Anti-Islanding Scheme for Synchronous DG Units Based ... - IEEE Xplore

IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 28, NO. 4, OCTOBER 2013

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Anti-Islanding Scheme for Synchronous DG Units Based on Tufts–Kumaresan Signal Estimation Method Mohsen Bakhshi, Reza Noroozian, Member, IEEE, and G. B. Gharehpetian, Senior Member, IEEE

Abstract—Considering safety and reliability problems of distribution networks, an exact distinction method is required to discriminate the loss of the main network from the existing parallel operation. Hence, this paper introduces a new islanding detection method for synchronous DGs. This method uses a noniterative signal estimation algorithm for the estimation of the damping factor and oscillation frequency of synchronous DG. The Tufts–Kumaresan (TK) signal estimation method is used in this study to detect the islanding conditions. Simulation results under various scenarios, such as different types of faults, load changes, and capacitor bank switching are used to evaluate the performance of the proposed method. This method has a high capability in islanding detection, even in cases with near to zero active power imbalances. To show the effectiveness of the proposed method, it is compared with the performance of ROCOF and ROCOFOP methods. Index Terms—Islanding detection, nondetection zone, synchronous distributed generation (SDG), Tufts–Kumaresan method.

I. INTRODUCTION

T

HESE DAYS, distributed generation (DG) has been broadly used in distribution systems. It can supply secure electricity to customers, be active in the electricity market, and increase the reliability and decrease environmental concerns [1], [2]. The islanding identification is an important issue for distribution networks. According to IEEE Standard 1547–2003, the islanding condition is defined as a situation in which a part of an electric power system is solely energized and separated from the rest of the system [3]. Failure to islanding detection can have several negative impacts for generators and connected loads, as follows [1], [4]. 1) The islanded grid cannot effectively control its frequency and voltage. This can result in damaged equipment. 2) It may cause safety hazards to utility workers and customers. Therefore, the islanding situation must be detected as soon as possible. Many islanding detection methods have been proposed, which can be classified into two main categories: 1) re-

Manuscript received July 30, 2012; revised December 28, 2012; accepted June 03, 2013. Date of publication July 17, 2013; date of current version September 19, 2013. Paper no. TPWRD-00798-2012. M. Bakhshi and R. Noroozian are with the Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371–38111, Iran (e-mail: [email protected]; [email protected]). G. B. Gharehpetian is with the Electrical Engineering Department, Amirkabir University of Technology, Tehran 15914, Iran (e-mail: [email protected]). Digital Object Identifier 10.1109/TPWRD.2013.2271837

mote methods, such as power-line communication [5] and 2) supervisory-control and data-acquisition [6] methods. They do not have a nondetection zone (NDZ) [2] and are more reliable than the local methods but are expensive. The local methods can be classified into two major groups: active and passive methods. According to active methods, islanding is detected based on adding a perturbation signal into the system. The perturbation signals in parallel operation have no significant effect; but in the case of the loss of the main grid, these signals are detected. Some of active methods, which have been recently introduced, include positive feedback for active and reactive power loops in governor and excitation system of synchronous DGs [7] and injection of a negative-sequence current through the interface voltage-source converters (VSCs) [8], Sandia frequency, and voltage-shift methods [9] and the harmonic amplification factor, which is based on the voltage change at the point of common coupling (PCC) [1]. The method of source impedance measurement, which has been introduced in [10], is the next active islanding detection method. This method, by repeatedly switching a known load on the supply and using changes on voltage, can estimate the source impedance. This method is suitable for very small generators with a power range of 1–10 kW. Passive methods are based on measuring local parameters of DG and comparing it with a preset value. Passive methods, which have been proposed, include over/underfrequency/voltage protections (OFP/UFP and OVP/UVP) and rate of change of frequency over time [11]–[13]. Vector surge relay is the other solution, which has been explained in [14]. Some of the passive methods use two parameters simultaneously, for example, rate of change of frequency over the active power [15] and rate of change of phase-angle difference [16]. According to [15] and [17], the technique of the rate of change of frequency over power has relatively large NDZ and it also needs to set two different thresholds. Finding the proper threshold is difficult in this method (for the detection of the islanding situation and prevention of trip signal transmission for the nonislanding situation). The islanding detection method based on harmonic distortion is one of the passive methods, which is usually used for inverter-based DGs. Therefore, regarding [17] and [18], this method has a major problem, when the system has nonlinear loads. In this condition, the adjustment of a proper threshold value is a tedious job. For islanding detection, signal estimation methods have been used in [17] and [19], recently. In [17], the ESPRIT method based on total least square (TLS) has been proposed. In [17], to achieve the target parameters, the special cost function must be

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minimized. It is clear that minimization of a cost function needs relatively more processing time. In [19], the same estimation method (ESPRIT) has been used by using a classification-based technique. This method has a major disadvantage, which is the high complexity of computations. Also, the window size of 1 s has been considered for sampling and processing stages. One of the most important signal estimation methods is the total least square technique (TLS). The TLS signal estimation technique is a recursive method, which depends on the initial value [20]. It means that in case of improper initial value selection, its results can reach a local minimum. Moreover, the TLS approach, as a recursive method, needs more calculation time than the direct approach methods, such as prony or Tufts–Kumaresan (TK) methods. Usually, in the signal estimation process, the results of prony or TK methods are used as a good initial value for the TLS method [20]. So by applying this technique in power system protection, problems are not recommended, considering their time-consuming process. Classification-based islanding detection schemes, which have been recently developed, are passive methods reviewed in [21] and [22]. The combination of different types of islanding detection techniques, which are known as hybrid methods, have been introduced in [4], [23], and [24]. In this paper, a passive based noniterative signal estimation algorithm is used for the estimation of the damping factor and oscillation frequency of DG. The used algorithm is the TK-damped sinusoidal signal estimation algorithm. In fact, the basic idea of the TK method can be attributed to the prony method. It transforms the nonlinear estimation problem into a linear one, to solve the problem, and estimate the feature of signals, even in noisy status. On the other hand, the proposed TK-based method is a direct approach and without using any recursive process, can estimate the target parameters accurately. It is necessary to note that the TLS-based methods are recursive, so they should be run more than one time, but the proposed method should be run only one time. The capability of the proposed method and its reliability is very high and it can detect the islanding conditions, even when the active power mismatch is close to zero. This paper is organized as follows: the TK estimation method is described in Section II. In Section III, the proposed method is introduced. Section IV discusses the results of simulations and in Sections V and VI, the comparison of the proposed method with two passive methods and conclusions are presented, respectively. II. TK ESTIMATION METHOD The samples of the observed data sequence , which consists of exponentially damped signals in white Gaussian noise are written, as follows:

(1) for , are complex numwhere bers and for are complex amplitudes. The will be called damping factors and are the radian frequencies of the desired signal, which will change between 0 and .

In [20], the following linear prediction equations using complex conjugate data in the backward direction, are introduced:

.. .

.. .

.. .

.. .

.. . (2)

where is the vector of the backward prediction coefficients [20] and the sign “star” indicates complex conjugate. It can be easily shown [21] that the following polynomial equation will have zeros at for : (3) whenever . If , (3) has addition-l zeros called extraneous zeros. To acquire an estimation of , (2) must be solved. In [20], instead of finding the least-square estimation, the following solution for based on the singular value decomposition (SVD) method has been proposed by TK (4) for or are the singular values where of . for and for are the eigenvectors of and , respectively. “ ” denotes the Hermitian matrix. The singular values of only have nonzero values and zero values of them produce the extraneous zeros of (3). Thus, by using (4) and calculating the roots of (3), the damping factor and radian frequency of the observed signal can be written as follows:

(5) (6) where and are the imaginary part and real part of roots and sampling time, respectively. Fig. 1 depicts an example of the TK method for an estimation of a simple signal in different noisy situations. In this example, we have 20 and 3. III. PROPOSED METHOD A. Frequency Deviations in Grid-Connected and Islanded Modes For a synchronous distributed generator (SDG), which is operating in parallel with a utility main network and feeding the local load, the following swing equation is defined [22], [23]: (7)

BAKHSHI et al.: ANTI-ISLANDING SCHEME FOR SYNCHRONOUS DG UNITS

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Fig. 1. Capability of the TK estimation method for the noisy signal.

Fig. 2. Values of synchronizing coefficient for different disturbances.

where and are generator inertia constant, synchronous speed of DG, damping coefficient, and the mechanical and electrical power of DG, respectively. The swing equation must be solved for two different modes; on-grid and off-grid modes. 1) Grid-Connected Mode of SDG: As presented in the Appendix, in this mode, (7) can be rewritten as follows:

By solving (15) with and as initial conditions, the following response for frequency deviations can be obtained:

(8) is known as the synchronizing where the term coefficient. This parameter has a very important role in the dynamic behavior of the synchronous generator. By solving (8) and considering and as initial conditions, the following responses for frequency and rotor-angle deviations can be obtained: (9) (10) where, we have (11) (12) (13) (14) Considering (9) and (10), it can be said that frequency and rotor-angle deviations have a damped sinusoidal waveform and after a while, the amplitude of these signals will be equal to the zero. 2) Loss of Main Grid: In this section, the response of the frequency to the loss of the main grid is determined. In the islanding situation, the transmitted power between DG and main network reaches zero (see Fig. 2). It means that the synchronizing coefficient must be equal to zero. Therefore, in (8), we have (15)

(16) is the active power imbalance. By comparing (10) Here, and (16), it can be seen that frequency deviations in grid-connected and islanded modes are different. When real power mismatch causes transients in the islanded portion, the frequency of the DG increases or decreases. Therefore, the aforementioned frequency deviations can be used to detect the islanding condition. B. Procedure of the Proposed Method In this section, the proposed islanding detection method will be introduced. From (10) and (16), it can be seen that the waveform of the DG frequency in both parallel operation and islanding condition has sinusoidal and exponential waveforms, respectively. For a sinusoidal waveform, both features of the damping factor and oscillation frequency with nonzero values can be extracted. On the other hand, exponential types only have a damping factor as a nonzero value. The sampling of the desired signal is the next attempt. According to the IEEE-1547 standard, the maximum allowable time to detect islanding conditions is 2 s. This time must be considered in the sampling process. Regarding the oscillation frequency of the DG’s output frequency, the sampling time can be different and changed from 300 to 800 ms for various types of SDGs. The inertia constant (i.e., ) of SDGs, determines this range. Thus, in this study, the window size of 500 ms for the sampling of the desired signal has been considered. In the TK estimation method, the size of the “ ” matrix depends on the number of samples . Therefore, to reduce the amount of computations, the is selected to 20. In addition, the is also selected to 4. In regards to the entire matrices, which are achieved in this study, the number of extraneous zeros from (3) is equal to 1. As a result, the numbers of three modes are obtained. As shown in Fig. 2, the frequency of oscillations is dependent on the synchronizing coefficient, and the synchronizing coefficient has different values in different disturbances. As a result, the oscillation frequency is changed for different disturbances.

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Fig. 3. Model of the proposed method.

Fig. 4. Single-line diagram of the studied system.

This range of variations must be determined. The aforementioned points are expressed by the following equations: (17) where and are the synchronizing coefficient, and Fig. 3, depicts the proposed method. In this figure, OFP/UFP has also been used, considering its good performance in large power mismatches, whereas in small power mismatches, the estimation-based method can exactly detect the islanding situation. and , which have been presented in Fig. 3, are introduced, as follows: (18) (19) where and are the minimum and maximum values of the oscillations frequency due to disturbances in the parallel operation. These values can be obtained by using the computer simulations for a case study system. IV. RESULTS AND DISCUSSION To verify the performance of the proposed method, the system shown in Fig. 4, is tested under different islanding and nonislanding disturbances. In this system, the 30-MVA synchronous DG is connected through one transmission line to the subtransmission system with the short-circuit level of 1500 MVA. A sixth-order model of SDG is used for simulations. More information about this system can be found in [11]. It should be noted that all simulations have been carried out in a Matlab/SimPowerSystem software environment. The TK estimation method has been coded as M-file and linked to the Simulink file in Matlab

Fig. 5. Samples of (a) islanding (b) and (c) nonislanding events.


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TABLE I OUTPUT RESULTS

OF THE PROPOSED METHOD FOR THE CONDITIONS (SCENARIO 1)

NONISLANDING

Fig. 6. Time of processing for the TK estimation method.

Fig. 7. Output results of the TK estimation method.

software through sim-function. The DG output frequency is obtained through one phase-locked loop (PLL) block, and the sampling process begins after a nonzero variation on the DG frequency. In this paper, to consider the wide range of loading conditions, the following three different scenarios have been proposed: • scenario 1: SDG power is 0.4 p.u. and (load at bus 4) is 10 MW Mvar; • scenario 2: SDG power is 0.8 p.u. and (load at bus 4) is 20 MW Mvar; • scenario 3: SDG power is 1 p.u. and (load at bus 4) is 30 MW Mvar. This study has the sampling time of 25 ms. After the sampling process, the calculation of the TK method begins. The output of the TK process is determined by the “Trip” variable. When the nonislanding disturbances occur, the variable of “Trip” is equal to zero and in the islanding condition, this variable is equal to 1. The nonislanding phenomena, which have been used in this paper, include different types of faults (with various fault resistance), capacitor bank opening and closing, and the RLC load changing (with different values). Thus, more than 60 different islanding and nonislanding disturbances in three different scenarios have been studied. The most important of them has been presented in Fig. 5. In this figure, all of disturbances have been applied at 2 s. For all faults, the duration time of six cycles is considered. The combinations of different types of disturbances have been simulated as well. This software is used on a Pentium-4 personal computer with a central processing unit of 2.81 GHz. As can be seen in Fig. 6, the required time of processing

for the TK estimation method varies between 99.26 and 111.5 ms. Regarding the time of sampling, which is equal to 500 ms, the entire time of the proposed method is equal to 611.5 ms. According to IEEE-1547 standard, the mentioned time is acceptable (lower than 2 s). It should be noted that this situation occurs, when the active power imbalances are small and the OFP/UFP does not operate. Fig. 7 and Tables I–VI depict the results of the TK estimation method. According to Fig. 3, the output of the TK method is presented by and variables. indicates the damping factor of the estimated signal for the first mode and expresses the oscillation frequency for the same case. This figure has been acquired for more than 60 disturbances, such as single-line-to-ground (SLG) faults (for different locations and different fault resistances and for different phases), double-line-to-ground (DLG) and three-phase faults (for all existing statuses with different fault resistances), capacitor bank and RLC load opening and closing (with various amplitudes for

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TABLE III

TABLE II OUTPUT RESULTS

OF THE PROPOSED METHOD FOR CONDITIONS (SCENARIO 2)

NONISLANDING

different locations in the system). In Fig. 7, the magnitude of the oscillation frequency versus the damping factor is shown. Regarding this figure, all islanding situations have a zero amount for oscillations frequency while for nonislanding disturbances, this parameter is nonzero. Therefore, all nonislanding conditions are centralized into a special zone. As a result, by using this idea, the proposed method can detect the islanding conditions accurately. V. COMPARISON

PROPOSED METHOD WITH TWO PASSIVE METHODS

OF THE

A. ROCOF Method The ROCOF relay is a simple method to detect the islanding conditions, which is considered by many utilities. Although the implementation of this method is very simple and cheaper than that of other methods, in small power mismatches, it has poor

OUTPUT RESULTS

OF THE PROPOSED METHOD FOR CONDITIONS (SCENARIO 3)

NONISLANDING

performance and cannot correctly detect the islanding condition. Thus, in this paper, the proposed method is compared with the ROCOF relay. The model of the ROCOF relay is shown in Fig. 8. According to this figure, the rate of change of frequency after crossing of a first-order filter is computed and it is compared with a preset threshold value. The first-order filter is used to reduce high-frequency transients. In Fig. 8, and are preset threshold values and the time constant, respectively. The time constant is adjusted to 0.1 s. It should be noted that before sending a trip signal, the terminal voltage of DG must be compared with a to prevent an undesired trip, which is caused by generator startup and short-circuit faults. The threshold value of the ROCOF relay is changed between 0.3 to 1.5 Hz/s. Therefore, in this study, by considering many scenarios, such as short-circuit faults, capacitor bank switching and load changing; is selected to 1.5 Hz/s. Fig. 9 depicts the output of the ROCOF relay for low active power mismatches. In this figure, it can be seen that for 9% of active power mismatches, the ROCOF


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TABLE IV OUTPUT RESULTS OF THE PROPOSED METHOD FOR ISLANDING CONDITIONS (SCENARIO 1)

TABLE VI OUTPUT RESULTS OF THE PROPOSED METHOD FOR ISLANDING CONDITIONS (SCENARIO 3)

TABLE V OUTPUT RESULTS OF THE PROPOSED METHOD FOR ISLANDING CONDITIONS (SCENARIO 2)

threshold; in this case, if the output value of signal is larger than the first threshold, an embedded counter is incremented by one. Finally, with the encroachment of counter value from the second threshold, the ROCOFOP proceeds to send a trip command. In this paper, this method is implemented in the Matlab environment on the case study system. The sample results of this method are depicted in Figs. 10 and 11. To reduce the false trip commands for nonislanding situations, the first and second thresholds are adjusted to 3 and 4, respectively. These threshold values are selected based on simulation results of different nonislanding situations. For the larger values than the adjusted values, in the case of thresholds, both parameters of detection time and NDZ criteria are increased. On the other hand, the lower values create false trips. Thus, in the paper, with these adjusted values for thresholds, the ROCOFOP method has an undesired trip for a special three-phase fault. This status has been shown in Fig. 10(b). According to this figure, when the ROCOFOP method encounters a three-phase fault, it sends a trip command. This disturbance describes voltage sag, which causes an SDG terminal voltage drop of 20%. The time for all faults is six cycles. Fig. 10 depicts the results of the ROCOFOP method in islanding conditions. It is obvious that the ROCOFOP method has relatively large NDZ. For an islanding condition with 15%, this method can detect the islanding condition within 0.283 s. For values lower than this value, the performance of the ROCOFOP is reduced but in comparison with ROCOF, it is the better choice. For example, the ROCOFOP method can detect the islanding condition with 7% within 0.717 s, while the ROCOF method with 9% and the detection time of 0.9 s for islanding conditions has relatively poor performance. Also, for 1% of active power mismatches, the ROCOFOP method can detect the islanding in more than 1s, whereas the proposed method for the same condition has the detection time of 0.6 s. It is necessary to note that for implementation of the ROCOFOP method, its output signal must be discrete. The discretization process has been carried out through one zero-order-hold (ZOH) filter. In this paper, this process with a frequency of ,

relay has poor performance and can detect the islanding conditions for a considerable time of about 1 s. For active power mismatches that are smaller than %, the ROCOF relay cannot detect the islanding conditions. This value is called the critical active power mismatches and it is usually changed between 9% to 15% in ROCOF relays. As a result, although the ROCOF relay in large power mismatches (larger than 10% of active power mismatches) has good performance and can quickly detect the islanding conditions, it has a large NDZ. B. Rate of Change of Frequency Over Power This method was introduced for the first time in [15]. Unlike the ROCOF method, this procedure is more sensitive and has lower NDZ than the ROCOF. Also, this technique has two different threshold preset values. Thus, adjusting these two parameters is a rather difficult problem. The overall procedure of rate of change of frequency over power (ROCOFOP) method is based on comparing the output signal with first

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Fig. 8. Model of the ROCOF method.

Fig. 9. Amplitude of the ROCOF relay for different active power mismatches.

Fig. 11. Output results of ROCOFOP for a few islanding situations.

VI. CONCLUSION This paper has proposed a new islanding detection method based on the TK estimation technique. For any value larger than 0.01 in frequency difference, the sampling process of the method starts to compute the online TK estimation process and, finally, finds the damping factor and oscillation frequency. These two parameters, in both islanding and nonislanding situations, have different values. Thus, this method is a powerful tool for islanding detection in the presence of SDGs. The NDZ of the proposed method is very small. This claim has been tested by simulations of the proposed method on the sample power system for more than 60 islanding and nonislanding disturbances in three different scenarios. In addition, the proposed method has been compared with ROCOF and ROCOFOP methods and it has been shown that the proposed method for active power mismatches smaller than 10% has better performance than the ROCOF and ROCOFOP methods. APPENDIX The following equation presents the swing equation [27], [28]: (A1) Fig. 10. Output results of the ROCOFOP for nonislanding situations.

which is adjusted to 60 Hz, has been accomplished. Eventually, the proposed method in comparison with two typical passive methods is a very effective method and has robust performance and good capability to detect small and very small active power mismatches.

If a part of the system encountered a disturbance and the rotor has a small variation, the following equation can be angle written:

(A2)


This equation can be rewritten, as follows:

(A3) If the rotor-angle deviation has a small variation , by substituting with and with 1, the aforementioned equation is converted to the following two simple equations: (A4) (A5) REFERENCES [1] M. Massoud, K. H. Ahmed, S. J. Finney, and B. W. Williams, “Harmonic distortion-based Island detection technique for inverter-based distributed generation,” Inst. Eng. Technol. Renew. Power Gen., vol. 3, no. 4, pp. 493–507, Dec. 2009. [2] H. Vahedi, R. Noroozian, A. Jalilvand, and G. B. Gharehpetian, “A new method for islanding detection of inverter-based distributed generation using DC-link voltage control,” IEEE Trans. Power Del., vol. 26, no. 2, pp. 1176–1186, Apr. 2011. [3] IEEE Standard for Interconnecting Distributed Resources With Electric Power Systems, IEEE Standard 1547-2003, Jul. 2003. [4] S. Jang and K. H. Kim, “An islanding detection method for distributed generations using voltage unbalance and total harmonic distortion of current,” IEEE Trans. Power Del., vol. 19, no. 2, pp. 745–752, Apr. 2004. [5] W. Wang, J. Kliber, G. ZHang, W. XU, B. Howell, and T. Palladino, “A power line signaling based scheme for anti-islanding protection of distributed generators—Part II: field test results,” IEEE Trans. Power Del., vol. 22, no. 3, pp. 1767–1772, Jul. 2007. [6] E. M. Davidson, S. D. J. Mcartur, J. R. Mcdonald, T. Cumming, and I. Watt, “Applying multi-agent system technology in practice: Automated management and analysis of SCADA and digital fault recorder data,” IEEE Trans. Power Syst., vol. 21, no. 2, pp. 559–567, May 2006. [7] P. Du, J. K. Nelson, and Z. Ye, “Active anti-islanding schemes for synchronous machine-based distributed generators,” Proc. Inst. Elect. Eng., Gen. Transm. Distrib., vol. 152, no. 5, pp. 597–606, Sep. 2005. [8] B. Bahrani, H. Karimi, and R. Iravani, “Nondetection zone assessment of an active islanding detection method and its experimental evaluation,” IEEE Trans. Power Del., vol. 26, no. 2, pp. 517–525, Apr. 2011. [9] L. Lopes and H. Sun, “Performance assessment of active frequency drifting islanding detection methods,” IEEE Trans. Energy Convers., vol. 21, no. 1, pp. 171–180, Mar. 2006. [10] P. D. Hopewell, N. Jenkins, and A. D. Cross, “Loss of mains detection for small generators,” Proc. Inst. Elect. Eng., Elect. Power Appl., vol. 143, no. 3, pp. 225–230, May 1996. [11] J. C. M. Vieira, W. Freita, W. Xu, and A. Morelato, “Efficient coordination of ROCOF and frequency relays for distributed generation protection by using the application region,” IEEE Trans. Power Del., vol. 21, no. 4, pp. 1878–1884, Oct. 2006. [12] J. C. M. Vieira, W. Freitas, W. Xu, and A. Morelato, “An investigation on the nondetection zones of synchronous distributed generation anti-islanding protection,” IEEE Trans. Power Del., vol. 23, no. 2, pp. 593–600, Apr. 2008. [13] J. C. M. Vieira, D. Salles, and W. Freitas, “Power imbalance application region method for distributed synchronous generator anti-islanding protection design and evaluation,” Int. J. Elect. Power Syst. Res., vol. 81, pp. 1952–1960, Jul. 2011. [14] W. Freitas, Zh. Huang, and W. Xu, “A practical method for assessing the effectiveness of vector surge relays for distributed generation applications,” IEEE Trans. Power Del., vol. 20, no. 1, pp. 57–63, Jan. 2005. [15] F. Sh. 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. 346–351, Dec. 2001. [16] 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. 391–398, Jun. 2011. [17] H. H. Zeineldin, T. A. Galil, E. F. E. Saadany, and M. M. A. Salam, “Islanding detection of grid connected distributed generators using TLS ESPRIT,” Int. J. Elect. Power Syst. Res., vol. 77, pp. 155–162, Apr. 2006.

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[18] M. Ropp and W. Bower, “Evaluation of islanding detection methods for photovoltaic utility interactive power systems, Tech. Rep. IEA PVPS T5-09, Mar. 2002, International Energy Agency Implementing Agreement on Photovoltaic Power Systems. [19] W. K. A. Najy, H. H. Zeineldin, A. H. K. Alaboudy, and W. L. Woon, “A bayesian passive islanding detection method for inverter-based distributed generation using ESPRIT,” IEEE Trans. Power Del., vol. 26, no. 4, pp. 2687–2696, Oct. 2011. [20] N. Kannan and D. Kundu, “Estimating parameters in the damped exponential model,” J. Signal Process., vol. 81, pp. 2343–2351, Nov. 2001. [21] Kh. E. Arroudi, G. Joós, I. Kamw, and D. T. Mc Gillis, “Intelligentbased approach to islanding detection in distributed generation,” IEEE Trans. Power Del., vol. 22, no. 2, pp. 828–835, Apr. 2007. [22] N. W. A. Lidula and A. D. Rajapakse, “A pattern recognition approach for detecting power islands using transient signals—Part I: Design and implementation,” IEEE Trans. Power Del., vol. 25, no. 4, pp. 3070–3077, Oct. 2010. [23] P. Mahat, Zh. Chen, and B. B. Jensen, “A hybrid islanding detection technique using average rate of voltage change and real power shift,” IEEE Trans. Power Del., vol. 24, no. 2, pp. 764–771, Apr. 2009. [24] W. E. Khattam, T. S. Sidhu, and R. Seethapathy, “Evaluation of two anti-islanding schemes for a radial distribution system equipped with self-excited induction generator wind turbines,” IEEE Trans. Energy Convers, vol. 25, no. 1, pp. 107–117, Mar. 2010. [25] R. Kumaresan and D. W. Tufts, “Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise,” IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-30, no. 6, pp. 833–840, Dec. 1982. [26] D. W. Tufts and R. Kumaresan, “Estimation of frequencies of multiple sinusoids; Making linear prediction perform like maximum likelihood,” Proc. IEEE, vol. 70, no. 9, pp. 975–989, Sep. 1982. [27] P. M. Anderson and A. A. Fouad, Power System Control and Stability. Ames, IA: The Iowa State University Press, 1977, pp. 13–148. [28] P. Kundur, Power System Stability and Control. New York: McGraw-Hill, 1994. Mohsen Bakhshi was born in Iran in 1987. He received the B.Sc. and M.Sc. degrees in electrical engineering from Zanjan University, Zanjan, Iran, in 2010 and 2012, respectively. His research interests include distributed generation, power electronics, power system operation, and artificial intelligence.

Reza Noroozian (M’09) was born in Iran. He received the B.Sc. degree in power systems from Tabriz University, Tabriz, Iran, in 2000, and the M.Sc. and Ph.D. degrees in electrical engineering from Amirkabir University of Technology, Tehran, Iran, in 2003 and 2008, respectively. He is an Associate Professor with the Department of Power Engineering, The University of Zanjan, Zanjan, Iran. His areas of interest include power electronics, power systems, power quality, integration and control of renewable generation units, custom power, microgrid operation, distributed-generation modeling, as well as operation and interface control. G. B. Gharehpetian (SM’08) received the B.S. degree (Hons.) in electrical engineering from Tabriz University, Tabriz, Iran, in 1987, the M.S. degree (Hons.) in electrical engineering from Amirkabir University of Technology (AUT), Tehran, Iran, in 1989, and the Ph.D. degree (Hons.) in electrical engineering from Tehran University, Tehran, Iran, in 1996. As a Ph.D. student, he has received scholarship from DAAD (German Academic Exchange Service) from 1993 to 1996 and he was with High Voltage Institute of RWTH Aachen, Aachen, Germany. He was Assistant Professor at Amirkabir University of Technology, Tehran, from 1997 to 2003, Associate Professor from 2004 to 2007, and has been Professor since 2007. He is the author of more than 640 journal and conference papers. His teaching and research interests include power system and transformers transients and power-electronics applications in power systems. Prof. Gharehpetian was selected by the ministry of higher education as the distinguished professor of Iran and by the Iranian Association of Electrical and Electronics Engineers (IAEEE) as the distinguished researcher of Iran and was awarded the National Prize in 2008 and 2010, respectively.