zero-non-detection zone for distribution systems with DG,” Int. J. Electr. Power Energy Syst., vol. 133, pp. 107247107262, 2021.

    • Search Google Scholar
    • Export Citation
  • [80]

    H. Khosravi, H. Samet, and M. Tajdinian, “Robust islanding detection in microgrids employing rate of change of kinetic energy over reactive power,” IEEE Trans. Smart Grid, vol. 13, no. 1, pp. 505515, 2021.

    • Search Google Scholar
    • Export Citation
  • [81]

    S. V. Kulkarni, D. N. Gaonkar, and J. M. Guerrero, “Operation of the microgrid with improved droop control strategy and an effective islanding detection technique for automatic mode switching,” Electr. Power Compon. Syst., vol. 49, nos 4-5, pp. 517531, 2021.

    • Search Google Scholar
    • Export Citation
  • [82]

    A. Kumar, R. K. Panda, A. Mohapatra, S. N. Singh, and S. C. Srivastava, “Mode of oscillation based islanding detection of inverter interfaced DG using ESPRIT,” Electr. Power Syst. Res., vol. 200, pp. 107479107487, 2021.

    • Search Google Scholar
    • Export Citation
  • [83]

    R. Nale, M. Biswal, and N. Kishor, “A passive communication based islanding detection technique for AC microgrid,” Int. J. Electr. Power Energy Syst., vol. 137, pp. 107657107668, 2021.

    • Search Google Scholar
    • Export Citation
  • [84]

    A. K. Özcanlı and M. Baysal, “A novel Multi-LSTM based deep learning method for islanding detection in the microgrid,” Electr. Power Syst. Res., vol. 202, pp. 107574107585, 2022.

    • Search Google Scholar
    • Export Citation
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Editor-in-Chief: Ákos, Lakatos University of Debrecen (Hungary)

Founder, former Editor-in-Chief (2011-2020): Ferenc Kalmár University of Debrecen (Hungary)

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Associate Editor: Derek Clements Croome University of Reading (UK)

Associate Editor: Dezső Beke University of Debrecen (Hungary)

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  • Mohammad Nazir AHMAD Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

    Murat BAKIROV Center for Materials and Lifetime Management Ltd. Moscow Russia

    Nicolae BALC Technical University of Cluj-Napoca Cluj-Napoca Romania

    Umberto BERARDI Ryerson University Toronto Canada

    Ildikó BODNÁR University of Debrecen Debrecen Hungary

    Sándor BODZÁS University of Debrecen Debrecen Hungary

    Fatih Mehmet BOTSALI Selçuk University Konya Turkey

    Samuel BRUNNER Empa Swiss Federal Laboratories for Materials Science and Technology

    István BUDAI University of Debrecen Debrecen Hungary

    Constantin BUNGAU University of Oradea Oradea Romania

    Shanshan CAI Huazhong University of Science and Technology Wuhan China

    Michele De CARLI University of Padua Padua Italy

    Robert CERNY Czech Technical University in Prague Czech Republic

    György CSOMÓS University of Debrecen Debrecen Hungary

    Tamás CSOKNYAI Budapest University of Technology and Economics Budapest Hungary

    Eugen Ioan GERGELY University of Oradea Oradea Romania

    József FINTA University of Pécs Pécs Hungary

    Anna FORMICA IASI National Research Council Rome

    Alexandru GACSADI University of Oradea Oradea Romania

    Eric A. GRULKE University of Kentucky Lexington United States

    Janez GRUM University of Ljubljana Ljubljana Slovenia

    Géza HUSI University of Debrecen Debrecen Hungary

    Ghaleb A. HUSSEINI American University of Sharjah Sharjah United Arab Emirates

    Nikolay IVANOV Peter the Great St.Petersburg Polytechnic University St. Petersburg Russia

    Antal JÁRAI Eötvös Loránd University Budapest Hungary

    Gudni JÓHANNESSON The National Energy Authority of Iceland Reykjavik Iceland

    László KAJTÁR Budapest University of Technology and Economics Budapest Hungary

    Ferenc KALMÁR University of Debrecen Debrecen Hungary

    Tünde KALMÁR University of Debrecen Debrecen Hungary

    Milos KALOUSEK Brno University of Technology Brno Czech Republik

    Jan KOCI Czech Technical University in Prague Prague Czech Republic

    Vaclav KOCI Czech Technical University in Prague Prague Czech Republic

    Imra KOCSIS University of Debrecen Debrecen Hungary

    Imre KOVÁCS University of Debrecen Debrecen Hungary

    Angela Daniela LA ROSA Norwegian University of Science and Technology

    Éva LOVRA Univeqrsity of Debrecen Debrecen Hungary

    Elena LUCCHI Eurac Research, Institute for Renewable Energy Bolzano Italy

    Tamás MANKOVITS University of Debrecen Debrecen Hungary

    Igor MEDVED Slovak Technical University in Bratislava Bratislava Slovakia

    Ligia MOGA Technical University of Cluj-Napoca Cluj-Napoca Romania

    Marco MOLINARI Royal Institute of Technology Stockholm Sweden

    Henrieta MORAVCIKOVA Slovak Academy of Sciences Bratislava Slovakia

    Phalguni MUKHOPHADYAYA University of Victoria Victoria Canada

    Balázs NAGY Budapest University of Technology and Economics Budapest Hungary

    Husam S. NAJM Rutgers University New Brunswick United States

    Jozsef NYERS Subotica Tech College of Applied Sciences Subotica Serbia

    Bjarne W. OLESEN Technical University of Denmark Lyngby Denmark

    Stefan ONIGA North University of Baia Mare Baia Mare Romania

    Joaquim Norberto PIRES Universidade de Coimbra Coimbra Portugal

    László POKORÁDI Óbuda University Budapest Hungary

    Antal PUHL (1950–2023) University of Debrecen Debrecen Hungary

    Roman RABENSEIFER Slovak University of Technology in Bratislava Bratislava Slovak Republik

    Mohammad H. A. SALAH Hashemite University Zarqua Jordan

    Dietrich SCHMIDT Fraunhofer Institute for Wind Energy and Energy System Technology IWES Kassel Germany

    Lorand SZABÓ Technical University of Cluj-Napoca Cluj-Napoca Romania

    Csaba SZÁSZ Technical University of Cluj-Napoca Cluj-Napoca Romania

    Ioan SZÁVA Transylvania University of Brasov Brasov Romania

    Péter SZEMES University of Debrecen Debrecen Hungary

    Edit SZŰCS University of Debrecen Debrecen Hungary

    Radu TARCA University of Oradea Oradea Romania

    Zsolt TIBA University of Debrecen Debrecen Hungary

    László TÓTH University of Debrecen Debrecen Hungary

    László TÖRÖK University of Debrecen Debrecen Hungary

    Anton TRNIK Constantine the Philosopher University in Nitra Nitra Slovakia

    Ibrahim UZMAY Erciyes University Kayseri Turkey

    Tibor VESSELÉNYI University of Oradea Oradea Romania

    Nalinaksh S. VYAS Indian Institute of Technology Kanpur India

    Deborah WHITE The University of Adelaide Adelaide Australia

International Review of Applied Sciences and Engineering
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2022  
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0,26
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57
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36
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23%

 

2019  
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4
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0,229
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Engineering (miscellaneous) Q2
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0,866
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35
Scopus
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47
Acceptance
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21%

 

International Review of Applied Sciences and Engineering
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International Review of Applied Sciences and Engineering
Language English
Size A4
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Foundation
2010
Volumes
per Year
1
Issues
per Year
3
Founder Debreceni Egyetem
Founder's
Address
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Publisher Akadémiai Kiadó
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Address
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Publisher
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ISSN 2062-0810 (Print)
ISSN 2063-4269 (Online)

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