Optimized Fractional Order Integral-Tilt Derivative Controller for Frequency Regulation of Interconnected Diverse Renewable Energy Resources

The interconnection of renewable energy systems, which are complex nonlinear systems, often results in power fluctuations in the interconnection line and high system frequency due to insufficient damping in extreme and dynamic loading situations. To solve this problem, load frequency control ensures nominal operating frequency and orderly fluctuation of grid interconnection power by delivering high-quality electric power to energy consumers through efficient and intelligent control systems. To introduce the frequency control of power systems, this paper presents a novel control technique of Fractional Order Integral-Tilt Derivative with Filter (FOI-TDN) controller optimized by the current soft computing technique of hybrid Sine-Cosine algorithm with Fitness Dependent Optimizer (hSC-FDO). For more realistic analysis, practical constraints with nonlinear features, such as controller dead band, communication time delay, boiler dynamics, and generation rate constraint are embedded in the given system model. The proposed approach outperforms some recently developed heuristic approaches such as fitness dependent optimizer, firefly algorithm, and particle swarm optimization for the interconnected power system of two areas with multiple generating units in terms of minimum undershoot, overshoot, and settling time. To improve the system performance, capacitive energy storage devices are used in each area and thyristor control phase shifter is used in the interconnection line of the power system. The potential of the hSC-FDO-based FOI-TDN is demonstrated by comparing it with conventional FOTID/FOPID/PID controllers for two areas with multiple power generators IPS. Finally, a robustness analysis is performed to determine the robustness of the presented control system by varying the system loads and system parameters.


I. INTRODUCTION
Maintaining the stability and security of interconnected modern power systems ( PSs ) has become an effective tool for additional services. These amenities provide uninterrupted power supply and ensure the quality of electricity. Frequency The associate editor coordinating the review of this manuscript and approving it for publication was Salvatore Favuzza . stabilization is an important indicator of power quality. Loadfrequency control (LFC) plays a crucial role in the operation and control of the PS and ensures the generation of high-quality electricity [1], [2]. With the help of LFC, the operating point of the power generation system is adjusted to match the amount of power generated under different load scenarios. In this way, an attempt is made to reduce frequency fluctuations in the system area. An intelligent and advanced LFC control structure is required to balance the effects of load fluctuations and maintain a certain range of generation, system frequency, and power flow in the interconnected grid. [3]- [5].

A. LITERATURE REVIEW
To improve dynamic efficiency, the LFC employs a variety of control mechanisms. Simple conventional controllers are the most commonly used LFC controllers in the industry because they are simple in structure, easy to implement, inexpensive, and well designed [6], [7]. The PID controller and its various modified forms are mostly used in AGC analysis [8]. Daraz [9] employed an I-PD for the AGC of two areas IPS with multi-generation units and showed that it outperforms the PID and PI controllers. The authors in [10] compared the performance of PID with a double derivative controller for an LFC system with the performance of I/ PI /PID controllers. The superior transient response under truly constrained conditions such as GRC, GDZ, CTD, and BD is not guaranteed by standard controllers. Due to the wide range of load magnitudes, the basic approaches of standard controllers are ineffective for acceptable dynamic output. The use of artificial neural networks and fuzzy logic controllers for the AGC system was also investigated in [11]. Compared to traditional controllers, the FLC-based LFC system optimizes the system outputs by selecting the rule base, membership features, scaling factor, and defuzzification mechanism. In contrast, FLC and ANN database analysis and training require a significant amount of processing time.
On the other hand, fractional order (FO) controllers are becoming more popular due to their adaptability and greater degree of freedom. In most cases, this leads to additional tuning requirements as new pole types are added, such as hyperdamped poles. This means that the stability range has been extended, allowing us to develop more flexible controllers. As a result, researchers have shown a strong interest in FO controllers (FOCs). In [12], [13], FOCs have been applied to a variety of electrical systems. A tilt-integral-derivative (TID) controller is also a member of the FOC family and has been used recently to solve LFC problems. The advantages of the TID controller include its ability to adjust the parameters of a closed-loop system, its robustness, and its greater ability to reject disturbances. As a result, several research papers have recommended the TID controller as a means of solving LFC problems [14], [15]. To take advantage of TID and FOPID controllers, Morsali et al. [16] proposes a hybrid controller based on their combination. Moreover, the configuration of the cascade controller (CC) was preferred to the configuration of the non cascade controller due to its advantages. Better results can be obtained if the cascaded controller has more adjustment knobs than a non-crude cascaded controller. Thus, the CC structure is one of the most successful controller strategies to improve the defence performance of a control system in control applications, especially when disturbances occur [17]. As a result, several variants of the cascaded controller have been used to improve the frequency stability of power systems [18], [19]. However, no performance evaluation of FOI-TDN structured controller has been discovered so far. Considering these facts, a proposed control strategy for multi-area AGC IPS models are presented.
To achieve optimal LFC of the power system, the design of the controller alone is not sufficient. Optimization strategies to determine the parameters of the controller are equally important in the field of LFC. Researchers in the field of LFC have used a variety of optimization techniques, such as Artificial Electric Field Algorithm (AEFA) [20] FOTID controller optimized with Path Finder Algorithm (FPA) [21], Fitness Dependent Optimizer (FDO) [22], Grasshopper Optimization Algorithm (GOA) [23], Firefly Algorithm (FA) [24], Harries Hawks Optimizer (HHO) [25], Frog Leaping Algorithm (FLA) [26], Marine Predator Algorithm (MPA) [27], Imperialist Competitive Algorithm (ICA) [28], Gray Wolf Optimizer (GWO) [29], Sine Cosine Algorithm (SCA) [30], Fuzzy PID controller optimized with Improved Ant Colony Optimization (IACO) [31], Atomic Search Optimization (ASO) [32], Improved Fitness Dependent Optimizer (I-FDO) [33], Hybrid PSO based Gravitational Search Optimization (GSO) [34], Volleyball Premier League (VPL) based optimized fractional order control [35], Hybrid Differential Evolution (DE) with Pattern Search (PS) algorithm [44], Hybrid Teaching-Learning Optimization with PS [36], Water Cycle Algorithm (WCA) based optimized I-TD controller [37], Gorilla Troops Optimizer [38] and Salp Swarm Algorithm (SSA) [39] algorithm. However, most of the above algorithms have the drawbacks of parametric sensitivity, premature convergence, and complex computation. To overcome these shortcomings, a new intelligent controller named FOI-TDN controller is presented in this work, along with a new meta-heuristic approach of hybrid SC with FDO algorithm. The main advantage of SC is the high exploitability of the search space. Incorporating the features of SC to refine the search for best neighbors and FDO to explore the entire search space for possible solutions leads to an improvement in the exploitability of FDO. For a comprehensive understanding of the LFC problem, it is crucial to include the important inherent requirements as well as the basic physical constraints in the model. In this regard, most authors have considered only a few constraints such as GRC and GDZ and neglected all possible practical constraints. However, in this study, all physical constraints such as GRC, GDZ, CTD, and BD are considered for the LFC problem of two area multi-generation resources like hydro, wind, and reheat thermal systems. Flexible AC Transmission Systems (FACTS) have been widely accepted as a way to use power electronic strategies for PS control and operation. Therefore, a TCPS and a CES are proposed in this work to improve the dynamic performance and stability of the system.

B. PREVIOUS WORK'S MOTIVATIONS AND LIMITATIONS
The primary observation based on previous literature is that LFC strategies that rely on the controller designer's experience, such as MPC, fuzzy logic control, and H-infinity techniques, achieve the desired performance but have some design flaws and take a long time to select control parameters. In addition, conventional PI and PID controllers have difficulties in dealing with system uncertainties. Most previous research paid little attention to many robustness analyses (e.g., system nonlinearities, system deficiencies, and system parameter variations). In addition, most previous research did not investigate the effect of a high renewable energy penetration on changes in system parameters. Based on these facts, this work proposes a new updated structure for the FOTIDN controller, called FOI-TDN controller, to improve the system frequency stability in the presence of system uncertainties, nonlinearities, and numerous load disturbances. In addition, the characteristics of the proposed FOI-TDN controller were chosen in accordance with the hSC-FDO to maintain both frequency and system stability under abnormal conditions.

C. CONTRIBUTION AND ORGANIZATION OF PAPER
In this study, the frequency stability of a two-area interconnected multi-source power system integrated with high renewable penetration is considered. A modified structure of the FOTIDN controller, called FOI-TDN controller, is used, which takes into account system uncertainties, nonlinearities and different load patterns. The main contribution of the research is summarized as follows compared to recent works on similar topics: 1) Design and implement a modified version of the FOTID controller known as FOI-TD controller with filter derivative for two areas, six-generation units interconnected PS including all practical constraint like GRC, GDZ, CTD and BD. 2) The proposed controller is optimized using a new meta-heuristic approach of hybrid SC with FDO algorithm. 3) This study compares the performance of the proposed hybrid SC-FDO algorithm with benchmark methods such as FDO, PSO and FA, to demonstrate its effectiveness. 4) The efficiency of the FOI-TDN controller is also compared with that of benchmark controllers such as FOTID, PID, and FOPID. 5) Further TCPS and CES are proposed in this study to improve the dynamic performance and stability of the system. 6) Furthermore, a sensitivity analysis is performed to show the potency of the proposed FOI-TDN controller by altering the system parameters and load conditions. The rest of this paper is laid out as follows. Section II describes the model of the power system under investigation followed by modeling of TCSP and CES. Section III discusses the proposed FOI-TDN controller configuration, the proposed optimization technique, and the suggested controller design procedures. Section IV contains the simulation results and analysis. Finally, in Section V, the conclusion and future work is presented.

II. POWER SYSTEM MODEL
The system under this research is a two-area distributed generation system consisting of reheat-thermal,, and wind turbines interconnected by a transmission line, as shown in Fig. 1. Moreover, practical constraints such as GRC, CTD, GDZ, and BD are incorporated into two interconnected domains PS with the addition of TCPS and CES to improve the stability of the system. The nonlinear constraints such as GRC are considered for hydrothermal units. For a thermal unit, a GRC of 10% per minute is used for both drawdown and uplift; for a hydro unit, the GRC is 360% per minute for drawdown and 270% per minute for uplift [39]. Nevertheless, communication time delays (CTDs) are another set of inherited practical constraints that have a significant impact on the current power system. The interconnected system utilises a large number of measurement and sensor devices that are normally located at remote sites. The control centre receives data from the metering devices to generate the proper control signal, which is relayed from the control centre to the generating units for appropriate response. The transmission and reception of signals between different units may not be fast; this delay is called CTD. Due to these CTDs, there is an interruption in changing the PS operating point, which has a significant impact and can occasionally cause the system to become unstable [33].
To overcome the aforementioned problem, CTDs must be included in the LFC study. A boiler is considered an integral part of any thermal plant that produces steam under pressure. Normally, boilers are drum boilers, also known as circulating boilers. The energy given off by the hot furnace walls (also known as water walls) is absorbed by the drum fluids through natural or forced circulation. The boiler receives preheated feed water from the economizer and uses it to produce saturated exhaust steam. This covers the long-standing effects of fuel and steam flow dynamics on the pressure in the boiler drum.
The present study considers BD in coal-fired units with well-tuned controls while analyzing the performance of LFC. Fig 2 depicts the block diagram representation of BD. The TF model for BD can be written as [9].
Additionally, this work considers the practical constraint GDZ solely for thermal and hydro units. GDZ is limited to 0.05 % in thermal units and 0.02 % in hydroelectric units. The TF for GDZ is given below [22]:

A. CAPACITIVE ENERGY STORAGE (CES)
Electrical energy can be stored and released in huge quantities using capacitive energy storage (CES). CES devices are fascinating consideration in theoretical and experimental research for their enormous potential in modern PS applications. They may be used to regulate system frequency variations caused by system transients and to mitigate low-frequency power fluctuations. Numerous advantages of CES include a rapid charge/discharge rate without sacrificing efficiency, a shorter response time, an increased power density, a longer service life, a large capacity for supplying high/ intermittent energy demand to the grid, no maintenance requirements, an environmentally friendly design, and simple and low-cost operation [40]. The CES unit consists of a capacitor, a power conversion system (PCS), and associated protection circuits. Under normal working conditions, the CES unit stores energy and immediately releases it to the grid through the PCS when a sudden load demand occurs. Thus, the CES unit assists AGC in quickly regulating PS to the final equilibrium state. The CES unit has an energy efficiency of about 95% [41]. Certain losses occur due to the energy conversion mechanism, internal leakage and self-discharge. To ensure that the dynamic performance of the power system is improved, and to mitigate variations in frequency and interconnection power, the CES units are included in both regions of the power system models considered. The incremental change in CES unit power is denoted by Eq (4) [41].
where T 1 , T 2 , T 3 , and T are the phase adjustment blocks' time constants. K CES signifies the gain, while T CES is the CES unit's time constant and F denotes an area's frequency deviation signal.

B. MODELLING OF THYRISTOR CONTROLLED PHASE SHIFTER (TCPS)
TCPS is a promising FACTS controller that is widely used in contemporary PSs for transmission line series compensation level applications. It is used to increase the capability of a transmission line's power transfer by dampening the power fluctuations caused by local and inter-area fluctuations. The modeling of TCPS is shown in Figure 3. TCPS enhances the power grid's dependability and stability by enabling flexible power planning in a variety of (changing) operating scenarios. The TCPS unit maintains real power flow in tie-lines under adverse environments, relieves high-frequency irregularity, and regulates system voltage by altering their relative phase angles [16]. The additional power flow between the area-1 and area-2 tie-lines can be indicated by Eq (5) [16].
After inserting a TCPS component into the proposed model, the actual power interchange between areas 1 and 2 through tie-line can be stated as follows [16] P Actual tie12 = The perturbation in the power flow between the tie lines, as illustrated by Eq (7). where Additionally, we know that angular deviation can be expressed as By taking the Laplace Transform of (7), we obtain The phase shifter angle ( ) adjusts the tie-line power flow exchange and is given as follows. where the error signal is the change in frequency,K shows the stabilization gain, and T TCPS signifies the time constant of the TCPS component. Also Eq (11) can be written as:

III. CONTROLLER STRUCTURE AND OPTIMIZATION TECHNIQUES
Various integer and fractional order controllers are used for LFC in different power systems applications. In the literature, the PID has a wide range of applications in frequency stability difficulties. The PID controller's transfer function is written as follows: Although the derivative mode of a PID controller enhances system stability and boost controller reaction speed, it results in unreasonably large control inputs to the plant. Large plant input signals result from noise in the control signal, and these problems are common in real-world systems. This problem can be solved by adding a first filter to the derivative term and fine-tuning its pole so that the chattering caused by noise is minimized and its transfer function is given below [33]: The proportional part of a PIDN controller is replaced by a tilting component with a transfer function of S ( −1 n ) in a TIDN controller. The TIDN controller's output transfer function is closer to an optimal transfer function, resulting in a more effective feedback controller.
Surprisingly, despite these benefits, FOI-TD with derivative Filter (FOI-TDN) controller structures are not used for LFC problems. In light of the foregoing, this paper represents the first attempt to apply a FOI-TDN controller to the LFC of an IPS. The FOI-TDN and FOTIDN controllers contain the same amount of parameters but have a different structure, as illustrated in 4 (a) and (b) respectively. Eq (16)) and Eq (17) The objective function is first developed based on the desired requirements and constraints in the formulation of a modern heuristic optimization technique with the controller. In control design, performance criteria such as the integral of time multiplied absolute error, integral of squared error, integral of time multiplied squared error, and the integral of absolute error are frequently taken into account which is given as below respectively:

A. FITNESS DEPENDENT OPTIMIZER (FDO)
The FDO [42] has a unique approach for calculating velocity (pace). It utilizes a fitness function to generate appropriate weights, which aid the search agents in balancing exploration and exploitation. Using the upper and lower bounds, the FDO method assigns random solutions to the scout bee population. Scout bees use a fusion of a casual walk and a fitness weight manner to find hives. By adding pace to their existing position, the scout bees modify their position. The scout bees' movement is estimated as [42]: X k,t+1 denote the next location, while X k,t denotes the current location and P represent the pace. The fitness weight (F w ) is calculated according to Eq (30)) Furthermore, the following are the necessary conditions for F w [42].

B. SINE COSINE ALGORITHM (SCA)
The behavior of the sine cosine algorithm (SCA) in attaining best solutions is based on the cosine and sine functions. SCA generates several preliminary arbitrary solutions that vary VOLUME 10, 2022 towards or away from the best solutions [30]. For sine and cosine phases, the following position updating equations are proposed [30].
X t+1 i denote the next location, while X t i denotes the current location and q represent the destination points. Where r 1 denotes the next position regions, r 2 specifies how far the movement should be towards or away from the destination and r 3 generates destination weights at random.
where r 4 is a random number between 0 and 1. As shown in Eq (27)), r 4 alternates between the sine and cosine components.

C. HYBRID SINE COSINE WITH FITNESS DEPENDENT OPTIMIZER (HSC-FDO)
This algorithm was developed by Chan.C by partially integrating the algorithm SC into the FDO techniques to improve the performance of the original FDO in terms of search accuracy, convergence speed, and the balance between exploration and exploitation capabilities in the search space [43]. In this technique, four modifications are employed which is given as below: 1) Modified Pace-Updating Equation: in this section, the notion of improved pace updating is introduced to increase the speed of convergence and the balance of exploration and exploitation capabilities of the original FDO. The equation for modified pace-updating is given below [43]: 2) Global Fitness Weight Strategy and Random Weight Factor: a random weight component and a global fitness weight constraint are incorporated into the search process to improve the performance of the proposed SC-search FDO. To increase the convergence and superiority of the solutions, the SC-FDO algorithm includes a modified calculation of the fitness weight Fw, which is given as follows [43]:  3) Conversion Parameter Strategy Parameters: r 1 , r 2 , and r 3 in the improved pace apprising equation convert search from exploitation to exploration at promising locations. The following solution's region is determined by the parameter r 1 , as indicated in Eq (27)). The high r 1 value fosters global exploration, whereas a low r 1 value supports local exploitation in the direction of the destination. But, r 1 is linearly decreased from (a) to (0) to accomplish a balanced exploration and exploitation, and is stated as follows [43]: where β represents a constant, t represents the current iteration and t max represents the maximum iteration. The flow scheme for hybrid SC-FDO technique is given in Fig 6.

IV. VALIDATION PERFORMANCES
In this section, a multi-renewable resource with a two-area interconnected system assimilated with CES and TCPS depicted in Fig 1 is developed in Matlab/Simulink environment using Appendix via 1 % step load disturbance (SLD) at t = 0 s. An ITSE-based hybrid SC-FDO technique is employed to optimize the effectiveness of the newly developed FOI-TDN controller. The ITSE base suggested algorithm is favored due to its superior cost function values, as shown in Table 1. After 30 iterations and selecting the optimum values, the parameters of the hSC-FDO listed in Appendix are used to calculate the proposed controller's optimal gains, which are listed in Table 3. The performance of the LFC system of two area multi-generation units has been validated with respect to three scenarios in order to demonstrate its performance.

1) Scenario-1 (FOI-TDN based different algorithms)
In this case, the hSCA-FDO meta-heuristic algorithm  is compared to benchmark methodologies such as FDO, PSO, and FA to demonstrate the effectiveness of our suggested model. Fig 5 illustrates Fig 7 (a-c) using various methodologies for frequency deviation in area-a ( F a ), area-b ( F b ), and tie-line power deviations ( P tie ). As illustrated in Fig 7 (a-c), the FOI-TDN controllers based on hSCA-FDO optimization rapidly suppressed oscillation and reduced peak overshoot and undershoot for F a , F b , and P tie . Table 4 shows a detailed comparison of the results for F a , F b , and P tie for various methods in terms of Overshoot (Os), Undershoot (Us), and Settling time (Ts). In comparison to a FOI-TDN based PSO algorithms, hSC-FDO tuned FOI-TDN controller improved settlement time by 49.45%, 1.38%, and 4.53%, and effectively reduced overshoot by 87.21%, 49.71 %, and 10.56 % for F a , F b , and P tie . Table 3 shows that when compared to the Pathfinder algorithm-based FOTID controller, the hSC-FDO-based tuned FOI-TDN controller offers a significant increase of 78.43%, 37.10 %, and 83.89 % effectively reducing peak overshoot of 86.53%, 49.34%, and 87.33 % and undershoot of 35.45 %, 59.48 %, and 49.22 % for two areas and in tie-line power. In comparison to the WCA-based I-TD controller, the FOI-TDN based hSC-FDO controller improves time settling by 35.98%, 72.41%, and 14.11 % for F a , F b , and P tie respectively.

2) Scenario-2 (CES and TCPS)
The effect of introducing CES and TCPS units on LFC is investigated in scenario-2. Each area has a CES unit, and the system's tie-line takes TCPS into account. The system's performance is assessed using hSC-FDO-based FOI-TDN controller with the effects of CES, TCPS, both CES and TCPS, and without CES and TCPS. Fig 8( Fig 8(a-c). Furthermore, Table 5 shows that our proposed approach performs admirably when combined with CES and TCPS.

3) Scenario-3 (hSC-FDO based various controllers)
In this scenario, the performance of FOI-TDN controller tuned with hSc-FDO algorithm was compared to FOTID, FOPID, and PID controllers optimized with the similar methodology. Fig 9(a-c) and Table 6 demonstrate the findings achieved using the recommended strategies.      it can be observed that our developed controller outperforms PID algorithms and FOPID controllers tuned with hSC-FDO algorithms in terms of O sh , T s and U sh .
The two-area power system is depicted in 11 (a-c) for various load fluctuation considering area 1, area 2 and tieline power. The frequency fluctuation is evaluated at different loads and the mitigating ability of controllers is analyzed, VOLUME 10, 2022 A. Daraz et al.: Optimized FO Integral-Tilt Derivative Controller for Frequency Regulation   where hSC-FDO based FOI-TDN reveals its superiority in dealing with quick variations in load as compared to Other techniques, such as FDO, FA, and PSO which exhibit more oscillations.

A. SENSITIVITY ANALYSIS
A robustness analysis is required to evaluate the ability of the LFC controller to withstand uncertainty. In the test, the newly developed system including numerous constraints using the hSC-FDO approach and under the supervision of the controller FOI-TDN was targeted with various loadings such as 10% SLD on area-a only, 10% and 30% SLD on both areas. The responses for this test are shown in Fig 10(a-c) and indicate that the current control approach can handle the system performs well even under uncertain load. In addition, a sensitivity analysis of the system is also performed by varying the system parameters in a range of ±30% such as the wind time constant (T w ), droop constant (R), the governor constant (T g ), and the turbine constant (T t ). Table 7 shows a comparative study of various parameters in terms of undershooting, overshoot, and settling time with a change of ±30 % from the nominal values. Thus, even if the system load and parameters vary greatly, there is no need to change the parameters of the FOI-TDN controller. Therefore, the control technique presented here is robust.

V. CONCLUSION AND FUTURE WORK
This study designs and develops a FOI-TDN controller for the LFC of two areas, i.e., six-gen units with the inclusion of practical constraints such as the governor dead zone, communication time delay, boiler dynamics, and generation rate constraint. To tune the gains of the proposed controller, the hSC-FDO meta-heuristic algorithm is used. Moreover, the integration of CES in each area and TCPS in series with the interconnects improves the dynamic response of the system. Based on the simulation results, it was found that the tuned FOI-TDN controller based on hSC-FDO effectively reduces the overshoot (O sh ) by 49.31 %, the temporal control by 33.22 % and undershoot (U sh ) reduced by 29.13 %, 39.34 % and 56.90 % for the F a , F b , and P tie respectively, compared to the hSC-FDO tuned PID controller. Similarly, the HSC-FDO algorithm tuned controller (FOI-TDN) provides a significant improvement in the settling time for both the areas and tie-line power. It also effectively reduces the peak overshoots of 88.90 %, 78.33 %, and 49.13 % and the undershoots of 17.02 %, 69.67 %, and 83.10 % for F a , F b , and P tie , respectively, compared to the WCA-based tuned I-TD controller. Moreover, the dynamic response of the system is improved by including the effects of TCPS and CES in terms of Os, Ts, and Us, compared to not including the effects of TCPS and CES for F a , F b , and P tie . Finally, for the LFC problem, the capability of the designed FOI-TDN controller was tested by adjusting the system characteristics and load conditions in a range of ±30%. In the future, the research could be extended to modern networked realistic power system using more advanced optimization approaches.   He has worked as a Research Associate with Dr. Nadeem Javaid during his M.S. period at CUI. He has authored around 60 research publications in ISI-indexed international journals and conferences. His research interests include data analytics, generative adversarial networks, network security, wireless networks, smart grid, cloud computing, berth scheduling at maritime container terminal, se transportation, and intelligent shipping. He served/serving as a TPC member, a guest editor, an assistant editor, and an invited reviewer for international journals and conferences.

APPENDIX
TAMIM ALKHALIFAH received the master's degree in CS from Swansea University, in 2009, and the Ph.D. degree in computer science from Flinders University, Adelaide, Australia, in 2018. He is currently working as an Assistant Professor with the Computer Department, College of Science and Arts in Ar Rass, Qassim University, Saudi Arabia. He has published several papers in the IT field. His primary research interests include e-technologies, mobile development, mobile learning, and gamification.
FAHAD ALTURISE received the Ph.D. degree in information technology from Flinders University. He is currently working as an Associate Professor with the Computer Department, College of Science and Arts in Ar Rass, Qassim University, Saudi Arabia. He has an experience of 12 years in the field of teaching and research. He has published 12 articles in international journals/conference proceedings. His primary research interests include e-learning, e-services, e-government, the IoT, ICT adaption, IT security, and software engineering. He was a member of the Australian Computer Society (ACS) for four years. VOLUME 10, 2022