Enhancing distribution generator impact mitigation using an adaptive protection scheme based on modified pelican optimization algorithm and active database management system

This paper addresses the challenge of protecting electrical networks in the presence of distribution generators (DGs). The use of DGs affects fault currents, leading to miscoordination between protection relays and causing constraints on network reliability. To tackle this issue, the authors propose an adaptive protection scheme (APS) based on a modified pelican optimization algorithm (MPOA) and active database management system (ADBMS). The APS coordinates directional overcurrent relays and distance relays, while the MPOA simulates a pelican mating strategy and includes a modified internal equation. The proposed APS is further upgraded with ADBMS to save system resources by storing relay settings in the database and calling them when the state of DGs changes without running optimization algorithms. The effectiveness of the proposed APS is validated on the Institute of Electrical and Electronics Engineers (IEEE) eight‐bus test system and the IEEE 14‐bus distribution network. Results indicate that the APS can effectively protect electrical networks in the presence of DGs, while the ADBMS upgrade saves system resources.


| INTRODUCTION
In recent years, there has been an increasing integration of renewable energy resources (RESs) into the electrical grid.These RESs, including solar, wind, and hydropower, are being combined with other energy sources to improve the stability of the network and reduce losses.As a result, there has been an increased utilization of RES in various locations on the electrical grid. 1 However, the incorporation of distribution generators (DGs) into the electrical grid also brings several challenges, one of which is the coordination of protective relays.The impact of DGs on power generation causes changes in the magnitude and direction of electrical current, resulting in power flow changes. 2 Due to the increased magnitude of electrical current, protective relays may malfunction, particularly when DGs are involved.DGs can have a greater impact on fault current since they provide more power to the fault area, leading to protective relays' miscoordination and malfunction. 3o protect transmission lines, directional overcurrent relays (DOCRs) and distance relays are commonly used.DOCRs are more complex and expensive than overcurrent relays (OCRs) as they rely on a voltage phaser via a potential transformer.However, they are also more effective and reliable. 4istance relays are a crucial and widely used transmission line protection equipment.They consist of several zones, with the first zone responding quickly to faults and protecting 80% of the transmission line.The second zone, which covers 120% of the transmission line, operates with a delay time, resulting in overlapping between DOCRs and distance relays. 5,6o address the impact of DGs on the electrical grid, an adaptive protection scheme (APS) has been proposed in this study.This APS is enhanced with an active database management system (ADBMS) and is based on the pelican optimization algorithm (POA) and its modified version (modified pelican optimization algorithm [MPOA]).The proposed APS is designed to solve the coordination process between directional and distance overcurrent relays (D&DOCRs) and to maximize the benefits of both types of relays while avoiding overlapping.Although this coordination process is complex and adds constraints to the optimization problem, the APS offers an effective solution to the challenges posed by DGs.
APS relies on a communication network that links all components of the electrical grid, which can be either supervisory control and data acquisition (SCADA) or power line carrier.This network provides APS with data collection and transmission capabilities for the new protective relay settings.Optimization techniques are utilized by APS to coordinate protective relays, and the proposed POA has been enhanced to enhance exploration and exploitation.This updated algorithm allows APS to obtain a more optimal solution with fewer iterations and avoid constraints. 7o prevent the recall of the optimization algorithm with any changes in the electrical grid components, APS is upgraded using ADBMS.ADBMS saves every coordination made by the optimization method, regardless of the electrical grid component's situation. 8The optimal coordination is sent directly based on the information saved in the database without recalling the optimization algorithm.This saves time and resources on APS components.To improve the benefits of APS, additional databases can be distributed, allowing it to operate even if one of the servers fails. 9daptive schemes were designed to function as realtime systems, necessitating rapid techniques for adjusting the settings of protection relays.The APS employs optimization algorithms due to its quick performance.Numerous research papers have addressed APS, focusing on the coordination of DOCRs using various optimization algorithms, such as particle swarm optimization (PSO), 10 genetic algorithm, 11 differential evolution algorithm, 12 ant colony optimization, 13 firefly algorithm, 14 gravitational search algorithm, 15 manta ray foraging optimization, 16 and hybrid Harris hawks optimization. 17n the past few years, there have been many optimization algorithms developed to overcome the limitations of well-known algorithms, including sand cat swarm optimization and turbulent flow of water-based optimization. 18Moreover, ongoing efforts are being made to improve the performance of existing algorithms and address their limitations.For instance, PSO has been further developed by Ghasemi et al. 19 and Yang and Liu. 20This paper proposes a new version of APS, which utilizes ADBMS, to tackle the same coordination problem between DOCRs and distance relays, thus enhancing the overall performance of the system.
The optimization algorithm proposed for coordinating protective relays is called POA, which is a novel algorithm inspired by nature.The algorithm simulates the behavior of pelicans searching for food and has been updated to include a mating method.POA is based on the No Free Lunch theorem, which guarantees that algorithms can provide quasioptimal solutions to optimization problems.The algorithm's key advantage is its exploration component, which enhances its efficiency. 21n ADBMS refers to a database that can respond or operate automatically in response to a particular event or situation.The term was first coined towards the end of the twentieth century.The use of ADBMS in the proposed APS can significantly improve its speed and effectiveness compared to an APS that solely relies on optimization algorithms. 22ADBMS can also be deployed across multiple locations, which can protect the network from server damage and connection failures. 23he proposed APS scheme presented in this paper can help address the challenges posed by DGs in protective systems, leading to a healthier and more reliable protection system.The paper's main contributions include modifying the exploration and exploitation behaviors of an optimization algorithm, proposing a new APS based on POA and MPOA, upgrading the APS with ADBMS to improve its speed and efficiency, and using the APS to coordinate both D&DOCRs.The main contributions of this paper are as follows: ✓ The paper focuses on the coordination of both D&DOCRs in the presence of DGs.This results in a challenging nonlinear problem with significant constraints.✓ The proposed APS is equipped with an ADBMS, enabling the storage of optimal solutions.This approach helps conserve system resources and allows for faster retrieval of optimal solutions compared to running the entire code.✓ To enhance the convergence and response of the proposed APS, the PSO algorithm is modified.Two main aspects were altered: the internal equation was modified, and a mating strategy inspired by pelicans was simulated.
The remaining parts of the paper are organized as follows: Section 2 discusses the coordination problem and presents the modeling approach used.Section 3 presents the proposed APS, while Section 4 discusses and analyzes the performance of the proposed APS and algorithms.Finally, Section 5 summarizes the main findings and presents future research directions.

| OPTIMIZATION PROBLEM
The main objective of this paper is to achieve optimal coordination between DOCRs and distance relays.The objective function (OF) includes the total operating times of DOCRs at the near (T Near ) and far (T Far ) ends, as well as the second zone time of distance relays (T Z2 ).The OF aims to minimize the total operation time and achieve the fastest possible coordination, as described in Abdelhamid et al. 24


The standard inverse time characteristics of DOCRs in the International Electrotechnical Commission standards are represented by the following equation 25 : where T i represents the operation time of the ith DOCR, TDS represents the relay's time dial setting, and I p represents the relay's pickup current.Other constants α, β, and γ have fixed values of 0.14, 0.02, and 1, respectively. 26

| The problem's limiters
To ensure the protection of power infrastructure from damages, the primary limiter of any protection relay is the maximum operation time (T max ), which cannot exceed 2 s. 27 Additionally, the relay settings are constrained with both maximum and minimum values for each setting, as shown in the following equations 28 :

| The problem's constraints
The proposed optimization problem becomes a highly constrained problem due to the constraints between the primary and backup pair of DOCRs at both ends, as well as the constraints between a pair of DOCRs and distance relays.These constraints are necessary to prevent miscoordination or overlapping from occurring between relay pairs.As shown in Figure 1, the relationship between DOCRs pair relays must consider the backup relay's time (T b ), which operates with a delay after the primary relay's time (T p ).This delay is referred to as the coordination time interval (CTI), and its value depends on the type of protective relays used.For electromagnetic relays, the CTI value should be larger than 0.3 s, while for digital relays, it should be greater than 0.2 s.In this paper, digital relays are used, and the CTI constraints are expressed by the following equation: The relationship between DOCRs and distance pair relays is depicted in Figure 2. The backup distance relay aliasing, with the primary DOCRs relay, and T Z b 2 must lag behind (T p f 1 ) by the CTI at the near end, as presented in Equation ( 8), while Equation ( 9) presents the distance and DOCRs relationship at the far end.The primary distance relay's second zone (T Z p 2 ) must lag behind the primary DOCR's operation time (T p f 2 ) by CTI at the far end.
Equation ( 10) uses the primary relay's operating time at the near and far ends to determine the optimal value for the second zone of the distance relay.The equation selects the maximum value among these values to set the time for the second zone of the distance relay, resulting in a reduction in penalties and constraints. 24max T T = ( , ).
To prevent miscoordination between relay pairs, a penalty function is suggested and expressed in the following equation 29 : where μ is the weighting factor of the penalty function.
In case of failure to coordinate between a relay pair, extending the OF becomes necessary for F pen .Consequently, the optimization technique adjusts its parameters to minimize the OF, aiming to eliminate miscoordination.

| ADAPTIVE PROTECTION SCHEME
The authors propose an adaptive scheme for protecting transmission lines using D&DOCRs at both near and far ends, where the decision-making process is based on monitoring the network's state.The fault current is used to determine the status of coordinated protection relays, which can malfunction due to changes in fault current caused by power flow and generation flow variations.Therefore, the proposed APS is essential for maintaining the reliability and health of the electrical network.
To improve the performance and convergence of the coordinated relay's settings, ETAP is used to calculate fault currents in the APS, and a modified algorithm is applied.The proposed algorithm is detailed in subsequent sections.
The main objective of the proposed APS is to integrate ADBMS with the system, which involves storing optimal values in the database and retrieving them based on events triggered by the state of DGs in the electrical grid.This ensures that optimal values are sent to the protection relays without calling the optimization algorithm, thereby conserving APS resources.
To enhance the efficiency of the scheme, distributed processing servers are employed instead of centralized servers, which can cause limitations.APS relies on communication networks such as SCADA to connect the distributed processing servers and protection relays.These systems utilize microprocessor-based protection relays that can be reset remotely.
The flowchart presented in Figure 3 outlines the main stages of the suggested APS.These stages are as follows: Step 1: Utilizing the communication network, the most recent configuration of the electrical grid is defined, including the size, location, and specific status of DGs.The system then scans for any changes in the electrical grid component information.If no changes are detected, the APS will continue to monitor.However, if a change is detected, the APS will proceed to the next stage.
Step 2: The APS checks the database for the most recent electrical grid configuration.If the configuration is already present in the database, the APS will call up the protection relays' settings.If not, the APS moves on to the next stage.
The flowchart of the proposed APS.ADBMS, active database management system; APS, adaptive protection scheme; D&DOCRs, directional and distance overcurrent relays; ETAP, electrical transient analyzer program; MPOA, modified pelican optimization algorithm; POA, pelican optimization algorithm.
Step 3: The APS utilizes ETAP to determine the fault currents flowing through the transmission lines via CBs.To address any new changes in the electrical grid, the APS examines the quality of the most recent relay settings.If these settings are adequate to handle the new network state without miscoordination or misoperation, the APS returns to monitoring the electrical grid.Otherwise, the APS proceeds to the next stage.
Step 4: The APS employs the modified algorithm proposed in the study to search for new relay settings that can manage the new electrical grid state effectively.Finally, the APS sends the updated relay settings to the network operator or intelligent electronic devices through information and communications technology.

| The original POA
Pelicans are social birds that thrive in groups of hundreds.With their large bodies, they are expert fishers and also prey on frogs and turtles.Their hunting behavior is a remarkable process that has led to their proficiency as hunters.This behavior served as the main inspiration for the design of the proposed POA, 21 with the pelicans' mating strategy being incorporated in the MPOA (Figure 4).
The POA is simulated such as other population-based algorithms with members of the population.Each member is generated at random from the search space's lower and upper bounds.For updating pelican's solutions.There are two phases to simulate the pelican's behavior and strategy for hunting prey.These phases are mathematically modeled as follows:

| First phase (exploration): Moving towards prey
In this phase, the prey is generated at random rather than based on their best solution.The pelican then moves towards the prey according to the following equation: where x i j P , 1 is the updating position according to the first phase of the ith solution and the jth dimension.p j is the random prey's position for the pelican.I is a random choice of 1 or 2. This parameter is the key to the algorithm that is used to explore the area.The new position of pelican is accepted, if its OF is better.

| Second phase (exploitation): Winging on the water surface
During this phase, after reaching the surface of the water, the pelicans spread their wings to move the fish upwards, then collect the prey in their throat pouch.
This strategy causes pelicans to catch more fish in the attacked area.
Modeling pelican behavior causes the proposed POA to converge to better hunting locations.This process leads to improve its exploitation ability.This hunting behavior of pelicans is mathematically simulated as the following equation: where x i j P , 2 is the updating position according to the second phase of the ith solution and the jth dimension.R is a constant with a value of 0.2.t is the current iteration value, and T is the maximum iteration value.The POA algorithm is a newly developed method that introduces a novel concept to improve exploration behavior.This modification is centered on two key aspects, enhancing both exploration and exploitation behaviors, with the aim of achieving high-speed performance and obtaining a superior optimal solution while avoiding constraints.

| First point: An internal equation
Equation ( 12) is modified by developed I as the following equation: The parameter I′ has been introduced in Equation ( 14) to achieve a balance between exploration and exploitation performance, allowing the algorithm to exploit the population and achieve better optimal solutions.

| Second point: Mating strategy
The POA algorithm is a newly developed method that introduces a novel concept to improve exploration behavior.This modification is centered around two key aspects, enhancing both exploration and exploitation behaviors, with the aim of achieving high-speed performance and obtaining a superior optimal solution while avoiding constraints. 24n this study, the mating strategy has been updated to reflect a more realistic approach.This was achieved by introducing a new parameter, called the "adult factor," which takes into account that not all pelicans in the group are adults.Some of the birds are still in the process of growing and building their nests.
The adult factor is a user-defined percentage that determines the number of adult pelicans in the group.These adult pelicans are the ones with a better OF and are selected to mate to increase the exploitation of parent solutions.Meanwhile, the immature birds continue to search for new nests, which ensures continuous exploration throughout the algorithm iterations.This phase is referred to as the third phase and is mathematically simulated using the following equation: where x j rand is generated randomly as the initial population from upper and lower limiters of search space (Figure 5).

| RESULTS AND DISCUSSION
To assess the effectiveness of the proposed APS, two test cases were utilized, namely, the Institute of Electrical and Electronics Engineers (IEEE) eight-bus test system No and the IEEE 14-bus distribution network.Both test cases used D&DOCRs as the protection relays.The coordination of D&DOCRs at both near and far ends was achieved using CTI, with a value of 0.2 s.The DOCRs used were time inverse type, with constant values of 0.14, 0.02, and 1 for α, β, and γ, respectively, as stated in reference. 25The settings for the DOCRs were limited to a minimum of 0.5 and a maximum of 4 for PS, while TDS was limited to 0.1 and 1.1 s, respectively.Furthermore, the operating time for both the distance relays and primary relays of DOCRs was restricted to not exceed 2.0 s, as specified in Rivas et al. 13 The efficiency of the proposed APS was evaluated in two stages.First, it was tested on the normal topology of the electrical network of the test case, and second, its ability to coordinate protection relays after DGs were brought online was tested.The proposed scheme utilizes POA and MPOA with a 300-population size during 1000 iterations, with an adult factor of 30% in MPOA.The APSs were implemented in MATLAB R2016a, and ETAP 12.6.0was used for calculating three-phase fault currents.

| Test case 1: IEEE eight-bus test system
The IEEE eight-bus test system, depicted in Figure 6, consists of eight buses interconnected by seven transmission lines, with each transmission line having two circuit breakers (CB).In total, there are 14 CBs protecting the transmission lines of the network.This system contains two synchronous generators that supply power to four loads. 20o test the proposed APSs, an external microgrid (EG) with a capacity of 400 MW was added to this network, causing changes in the magnitude and direction of the three-phase fault currents.As a result, 42 design variables and 32 constraints between DOCRs were affected in the normal electrical network, while 34 constraints between DOCRs were affected after the EG was online.The constraints between Distance relays and DOCRs remained the same, with a total of 40 constraints.All variable designs were optimized by the APSs within specified limits. 6,13able 1 presents the optimal values for the variable designs that were tuned by APS based on POA and MPOA at a normal electrical network.These values were selected to avoid miscoordination between DOCRs and D&DOCRs, as shown in Table 2.After the EG was online, the APS based on POA and MPOA were rearranged and coordinated with the protection relays, and the optimal values are listed in Table 3.These optimal values were tuned using the proposed optimization algorithms and satisfy all constraints, as demonstrated in Table 4.
These tables illustrate the effectiveness of the proposed APS in coordinating D&DOCRs at both near and far ends with optimal values that respect all constraints.The APS based on MPOA outperforms the APS based on POA since its optimal solutions are lower than those obtained by the POA-based APS.
Figures 7 and 8 depict the convergence behavior and penalty values of POA and MPOA algorithms for the normal electrical network.Figures 9 and 10, on the other hand, show the convergence characteristics and penalty values for the electrical network with EG online.The results demonstrate that the APS based on MPOA converges faster, avoids constraints, and produces better optimal values compared to APS based on POA.Therefore, MPOA has the potential to enhance the exploration and exploitation behaviors of the algorithm.

| Test case 2: IEEE 14-bus distribution network
The test case illustrated in Figure 11 30 is known as the IEEE 14-bus distribution network and is based on the downstream of the IEEE 14-bus test system.Two synchronous generators with a power output of 5 MVA and a power factor of 0.9 lagging are added to the distribution network, located at the fifth and seventh buses, to enhance their electrical performance. 20,31he presence of the DGs in the network led to variations in the magnitude of the three-phase fault currents, necessitating the coordination of both D&DOCRs based on the status of the DGs (online or offline).The optimization problem required the tuning of between DOCRs and 44 constraints between D&DOCRs. 31,32he optimal values of the variable designs are presented in Table 5, which were tuned by the APS using POA and MPOA for a normal electrical network.The values were able to prevent miscoordination between DOCRs and D&DOCRs, as shown in Table 6.After the DGs were brought online, the APS based on POA and MPOA were reconfigured to coordinate the protective relays, resulting in the optimal values listed in Table 7.All constraints were adhered to, as demonstrated by Table 8.These tables highlight the ability of the proposed optimization algorithms to achieve optimal values that prevent miscoordination and adhere to constraints in coordinating D&DOCRs.
Table 5 presents the variable designs and their optimal values, which were tuned by APS based on POA and MPOA in a normal electrical network.As can be seen in Table 6, these values prevent miscoordination between DOCRs and D&DOCRs.After the EG was online, APS based on POA and MPOA were rearranged to coordinate the protective relays, as shown in Table 7.These optimal values were obtained using the proposed optimization algorithms while respecting all constraints, as shown in Table 8.
These tables demonstrate the proposed APS's ability to coordinate D&DOCRs at both near and far ends with optimal values while considering limiters and avoiding all constraints.The APS based on MPOA outperforms the APS based on POA, as its optimal solutions are lower.
The convergence characteristics of POA and MPOA with a normal electrical network are illustrated in Figure 12, whereas their penalty values are shown in Figure 13. Figure 14 displays the convergence characteristics of POA and MPOA with EG online, and their penalty values are presented in Figure 15.These figures demonstrate that APS based on MPOA can converge faster, achieve better optimal values, and avoid limitations, highlighting MPOA's potential to increase exploration and exploitation behaviors.T A B L E 5 Optimal values of the relay settings of the normal grid of the IEEE 14-bus distribution network.better for both the normal electrical grid of the IEEE eight-bus test system and the normal electrical grid of the IEEE 14-bus distribution network.

| The comparison study
This section presents a comparison between the suggested APS algorithms based on POA and MPOA with existing APSs that use well-known optimization algorithms such as PSO, 28 tunicate swarm algorithm, 28 and school-based optimizer (SBO). 6The optimal solutions obtained from the proposed APS and the other algorithms are listed in Table 10.On the basis of the results in the table, it can be observed that the APS employing MPOA consistently provides the best optimal solution for all case studies.Moreover, it is worth noting that in the IEEE eightbus test system, the APS based on POA outperforms the APS based on PSO in terms of obtaining a more optimal solution.Similarly, in the IEEE 14-bus distribution network, the APS based on POA surpasses the APS based on SBO to achieve a better optimum solution.

| CONCLUSION
In conclusion, the proposed APS based on MPOA and upgraded with ADBMS has been shown to effectively solve the coordination problem between protective relays in the presence of DGs in electrical networks.The APS algorithm has successfully coordinated both DOCRs and the second zone time of the distance relay, resulting in increased system reliability.The MPOA has proved to be more effective in solving coordination problems due to its higher convergence characteristics and better optimal values than other suggested algorithms.The APS system has been upgraded with ADBMS to save system resources and time by storing optimization solutions based on the condition of the network's components.Despite the proposed solution's limitations, such as the high infrastructure cost required, the proposed APS system offers an efficient and reliable solution for mitigating the impact of DGs on electrical networks, ensuring the protection of the system's components without any miscoordination between primary and backup protection relays.| 4125

F I G U R E 1
The relationship respectively DOCRs pair relays.CTI, coordination time interval; DOCR, directional overcurrent relay.F I G U R E 2 The relationship between DOCRs and distance pair relays.CTI, coordination time interval; DOCR, directional overcurrent relay.

F
I G U R E 4 The flowchart of pelican optimization algorithm (POA).

5
Flowchart of MPOA.MPOA, modified pelican optimization algorithm; POA, pelican optimization algorithm.F I G U R E 6 The single line diagram of the Institute of Electrical and Electronics Engineers eight-bus test system.T A B L E 1 Optimal values of the relays settings of the normal grid of the IEEE eight-bus test system.

F
I G U R E 11 The single line diagram of the IEEE 14-bus distribution network.IEEE, Institute of Electrical and Electronics Engineers.
T A B L E 2 Constraints according to APS based on MPOA's optimal values in a normal grid of IEEE eight-bus test system.
T A B L E 3 Optimal values of the relay settings of the IEEE eight-bus test system after EG switched on.Constraints according to APS based on MPOA's optimal values of IEEE eight-bus test system after EG switched on.
Abbreviations: EG, external microgrid; IEEE, Institute of Electrical and Electronics Engineers; OF, objective function; POA, pelican optimization algorithm; TDS, time dial setting.Abbreviations: APS, adaptive protection scheme; CTI, coordination time interval; D&DOCR, directional and distance overcurrent relay; DOCRs, distance overcurrent relays; EG, external microgrid; IEEE, Institute of Electrical and Electronics Engineers; MPOA, modified pelican optimization algorithm.F I G U R E 7 Operation times convergences of proposed APS in the normal grid of IEEE eightbus test system.APS, adaptive protection scheme; IEEE, Institute of Electrical and Electronics Engineers; MPOA, modified pelican optimization algorithm; POA, pelican optimization algorithm.F I G U R E 9 Operation times convergences of proposed APS of IEEE eight-bus test system after EG switched on.APS, adaptive protection scheme; IEEE, Institute of Electrical and Electronics Engineers; EG, external microgrid; MPOA, modified pelican optimization algorithm; POA, pelican optimization algorithm.F I G U R E 10 Penalty convergences of proposed APS of IEEE eight-bus test system after EG switched on.APS, adaptive protection scheme; IEEE, Institute of Electrical and Electronics Engineers; EG, external microgrid; MPOA, modified pelican optimization algorithm; POA, pelican optimization algorithm.
Abbreviations: IEEE, Institute of Electrical and Electronics Engineers; POA, pelican optimization algorithm; TDS, time dial setting.Constraints according to APS based on MPOA's optimal values in a normal grid of IEEE 14-bus distribution network.

Table 9
Abbreviations: APS, adaptive protection scheme; CTI, coordination time interval; D&DOCRs, directional and distance overcurrent relays; DOCRs, distance overcurrent relays; IEEE, Institute of Electrical and Electronics Engineers; MPOA, modified pelican optimization algorithm.Optimal values of the relay's settings of the IEEE 14-bus distribution network after DGs switched on.Constraints according to APS based on MPOA's optimal values of IEEE 14-bus distribution network after DGs switched on.
Abbreviations: APS, adaptive protection scheme; CTI, coordination time interval; D&DOCRs, directional and distance overcurrent relays; DOCRs, distance overcurrent relays; IEEE, Institute of Electrical and Electronics Engineers; MPOA, modified pelican optimization algorithm.F I G U R E 12 Operation times convergences of proposed APS in the normal grid of IEEE 14-bus distribution network.APS, adaptive protection scheme; IEEE, Institute of Electrical and Electronics Engineers; MPOA, modified pelican optimization algorithm; POA, pelican optimization algorithm.F I G U R E 14 Operation times convergences of proposed APS of the IEEE 14-bus distribution network after DGs switched on.APS, adaptive protection scheme; DGs, distribution generators; IEEE, Institute of Electrical and Electronics Engineers; MPOA, modified pelican optimization algorithm; POA, pelican optimization algorithm.
T A B L E 9 Statistical analyses of APS.: APS, adaptive protection scheme; DGs, distribution generators; EG, external microgrid; IEEE, Institute of Electrical and Electronics Engineers; MPOA, modified pelican optimization algorithm; POA, pelican optimization algorithm.T A B L E 10 Comparison study of proposed APS and others based on well-known algorithms.