proposed sandia frequency shift for anti islanding detection method based on artificial immune system

Alexandria Engineering Journal (2017) xxx, xxx–xxx H O S T E D BY Alexandria University Alexandria Engineering Journal www.elsevier.com/locate/aej www.sciencedirect.com ORIGINAL ARTICLE Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system A.Y Hatata *, El-H Abd-Raboh, Bishoy E Sedhom Faculty of Engineering, Mansoura University, Egypt Received April 2015; revised 19 October 2016; accepted 15 December 2016 KEYWORDS Anti-islanding detection; Sandia frequency shift (SFS); Non-detection zone (NDZ); Total harmonic distortion (THD); Artificial immune system (AIS); Clonal selection algorithm Abstract Sandia frequency shift (SFS) is one of the active anti-islanding detection methods that depend on frequency drift to detect an islanding condition for inverter-based distributed generation The non-detection zone (NDZ) of the SFS method depends to a great extent on its parameters Improper adjusting of these parameters may result in failure of the method This paper presents a proposed artificial immune system (AIS)-based technique to obtain optimal parameters of SFS anti-islanding detection method The immune system is highly distributed, highly adaptive, and self-organizing in nature, maintains a memory of past encounters, and has the ability to continually learn about new encounters The proposed method generates less total harmonic distortion (THD) than the conventional SFS, which results in faster island detection and better non-detection zone The performance of the proposed method is derived analytically and simulated using Matlab/Simulink Two case studies are used to verify the proposed method The first case includes a photovoltaic (PV) connected to grid and the second includes a wind turbine connected to grid The deduced optimized parameter setting helps to achieve the ‘‘non-islanding inverter” as well as least potential adverse impact on power quality Ó 2016 Faculty of Engineering, Alexandria University Production and hosting by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Introduction Distributed generations (DG) are small-scale generation units that can be installed near to consumers with the ability of interacting with the grid importing or exporting energy One of the major problems associated with such generators is the unwanted islanding phenomenon Islanding occurs when a portion of the distribution system becomes electrically isolated * Corresponding author E-mail address: a_hatata@yahoo.com (A.Y Hatata) Peer review under responsibility of Faculty of Engineering, Alexandria University from the remainder of the power system yet continues to be energized by one or more DGs An important requirement to interconnect a DG to a power distributed system is the capability of the DG unit to detect islanding with the minimum time possible The continued energizing of the load can lead to damage of equipment or injury to maintenance personnel working within the islanded section without knowing the system is still alive Most DG units are designed in such a way that they are disconnecting from the grid when over/under voltage or frequency occurs on the network In the case that the grid is disconnected while the load and the source are matched, the DG units will thus continue to power the line, thereby leading to the formation of an island http://dx.doi.org/10.1016/j.aej.2016.12.020 1110-0168 Ó 2016 Faculty of Engineering, Alexandria University Production and hosting by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 DGs must detect islanding and immediately stop feeding the utility lines with power This is known as anti-islanding Anti-islanding methods assist the DG units to detect islanding or force the islanded section out of the normal operational specifications of the grid This is achieved by attempting to perturb either the voltage or the frequency of the network In the presence of the grid, these perturbations will have no effect on the voltage or frequency If the grid is disconnected, variations in voltage or frequency can occur These variations can be detected by the over/under voltage or frequency protection system, and the DG is disconnected or shutdown Anti-islanding methods generally can be classified into four major groups, which include passive methods, active methods, hybrid methods and communication base methods [1] Passive methods monitor selected parameters, such as voltage, frequency or their characteristics, and they switch off the inverter if one of these parameters deviates outside specified boundaries or conditions [2] The boundary limits of these parameters define the non-detection zone (NDZ) The passive methods include Over/Under Voltage Protection (OVP/UVP), Over/ Under Frequency Protection (OFP/UFP), voltage phase jump detection, and detection of voltage and current harmonics methods [2–4] These methods are conceptually simple and easy to implement and not introduce any change to the power quality of the system However, they have a number of weaknesses including the inability to detect islanding because they have a large NDZ They tend to false trip due to disturbances on the grid which may weaken grid stability and security In order to reduce the NDZ, particularly in cases where the local loads are close in capacity to the DG systems, active detection methods have been proposed Active methods perturb the connected circuit and then monitor the response to determine whether islanding has occurred [2,5–8] Active methods include Impedance measurement method [9–11], Slip Mode Frequency Shift (SMS) [12], Active Frequency Drift (AFD) [13–19], Sandia Frequency Shift (SFS) [13,20–22], Sandia Voltage Shift (SVS) [5,8], Reactive Power Variation (RPV) method [23], and Mains monitoring units with allocated allpole switching devices connected in series (MSD) [24] Active methods attempt to create a power mismatch between the load and the DG when they are closely matched It is possible that some of the active methods can cancel out the mismatch in an attempt to create one It should also be noted that the positive feedback in some active methods could lead to power-quality degradation [25] The injection signals can also induce some voltage waveform distortion Among frequency drift islanding detection methods, SFS is considered as one of the most effective methods in detecting islanding conditions The method can be used to improve the NDZ and THD by using a positive feedback gain, but still affect the power quality of the system This paper presents a proposed technique to modify SFS anti-islanding method using Artificial Immune Systems (AIS) The method optimizes the parameters of the SFS method to reduce both the NDZ and the THD of the current waveforms AIS are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system During the past decade, they have attracted a lot of interest from researchers aiming to develop immunebased models and techniques to solve complex computational or engineering problems Authors in [26,27] propose a A.Y Hatata et al technique that is based on AIS for fault detection Ref [28] applies AIS for disturbance detection On the other hand, AIS was used for fault detection in the stator and rotor circuits of induction machines in [29] The impacts of the load parameters on the performance of the SFS method have been discussed in many papers However these papers are concerned with reducing NDZ only and ignore the impact on power quality deterioration In [19] the performances of SFS method and its major factors affecting the phase-frequency characteristic of the islanding system were analyzed Ref [20] proposed a technique to estimate the parameter of SFS using fuzzy logic Ref [21] proposed SFS methods that prevent islanding without any deregulation in power quality Authors in [22] presented a method to improve the dynamic NDZ in load parameter space In [30] a SFS based PLL algorithm was proposed for islanding detection Authors in [31,32] discussed the impact of load frequency and DG on the parameters of SFS method There are many artificial intelligence techniques used for islanding detection [33–36] Ref [33] presented a passive method for detecting islanding of DG inspired by the information processing properties of natural immune system Ref [34] proposed an islanding detection algorithm using combination of an optimal Artificial Neural Network (ANN) based on Particle Swarm Optimization (PSO) with a simple active method Two-dimensional particle swarm optimization (2D-PSO) method for optimizing the weighting in extension theory to detect the islanding in the presence of photovoltaic (PV) power generation systems was presented in Ref [35] Authors in [36] presented an islanding detection method that is based on neuro-fuzzy system which is trained by using four different heuristic algorithms and finally among all of them, PSO with the best results is elected This paper presents a proposed AIS-based technique to obtain optimal parameters of SFS anti-islanding detection method This method improves the performance of the conventional SFS method by reducing THD, NDZ, and results in faster island detection The proposed method is derived analytically and simulated using Matlab/Simulink The verification of this method can be achieved by a two case studies; the first case includes a photovoltaic (PV) connected to grid and the second includes a wind turbine connected to grid The remainder of this paper is organized as follows: Section describes the SFS anti-islanding method, Section describes the system modeling and simulation, Section introduces the AIS method and the clonal selection algorithm, Section presents the modified SFS based AIS method and the AIS algorithm for solving this problem, and Section presents the results and discussion Finally, Section concludes the paper Sandia frequency shift anti-islanding method Sandia Frequency Shift (SFS) is one of the active islanding detection methods that rely on frequency drift to detect an islanding condition These methods depend on injecting a distorted current waveform into the original reference current of the inverter; therefore, in the case of islanding operation the frequency drift up or down depending on the sign of the so called chopping factor ‘‘Cf” A positive feedback is utilized Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 Anti-islanding detection method to prevent islanding and to decrease NDZ value The procedures of applying the SFS method can be summarized as follows [24]: Inject a current harmonic signal with a limited duration into the Point of Common Coupling (PCC) so as to comply with the maximum THDi allowed by interconnection standards The injected current signal distorts the inverter current by presenting a A segment for drift up operation as shown in Fig The desirable effect of the A segment, is that the fundamental component of the inverter current leads the voltage by a small angle hAFD , which is frequency dependent and it creates a positive feedback When the grid is disconnected, the frequency of the voltage of the PCC tends to drift, reaching values higher until the frequency is out of the OFP/UFP trip window (Range) and the inverter is disconnected A positive feedback is utilized to prevent islanding The NDZ of the SFS highly depends on its design parameters The design parameters include both chopping factor Cf and feedback gain factor K If these parameters are not properly tuned, it may result in failure of the method or deterioration of the system power quality through injection of high amount of harmonics However, SFS may fail to detect islanding considering a fact that the deviations of voltage and frequency are small due to the power balance between DG sources and local loads The chopping factor is a function of the error in the line frequency and may be computed as follows: Cf ẳ Cfo ỵ Kfa fline Þ ð1Þ where Cfo is the chopping factor when there is no frequency error, K is an accelerating gain that does not change direction, fa is the line frequency measured at PCC, and fline, is the nominal line frequency Figure AFD current reference of PV inverter System modeling and simulation To verify the proposed method and prove its effectiveness, it is applied to a test system shown in Fig The system consists of a DG, connected to the grid PCC through a current controlled VSI inverter to supply a local ac load The load can also be supplied by the grid through a power transformer The local load is represented by a parallel RLC with variable resonant frequency and quality factor The utility breaker connects the grid to the PCC in case of normal operation For islanding condition the breaker is opened at a prescribed time The test system is modeled and simulated in MATLAB/Simulink environment as shown in Fig The simulation parameters are presented in Table The simulation is implemented in the following steps:  The frequency is firstly measured If the frequency exceeds the IEEE Std 929–2000 limits, then the OFP/UFP will generate a fault signal to shut down the inverter  In case that load and DG output are closely mismatched, then the frequency of the network is perturbed to create a power mismatch between load and DG  A variations in frequency can be detected by OFP/UFP system and consequently the DG is disconnected or shutdown  Since this perturbation signal may induce current and voltage waveform distortion, the load current is checked to ensure that the THD limits are not violated Artificial immune system (AIS) The immune system of vertebrates including human is composed of cells, molecules and organs in the body which protect the body against infectious diseases caused by foreign pathogens such as viruses and bacteria [37] To perform these functions, the immune system has to be able to distinguish between the body’s own cells such as self-cells and foreign pathogens such as non-self-cells or antigens After distinguishing between self and non-self-cells, the immune system has to perform an immune response in order to eliminate non-self-cell or antigen [38–40] Clonal selection principle of AIS describes how the immune cells capture a foreign antigen This algorithm is simple and efficient for achieving optimum solution [41] The algorithm steps involving clonal selection are as follows: Figure Single line diagram of the test system Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 A.Y Hatata et al Figure Table Test system modeled in Matlab Simulink Simulation parameters Grid voltage (rms) Grid frequency 400 V 50 Hz LC filter L = 18 mH C = 30 lF DG system output power Pinv = kW Qinv = Var Parallel RLC load R = 120 O L = 153 mH C = 67 lF Start with a number of antibodies (immune cells) which represent initial population size When an antigen or pathogen invades the organism, a number of antibodies which identify these pathogens survive In Fig only the antibody C is able to identify the pathogen as its shape fits to a portion of the antigen So fitness of antibody C is greater than others Figure The immune cells that identify the antigens undergo cellular reproduction After reproducing the somatic cells reproduction asexual form, i.e there are no crossovers of the genetic material during the cell mitosis The genetic new cells are copies (clones) of their parents as shown in antibody C in Fig 4 A portion of cloned cells endures a mutation mechanism which is called somatic hyper mutation The affinity of any cell with each other is measured by the difference in similarity between them The affinity is determined by measuring the difference between two cells The response of antibodies found in memory has an average affinity higher than the early first response This phenomenon refers to the maturation of the immune response After the mutation process the fitness as well as the affinity of the antibody to each other is changed For each iteration, among the efficient immune cells, some become effecter cells (Plasma Cell) and the other cells become memory cells The effecter cells secrete antibodies and memory cells have a long life span so as to act faster Basic of clonal selection algorithm Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 Anti-islanding detection method and effective in the future when the organism is exposed to the same antigen This process is continued till the termination condition is satisfied; else the steps from to are repeated Clonal selection theory explains how the immune system fights against an antigen It establishes the idea that only those cells that recognize the antigen, are selected to proliferate The selected cells are subjected to an affinity maturation process which improves their affinity to the selected antigens [37,41] where fomax is the maximum value of load resonant frequency that will result in islanding operation, and fomin is the minimum value of load resonant frequency that will result in islanding operation, and they can be expressed as [42]: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi f  fomax ¼ max À tan hSFS fmax ị ỵ tan2 hSFS fmax ị ỵ 4Q2f 3ị 2Qf fomin ẳ q fmin  tan hSFS fmin ị ỵ tan2 hSFS fmin ị ỵ 4Q2f 2Qf ð4Þ hSFS ðfÞ is the phase angle of the inverter and can be computed as follows: Proposed SFS–based AIS method The two parameters K and Cfo have different effects on the NDZ of SFS schemes Initial chopping factor Cfo changes the location of NDZ on load space, but it doesn’t change the size of NDZ Increasing Cfo causes adverse impact on power quality, as it perturbs the network with a distorted current The THD of the output current is directly proportional to Cfo [13] According to the impact on power quality; smaller values are preferred in Cfo setting Positive feedback gain K has a positive impact on NDZ consideration Increasing K will decrease NDZ and push the NDZ toward load space with higher Cfo , but it may also produce negative impact on power quality of the distribution system Therefore smaller values of K parameter are preferred for efficient detection of islanding To minimize both NDZ and THD to acceptable limit, it is necessary to optimize the values of Cfo and K In this paper AIS optimization technique is used to obtain optimal values of Cfo and K It is clear that selecting the parameters of islanding detection system is not a straightforward task This paper presents a method for optimizing the selection of these parameters so that the best possible fast islanding detection is achieved at the lowest cost of adverse effects on power quality The proposed method formulates SFS parameter selection as an optimization problem with main objective of minimizing NDZ while satisfying THD limit as a constraint The optimization method used is AIS The following section gives detailed representation of the optimization problem hSFS ðfÞ ¼ ð5Þ where Qf is the load quality factor and is defined as follows: p 6ị Qf ẳ R C=L where R, L and C are the load resistance, inductance and capacitance respectively (B) Problem Constraints Total harmonic distortion limits and accelerating gain limit are the main constraints for which the objective function in (2) is subjected: i Total harmonic distortion limits Due to its negative impact on the power quality of the distribution system, THD must be limited to a preset value According to IEEE Std 929–2000 limits THD must be lesser than 5% [43] ii Accelerating gain limit The performance of the SFS is affected by the positive gain coefficient K Better performance is achieved with larger values of K Meanwhile increasing K will increase the current distortion and results in bad effect on system power quality So, the value of K factor must not violate the power quality of the system This constraint can be expressed mathematically as [19] 5.1 Problem formulation K> In order to modify the SFS parameters for better islanding detection method, AIS is used as an optimization technique to optimize these parameters AIS mimics these biological principles of clone generation, proliferation and maturation The fundamental of AIS is inspired by the theoretical immune system and the observed immune functions, principles, and models [38,39] Today, the AIS techniques are used to solve complex problems in many areas that include engineering, science, computing, and other research areas pCf ðfÞ Qf 12:5p ð7Þ The problem is solved using AIS to obtain the optimal values of both Cfo and K that minimize the NDZ and THD to acceptable values in a short time 5.2 Proposed algorithm procedure The following steps summarize the procedure of applying the proposed SFS–based AIS method to solve the islanding detection problem using SFS technique (A) Objective function i Antibody representation The objective function can be minimized by controlling the design variables of the SFS The objective function can be expressed mathematically as MinDFo ị ẳ fomax fomin 2ị In applying AIS to solve a problem, the solution of the problem is considered as a population of antibodies In this study both Cfo and K are considered as antibody and each parameter is represented by a gene of antibody Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 A.Y Hatata et al ii Antigen representation values To represent this case using Matlab Simulink, the circuit breaker that connects the utility grid with the DG is opened at a significant time forming islanding After this, the system values are disturbed and it is possible to detect islanding as fast as possible The antigen can be referred to the case of islanding occurring and as a consequence, the voltage, frequency, and the THD measured from the PCC are deviated from its nominal Start Read all system data for the test system (Figure 4), parameters limits Set the initial antibodies population for Cfo and K For each antibody in population, simulate the model and calculate the objective function NDZ, F, THD, and IDT Calculate the affinity from equation (8) Yes No Reach population size? Select antibodies whose affinity is high for cloning Clone the selected antibodies in set C For each mutated antibody, simulate the model; calculate the objective function NDZ, F, THD, and IDT Then calculate the affinity Mutate the selected antibodies in set T Reselect the best antibody that has the highest affinity and remove the worst No Yes Reach the population size? No Conditions are satisfied? Yes End Figure Print the data for K, Cfo, THD, NDZ, and IDT Flowchart of the modified SFS – based AIS method Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 Anti-islanding detection method vi Cloning iii Initialization The first step of applying the AIS method is the generation of initial population The initial population is generated between a pre-defined minimum and maximum values using a random function If the generated solutions not fall into the feasible range, they are ignored and the generation process is repeated until the required number of solutions is generated The size of the initial population is determined by making a trade-off between quality of a solution and computation time to yield that solution iv Affinity function To introduce affinity (fitness) function, the variables should be put in the model and then the difference of the estimated values from the actual data for each antibody is calculated and in each generation the individual with minimum difference must be returned Individual parameters are selected randomly and the affinity is calculated according to Euclidean distance [44]: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xm xi yi ị2 affinityx; yị ẳ =m 8ị iẳ1 v Selection In selection step, n antibodies with highest affinity are selected to generate a new population The selected n individuals of the population that are cloned (reproduced) by the clonal process, give rise to a temporary population of clones, C The higher the affinity, the larger the number of clones generated for each of the n selected antibodies Antibodies in the population must be cloned according to their affinity and generate a temporary population vii Mutation The cloned antibodies are mutated to create a population T During mutation, it assigns a lower mutation rate for higher affinity antibodies than low affinity antibodies The idea is that the antibodies close to a local optimum need only be finetuned, whereas antibodies far from an optimum should move larger steps toward an optimum or other regions of the affinity landscape viii Reselection and diversification This process reselects the improved antibodies from the population T to update it Finally, the diversity introduction process replaces the low affinity antibodies with new ones A flowchart of the proposed SFS – based AIS method is shown in Fig (a) System Respoce (b) Voltage at PCC 400 80 200 Vpcc (Volts) System Responce (Seconds) 100 60 40 -200 20 X= 0.23722 Y= 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 -400 0.5 0.05 0.1 0.15 Time (Seconds) 0.25 0.3 0.35 0.4 0.45 0.5 Time (Seconds) (c) Load Current (d) System Frequency 150 100 Frequency (Hz) Iload (Ampere) 0.2 -2 -4 50 -50 0.05 0.1 0.15 0.2 0.25 0.3 Time (Seconds) 0.35 0.4 0.45 0.5 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Time (Seconds) Figure SFS anti-islanding detection method output waveform for PV (a) system response, (b) voltage at PCC, (c) load current and (d) system frequency Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 A.Y Hatata et al (b) Voltage at PCC 500 80 Vpcc (Volts) System Responce (Seconds) (a) System Respoce 100 60 40 20 X= 0.2324 Y= 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 -500 0.4 0.05 0.1 0.15 Time (Seconds) (c) Load Current 0.3 0.35 0.4 150 100 Frequency (Hz) Iload (Ampere) 0.25 (d) System Frequency -2 -4 -6 0.2 Time (Seconds) 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 50 -50 -100 0.05 0.1 0.15 Time (Seconds) 0.2 0.25 0.3 0.35 0.4 Time (Seconds) Figure SFS anti-islanding detection method output waveform for wind (a) system response, (b) voltage at PCC, (c) load current and (d) system frequency NDZ 0.8 0.6 0.4 0.2 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 K Figure 0.04 0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 Cfo Population and cloning of the best affinity values Results and discussion In this paper the grid-connected current controlled inverter fed by a DC voltage source is disconnected after 0.228 s Both the conventional SFS and the proposed modified SFS – based AIS methods are applied to the test system and a comparison between the results is presented below 6.1 Conventional SFS method The conventional SFS method is applied to the test system shown in Fig The SFS is applied to two cases using Matlab Simulink The first case is PV system and the second is wind system These two cases are used to verify the effectiveness of the proposed method (A) Case 1: PV connected to grid For the case of PV connected to grid, the DG in Fig is represented by PV system The simulation results show that: the feedback gain K equals to 0.096 and Cfo equals to 0.026 with a NDZ of 0.97359 and THD of 4.43% Fig 6a illustrates the system response for detecting the islanding condition The islanding is detected at 0.2372 s so that the DG is disconnected 9.2 ms after the grid disconnection (which occurs at 0.228 s) Fig 6b and c shows the voltage and load current waveforms during the simulation period, whereas Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 Anti-islanding detection method Fig 6d shows disconnection the frequency change after the grid (B) Case 2: Wind connected to grid For the case of wind connected to grid, the DG in Fig is represented by a wind system The simulation results show that: the feedback gain K equals to 0.108 and Cfo equals to 0.027 with a NDZ of 0.97025 Hz and THD of 4.048% The utility grid is disconnected at 0.228 s After the disconnection of the grid, the system detects the islanding by applying SFS method and sends a trip signal to disconnect the DG at 0.2324 s that is after 4.4 ms from the grid disconnection as shown in Fig 7a When the system responds to detect island, the voltage at PCC and load current begins to decay until reaching steady state values and disconnects the DG as shown in Fig 7(b) and (c) Fig 7d shows the frequency change after the grid is disconnected that the DG is disconnected 1.28 ms after the grid disconnection Fig 9b and c shows the voltage and load current waveforms during the simulation period, whereas Fig 9d shows the frequency change after the grid disconnection (B) Case 2: Wind connected to grid According to this method the optimal values of the feedback gain K and Cfo are 0.13 and 0.026 respectively, with a NDZ of 0.9641 Hz and THD of 3.35% From Fig 10a the system responds for detecting the island condition at 0.22924 s so that the DG is disconnected after 1.24 ms from the grid disconnection (The grid is disconnected at 0.228 s.) When the grid is disconnected, the voltage at PCC and the load current start to decay after sending a trip signal to disconnect the DG, as explained in Fig 10(b) and (c) The frequency change after the grid is disconnected is shown in Fig 10d 6.2 Proposed SFS – based AIS method 6.3 Result analysis In this case the modified SFS-based AIS method is applied to the studied system Fig shows population and cloning of the best affinity values Set the parameter K as a population from 0.01 to 1, and set the parameter Cfo as a population from 0.01 to 0.05 (A) Case 1: PV connected to grid (a) System Respoce (b) Voltage at PCC 400 100 80 200 Vpcc (Volts) System Responce (Seconds) The optimal values of the feedback gain K and Cfo are 0.108 and 0.036 respectively, with a NDZ of 0.970164 and THD of 3.91% Fig 9a illustrates the system response for detecting the islanding condition The islanding is detected at 0.22928 s so A comparison between the conventional SFS islanding detection method and the proposed SFS method is made to validate the effectiveness of the proposed method The proposed method reduced the NDZ, THD and the islanding detection time (IDT) Table illustrates a comparison between the conventional and the proposed SFS method for NDZ, THD and IDT It is observed that the modified method can reduce the NDZ for the two case studies There is an improvement in the THD value after applying the modified SFS method For the PV system connected to grid, there is an improvement in the THD by 11.7% and for the wind system connected to grid, the THD is improved by 17.2% The IDT for the first 60 40 20 0 -200 X= 0.2259 Y= 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 -400 0.5 0.05 0.1 0.15 Time (Seconds) 100 -2 0.05 0.1 0.15 0.2 0.25 0.3 Time (Seconds) 0.25 0.3 0.35 0.4 0.45 0.5 (d) System Frequency 150 Frequency (Hz) Iload (Ampere) (c) Load Current -4 0.2 Time (Seconds) 0.35 0.4 0.45 0.5 50 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Time (Seconds) Figure SFS based AIS anti-islanding detection method output waveform for PV (a) system response, (b) voltage at PCC, (c) load current and (d) system frequency Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti-islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 10 A.Y Hatata et al (b) Voltage at PCC 500 80 Vpcc (Volts) System Responce (Seconds) (a) System Respoce 100 60 40 20 0 X= 0.22924 Y= 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 -500 0.4 0.05 0.1 Time (Seconds) (c) Load Current 0.25 0.3 0.35 0.4 (d) System Frequency 150 100 Frequency (Hz) Iload (Ampere) 0.2 Time (Seconds) -2 -4 -6 0.15 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 50 -50 -100 0.05 Time (Seconds) 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Time (Seconds) Figure 10 SFS based AIS anti-islanding detection method output waveform for wind (a) system response, (b) voltage at PCC, (c) load current and (d) system frequency References Table SFS Comparison between conventional and modified Conventional SFS method Modified SFS based AIS PV connected to grid THD = 4.43% IDT = 9.2 ms NDZ = 0.97359 Hz THD = 3.91% IDT = 1.28 ms NDZ = 0.970164 Hz Wind connected to grid THD = 4.048% IDT = 4.4 ms NDZ = 0.97025 Hz THD = 3.35% IDT = 1.24 ms NDZ = 0.9641 Hz case is improved by 86.08%, and for the second case study it is improved by 71.8% Conclusion This paper presents a proposed technique to modify SFS antiislanding method using AIS SFS is one of an active methods for islanding detection and has its advantages in addition to that it has a feedback gain that can be used for rapidly islanding detection AIS is an optimization technique used to optimize the SFS parameters to achieve more efficient islanding detection The modified SFS method is applied to two cases studies using MATLAB/SIMULINK A comparison between the proposed method and conventional SFS was presented and the results show that the proposed method generates less THD than the 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based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 ... study of detection anti- islanding Please cite this article in press as: A.Y Hatata et al., Proposed Sandia frequency shift for anti- islanding detection method based on artificial immune system, ... al., Proposed Sandia frequency shift for anti- islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 Anti- islanding detection. .. al., Proposed Sandia frequency shift for anti- islanding detection method based on artificial immune system, Alexandria Eng J (2017), http://dx.doi.org/10.1016/j.aej.2016.12.020 Anti- islanding detection