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Rapid advancements in the internet of things (IoT) are driving massive transformations of health care, which is one of the largest and critical global industries. Recent pandemics, such as coronavirus 2019 (COVID-19), include increasing demands for ubiquitous, preventive, and personalized health care to be provided to the public at reduced risks and costs with rapid care. Mobile crowdsourcing could potentially meet the future massive health care IoT (mH-IoT) demands by enabling anytime, anywhere sense and analyses of health-related data to tackle such a pandemic situation. However, data reliability and availability are among the many challenges for the realization of next-generation mH-IoT, especially in COVID-19 epidemics. Therefore, more intelligent and robust health care frameworks are required to tackle such pandemics. Recently, reinforcement learning (RL) has proven its strengths to provide intelligent data reliability and availability. The action-state learning procedure of RL-based frameworks enables the learning system to enhance the optimal use of the information as the time passes and data increases. In this article, we propose an RL-based crowd-to-machine (RLC2M) framework for mH-IoT, which leverages crowdsourcing and an RL model (Q-learning) to address the health care information processing challenges. The simulation results show that the proposed framework rapidly converges with accumulated rewards to reveal the sensing environment situation.
Alaa Omran Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. A Reinforcement Learning-Based Framework for Crowdsourcing in Massive Health Care Internet of Things. Big Data 2021, 1 .
AMA StyleAlaa Omran Almagrabi, Rashid Ali, Daniyal Alghazzawi, Abdullah AlBarakati, Tahir Khurshaid. A Reinforcement Learning-Based Framework for Crowdsourcing in Massive Health Care Internet of Things. Big Data. 2021; ():1.
Chicago/Turabian StyleAlaa Omran Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. 2021. "A Reinforcement Learning-Based Framework for Crowdsourcing in Massive Health Care Internet of Things." Big Data , no. : 1.
In this paper, a modified Rao-2 (MRao-2) algorithm is proposed to solve the problem of optimal power flow (OPF) in a power system incorporating renewable energy sources (RES). Quasi-oppositional and Levy flight methods are used to improve the performance of the Rao algorithm. To demonstrate effectiveness of the MRao-2 technique, it is tested on two standard test systems: an IEEE 30-bus system and an IEEE 118-bus system. The objective function of the OPF is the minimization of fuel cost in five scenarios. The IEEE 30-bus system reflects fuel cost minimization in three scenarios (without RES, with RES, and with RES under contingency state), while the IEEE 118-bus system reflects fuel cost minimization in two scenarios (without RES and with RES). The achieved results of various scenarios using the suggested MRao-2 technique are compared with those obtained using five recent techniques: Atom Search Optimization (ASO), Turbulent Flow of Water-based Optimization (TFWO), Marine Predators Algorithm (MPA), Rao-1, Rao-3 algorithms, as well as the conventional Rao-2 algorithm. Those comparisons confirm the superiority of the MRao-2 technique over those other algorithms in solving the OPF problem.
Mohamed Hassan; Salah Kamel; Ali Selim; Tahir Khurshaid; José Domínguez-García. A Modified Rao-2 Algorithm for Optimal Power Flow Incorporating Renewable Energy Sources. Mathematics 2021, 9, 1532 .
AMA StyleMohamed Hassan, Salah Kamel, Ali Selim, Tahir Khurshaid, José Domínguez-García. A Modified Rao-2 Algorithm for Optimal Power Flow Incorporating Renewable Energy Sources. Mathematics. 2021; 9 (13):1532.
Chicago/Turabian StyleMohamed Hassan; Salah Kamel; Ali Selim; Tahir Khurshaid; José Domínguez-García. 2021. "A Modified Rao-2 Algorithm for Optimal Power Flow Incorporating Renewable Energy Sources." Mathematics 9, no. 13: 1532.
Clean energy resources have become a worldwide concern, especially photovoltaic (PV) energy. Solar cell modeling is considered one of the most important issues in this field. In this article, an improvement for the search steps of the bald eagle search algorithm is proposed. The improved bald eagle search (IBES) was applied to estimate more accurate PV model parameters. The IBES algorithm was applied for conventional single, double, and triple PV models, in addition to modified single, double, and triple PV models. The IBES was evaluated by comparing its results with the original BES through 15 benchmark functions. For a more comprehensive analysis, two evaluation tasks were performed. In the first task, the IBES results were compared with the original BES for parameter estimation of original and modified tribe diode models. In the second task, the IBES results were compared with different recent algorithms for parameter estimation of original and modified single and double diode models. All tasks were performed using the real data for a commercial silicon solar cell (R.T.C. France). From the results, it can be concluded that the results of the modified models were more accurate than the conventional PV models, and the IBES behavior was better than the original BES and other compared algorithms.
Abdelhady Ramadan; Salah Kamel; Mohamed Hassan; Tahir Khurshaid; Claudia Rahmann. An Improved Bald Eagle Search Algorithm for Parameter Estimation of Different Photovoltaic Models. Processes 2021, 9, 1127 .
AMA StyleAbdelhady Ramadan, Salah Kamel, Mohamed Hassan, Tahir Khurshaid, Claudia Rahmann. An Improved Bald Eagle Search Algorithm for Parameter Estimation of Different Photovoltaic Models. Processes. 2021; 9 (7):1127.
Chicago/Turabian StyleAbdelhady Ramadan; Salah Kamel; Mohamed Hassan; Tahir Khurshaid; Claudia Rahmann. 2021. "An Improved Bald Eagle Search Algorithm for Parameter Estimation of Different Photovoltaic Models." Processes 9, no. 7: 1127.
The enhancement of photovoltaic (PV) energy systems relies on an accurate PV model. Researchers have made significant efforts to extract PV parameters due to their nonlinear characteristics of the PV system, and the lake information from the manufactures’ PV system datasheet. PV parameters estimation using optimization algorithms is a challenging problem in which a wide range of research has been conducted. The idea behind this challenge is the selection of a proper PV model and algorithm to estimate the accurate parameters of this model. In this paper, a new application of the improved gray wolf optimizer (I-GWO) is proposed to estimate the parameters’ values that achieve an accurate PV three diode model (TDM) in a perfect and robust manner. The PV TDM is developed to represent the effect of grain boundaries and large leakage current in the PV system. I-GWO is developed with the aim of improving population, exploration and exploitation balance and convergence of the original GWO. The performance of I-GWO is compared with other well-known optimization algorithms. I-GWO is evaluated through two different applications. In the first application, the real data from RTC furnace is applied and in the second one, the real data of PTW polycrystalline PV panel is applied. The results are compared with different evaluation factors (root mean square error (RMSE), current absolute error and statistical analysis for multiple independent runs). I-GWO achieved the lowest RMSE values in comparison with other algorithms. The RMSE values for the two applications are 0.00098331 and 0.0024276, respectively. Based on quantitative and qualitative performance evaluation, it can be concluded that the estimated parameters of TDM by I-GWO are more accurate than those obtained by other studied optimization algorithms.
Abd-Elhady Ramadan; Salah Kamel; Tahir Khurshaid; Seung-Ryle Oh; Sang-Bong Rhee. Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer. Sustainability 2021, 13, 6963 .
AMA StyleAbd-Elhady Ramadan, Salah Kamel, Tahir Khurshaid, Seung-Ryle Oh, Sang-Bong Rhee. Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer. Sustainability. 2021; 13 (12):6963.
Chicago/Turabian StyleAbd-Elhady Ramadan; Salah Kamel; Tahir Khurshaid; Seung-Ryle Oh; Sang-Bong Rhee. 2021. "Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer." Sustainability 13, no. 12: 6963.
Internet of Things (IoT) and Named Data Network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet Protocol (IP) based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this paper, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.
Muhammad Ali Naeem; Tu N. Nguyen; Rashid Ali; Korhan Cengiz; Yahui Meng; Tahir Khurshaid. Hybrid Cache Management in IoT-based Named Data Networking. IEEE Internet of Things Journal 2021, PP, 1 -1.
AMA StyleMuhammad Ali Naeem, Tu N. Nguyen, Rashid Ali, Korhan Cengiz, Yahui Meng, Tahir Khurshaid. Hybrid Cache Management in IoT-based Named Data Networking. IEEE Internet of Things Journal. 2021; PP (99):1-1.
Chicago/Turabian StyleMuhammad Ali Naeem; Tu N. Nguyen; Rashid Ali; Korhan Cengiz; Yahui Meng; Tahir Khurshaid. 2021. "Hybrid Cache Management in IoT-based Named Data Networking." IEEE Internet of Things Journal PP, no. 99: 1-1.
The optimal location of renewable distributed generations (DGs) into a radial distribution system (RDS) has attracted major concerns from power system researchers in the present years. The main target of DG integration is to improve the overall system performance by minimizing power losses and improving the voltage profile. Hence, this paper proposed a hybrid approach between an analytical and metaheuristic optimization technique for the optimal allocation of DG in RDS, considering different types of load. A simple analytical technique was developed in order to determine the sizes of different and multiple DGs, and a new efficient metaheuristic technique known as the Salp Swarm Algorithm (SSA) was suggested in order to choose the best buses in the system, proportionate to the sizes determined by the analytical technique, in order to obtain the minimum losses and the best voltage profile. To verify the power of the proposed hybrid technique on the incorporation of the DGs in RDS, it was applied to different types of static loads; constant power (CP), constant impedance (CZ), and constant current (CI). The performance of the proposed algorithm was validated using two standards RDSs—IEEE 33-bus and IEEE 69-bus systems—and was compared with other optimization techniques.
Amal Mohamed; Salah Kamel; Ali Selim; Tahir Khurshaid; Sang-Bong Rhee. Developing a Hybrid Approach Based on Analytical and Metaheuristic Optimization Algorithms for the Optimization of Renewable DG Allocation Considering Various Types of Loads. Sustainability 2021, 13, 4447 .
AMA StyleAmal Mohamed, Salah Kamel, Ali Selim, Tahir Khurshaid, Sang-Bong Rhee. Developing a Hybrid Approach Based on Analytical and Metaheuristic Optimization Algorithms for the Optimization of Renewable DG Allocation Considering Various Types of Loads. Sustainability. 2021; 13 (8):4447.
Chicago/Turabian StyleAmal Mohamed; Salah Kamel; Ali Selim; Tahir Khurshaid; Sang-Bong Rhee. 2021. "Developing a Hybrid Approach Based on Analytical and Metaheuristic Optimization Algorithms for the Optimization of Renewable DG Allocation Considering Various Types of Loads." Sustainability 13, no. 8: 4447.
In this paper, Chaotic Artificial Ecosystem-based Optimization Algorithm (CAEO) is proposed and utilized to determine the optimal solution which achieves the economical operation of the electrical power system and reducing the environmental pollution produced by the conventional power generation. Here, the Combined Economic Emission Dispatch (CEED) problem is represented using a max/max Price Penalty Factor (PPF) to confine the system’s nonlinearity. PPF is considered to transform a four-objective problem into a single-objective optimization problem. The proposed modification of AEO raises the effectiveness of the populations to achieve the best fitness solution by well-known 10 chaotic functions and this is valuable in both cases of the single and multi-objective functions. The CAEO algorithm is used for minimizing the economic load dispatch and the three bad gas emissions which are sulfur dioxide (SO2), nitrous oxide (NOx), and carbon dioxide (CO2). To evaluate the proposed CAEO, it is utilized for four different levels of demand in a 6-unit power generation (30-bus test system) and 11-unit power generation (69-bus test system) with a different value of load demand (1000, 1500, 2000, and 2500MW). Statistical analysis is executed to estimate the reliability and stability of the proposed CAEO method. The results obtained by CAEO algorithm are compared with other methods and conventional AEO to prove that the modification is to boost the search strength of conventional AEO. The results display that the CAEO algorithm is superior to the conventional AEO and the others in achieving the best solution to the problem of CEED in terms of efficient results, strength, and computational capability all over study cases. In the second scenario of the bi-objective problem, the Pareto theory is integrated with a CAEO to get a series of Non-Dominated (ND) solutions, and then using the fuzzy approach to determine BCS.
Mohamed H. Hassan; Salah Kamel; Sinan Q. Salih; Tahir Khurshaid; Mohamed Ebeed. Developing Chaotic Artificial Ecosystem-based Optimization Algorithm for Combined Economic Emission Dispatch. IEEE Access 2021, PP, 1 -1.
AMA StyleMohamed H. Hassan, Salah Kamel, Sinan Q. Salih, Tahir Khurshaid, Mohamed Ebeed. Developing Chaotic Artificial Ecosystem-based Optimization Algorithm for Combined Economic Emission Dispatch. IEEE Access. 2021; PP (99):1-1.
Chicago/Turabian StyleMohamed H. Hassan; Salah Kamel; Sinan Q. Salih; Tahir Khurshaid; Mohamed Ebeed. 2021. "Developing Chaotic Artificial Ecosystem-based Optimization Algorithm for Combined Economic Emission Dispatch." IEEE Access PP, no. 99: 1-1.
The 5th generation (5G) wireless networks propose to address a variety of usage scenarios, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Due to the exponential increase in the user equipment (UE) devices of wireless communication technologies, 5G and beyond networks (B5G) expect to support far higher user density and far lower latency than currently deployed cellular technologies, like long-term evolution-Advanced (LTE-A). However, one of the critical challenges for B5G is finding a clever way for various channel access mechanisms to maintain dense UE deployments. Random access channel (RACH) is a mandatory procedure for the UEs to connect with the evolved node B (eNB). The performance of the RACH directly affects the performance of the entire network. Currently, RACH uses a uniform distribution-based (UD) random access to prevent a possible network collision among multiple UEs attempting to access channel resources. However, in a UD-based channel access, every UE has an equal chance to choose a similar contention preamble close to the expected value, which causes an increase in the collision among the UEs. Therefore, in this paper, we propose a Poisson process-based RACH (2PRACH) alternative to a UD-based RACH. A Poisson process-based distribution, such as exponential distribution, disperses the random preambles between two bounds in a Poisson point method, where random variables occur continuously and independently with a constant parametric rate. In this way, our proposed 2PRACH approach distributes the UEs in a probability distribution of a parametric collection. Simulation results show that the shift of RACH from UD-based channel access to a Poisson process-based distribution enhances the reliability and lowers the network’s latency.
Alaa Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. A Poisson Process-Based Random Access Channel for 5G and Beyond Networks. Mathematics 2021, 9, 508 .
AMA StyleAlaa Almagrabi, Rashid Ali, Daniyal Alghazzawi, Abdullah AlBarakati, Tahir Khurshaid. A Poisson Process-Based Random Access Channel for 5G and Beyond Networks. Mathematics. 2021; 9 (5):508.
Chicago/Turabian StyleAlaa Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. 2021. "A Poisson Process-Based Random Access Channel for 5G and Beyond Networks." Mathematics 9, no. 5: 508.
The appropriate control and management of reactive power is of great relevance in the electrical reliability, stability, and security of power grids. This issue is considered in order to increase system efficiency and to maintain voltage under the acceptable value range. In this regard, novel technologies as FACTS, renewable energies, among others, are varying conventional grid behavior leading to unexpected limit capacity reaching due to large reactive power flow. Thus, optimal planning of this must be considered. This paper proposes a new application for a simple and easy implementation optimization algorithm, called Rao-3, to solve the constrained non-linear optimal reactive power dispatch problem. Moreover, the integration of solar and wind energy as the most applied technologies in electric power systems are exploited. Due to the continuous variation and the natural intermittence of wind speed and solar irradiance as well as load demand fluctuation, the uncertainties which have a global concern are investigated and considered in this paper. The proposed single-objective and multi-objective deterministic/stochastic optimal reactive power dispatch algorithms are validated using three standard test power systems, namely IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus. The simulation results show that the proposed optimal reactive power dispatch algorithms are superior compared with two recent algorithms (Artificial electric field algorithm (AEFA) and artificial Jellyfish Search (JS) algorithm) and other optimization algorithms used for solving the same problem.
Mohamed H. Hassan; Salah Kamel; Mahmoud A. El-Dabah; Tahir Khurshaid; Jose Luis Dominguez-Garcia. Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm. IEEE Access 2021, 9, 23264 -23283.
AMA StyleMohamed H. Hassan, Salah Kamel, Mahmoud A. El-Dabah, Tahir Khurshaid, Jose Luis Dominguez-Garcia. Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm. IEEE Access. 2021; 9 ():23264-23283.
Chicago/Turabian StyleMohamed H. Hassan; Salah Kamel; Mahmoud A. El-Dabah; Tahir Khurshaid; Jose Luis Dominguez-Garcia. 2021. "Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm." IEEE Access 9, no. : 23264-23283.
The Alternating Current-Direct Current (AC-DC) hybrid distribution network has received attention in recent years. Due to advancement in technologies such as the integration of renewable energy resources of DC–type output and usage of DC loads in the distribution network, the modern distribution system can meet the increasing energy demand with improved efficiency. In this paper, a new AC-DC hybrid distribution network architecture is analyzed that considers distributed energy resources (DER) in the network. A network reconfiguration scheme is proposed that uses the AC soft open point (AC-SOP) and the DC soft open point (DC-SOP) along with an SOP selection algorithm for minimizing the network power losses. Subsequently, the real-time data for DER and load/demand variation are considered for a day-a-head scenario for the verification of the effectiveness of the network reconfiguration scheme. The results show that the proposed network reconfiguration scheme using AC-SOP and DC-SOP can successfully minimize the network power losses by modifying the network configuration. Finally, the effectiveness of the proposed scheme in minimizing the network power losses by the upgraded network configuration is verified by constructing an AC-DC hybrid distribution network by combining two IEEE 33-bus distribution networks.
Muhammad Omer Khan; Abdul Wadood; Muhammad Irfan Abid; Tahir Khurshaid; Sang Bong Rhee. Minimization of Network Power Losses in the AC-DC Hybrid Distribution Network through Network Reconfiguration Using Soft Open Point. Electronics 2021, 10, 326 .
AMA StyleMuhammad Omer Khan, Abdul Wadood, Muhammad Irfan Abid, Tahir Khurshaid, Sang Bong Rhee. Minimization of Network Power Losses in the AC-DC Hybrid Distribution Network through Network Reconfiguration Using Soft Open Point. Electronics. 2021; 10 (3):326.
Chicago/Turabian StyleMuhammad Omer Khan; Abdul Wadood; Muhammad Irfan Abid; Tahir Khurshaid; Sang Bong Rhee. 2021. "Minimization of Network Power Losses in the AC-DC Hybrid Distribution Network through Network Reconfiguration Using Soft Open Point." Electronics 10, no. 3: 326.
In this paper, a new application of Equilibrium Optimizer (EO) is proposed for design hybrid microgrid to feed the electricity to Dakhla, Morocco, as an isolated area. EO is selected to design the microgrid system due to its high effectiveness in determining the optimal solution in very short time. EO is presented for selecting the optimal system design which can minimize the cost, improve the system stability, and cover the load at different climate conditions. Microgrid system consists of photovoltaic (PV), wind turbine (WT), battery, and diesel generator. The objective function treated in this paper is to minimize the net present cost (NPC), respecting several constraints such as the reliability, availability, and renewable fraction. The sensitivity analysis is conducted in two stages: Firstly, the impact of wind speed, solar radiation, interest rate, and diesel fuel on the NPC, and levelized cost of energy (LCOE) is analyzed. Secondly, the influence of size variation on loss of power supply probability (LPSP) is investigated. The results obtained by EO are compared with those obtained by recent metaheuristics optimization algorithms, namely, Harris Hawks Optimizer (HHO), Artificial Electric Field Algorithm (AEFA), Grey Wolf Optimizer (GWO), and Sooty Tern Optimization Algorithm (STOA). The results show that the optimal system design is achieved by the proposed EO, where renewable energy sources (PV and WT) represent 97% of the annual contribution and fast convergence characteristics are obtained by EO. The best NPC, LCOE, and LPSP are obtained via EO achieving 74327 $, 0.0917 $/kWh, and 0.0489, respectively.
Mohammed Kharrich; Salah Kamel; Mohamed Abdeen; Omar Hazem Mohammed; Mohammed Akherraz; Tahir Khurshaid; Sang-Bong Rhee. Developed Approach Based on Equilibrium Optimizer for Optimal Design of Hybrid PV/Wind/Diesel/Battery Microgrid in Dakhla, Morocco. IEEE Access 2021, 9, 13655 -13670.
AMA StyleMohammed Kharrich, Salah Kamel, Mohamed Abdeen, Omar Hazem Mohammed, Mohammed Akherraz, Tahir Khurshaid, Sang-Bong Rhee. Developed Approach Based on Equilibrium Optimizer for Optimal Design of Hybrid PV/Wind/Diesel/Battery Microgrid in Dakhla, Morocco. IEEE Access. 2021; 9 (99):13655-13670.
Chicago/Turabian StyleMohammed Kharrich; Salah Kamel; Mohamed Abdeen; Omar Hazem Mohammed; Mohammed Akherraz; Tahir Khurshaid; Sang-Bong Rhee. 2021. "Developed Approach Based on Equilibrium Optimizer for Optimal Design of Hybrid PV/Wind/Diesel/Battery Microgrid in Dakhla, Morocco." IEEE Access 9, no. 99: 13655-13670.
Alaa Omran Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things. Computers, Materials & Continua 2021, 68, 359 -376.
AMA StyleAlaa Omran Almagrabi, Rashid Ali, Daniyal Alghazzawi, Abdullah AlBarakati, Tahir Khurshaid. Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things. Computers, Materials & Continua. 2021; 68 (1):359-376.
Chicago/Turabian StyleAlaa Omran Almagrabi; Rashid Ali; Daniyal Alghazzawi; Abdullah AlBarakati; Tahir Khurshaid. 2021. "Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things." Computers, Materials & Continua 68, no. 1: 359-376.
Renewable energy resources like solar energy, wind energy, hydro energy, photovoltaic etc. are gaining much importance due to the day by day depletion of conventional resources. Owing to the lower efficiencies of renewable energy resources, much attention has been paid to improving them. The concept of utilizing phase change materials (PCMs) has attracted wide attention in recent years. This is due to their ability to extract thermal energy when used in collaboration with photovoltaic (PV), thus improving the photoelectric conversion efficiency. In this paper, the objective is to design and fabricate a novel thermal energy storage system using phase change material. An investigation on the characteristics of Potash Alum as a phase change material due to its low cost, easy availability and its usage as an energy storage for the indoor purposes are taken into account. The use of a latent heat storage system using phase change materials (PCMs) is an effective way of storing thermal energy and has the advantage of high-energy storage density and the isothermal nature of the storage process. In the current study, potash alum was identified as a phase change material combined with renewable energy sources, that can be efficiently and effectively used in storing thermal energy at compartively lower temperatures that can later be used in daily life heating requirements.A parabolic dish which acts of a heat collector is used to track and reflects solar radiation at a single point on a receiver tank. Heat transfer from the solar collector to the storage tank is done by using a circulating heat transfer fluid with the help of a pump. The experimental results show that this system is capable of successfully storing and utilizing thermal energy on indoor scale such as cooking, heating and those applications where temperature is below 92 °C.
Muhammad Suleman Malik; Naveed Iftikhar; Abdul Wadood; Muhammad Omer Khan; Muhammad Usman Asghar; Shahbaz Khan; Tahir Khurshaid; Ki-Chai Kim; Zabdur Rehman; S. Tauqeer Ul Islam Rizvi. Design and Fabrication of Solar Thermal Energy Storage System Using Potash Alum as a PCM. Energies 2020, 13, 6169 .
AMA StyleMuhammad Suleman Malik, Naveed Iftikhar, Abdul Wadood, Muhammad Omer Khan, Muhammad Usman Asghar, Shahbaz Khan, Tahir Khurshaid, Ki-Chai Kim, Zabdur Rehman, S. Tauqeer Ul Islam Rizvi. Design and Fabrication of Solar Thermal Energy Storage System Using Potash Alum as a PCM. Energies. 2020; 13 (23):6169.
Chicago/Turabian StyleMuhammad Suleman Malik; Naveed Iftikhar; Abdul Wadood; Muhammad Omer Khan; Muhammad Usman Asghar; Shahbaz Khan; Tahir Khurshaid; Ki-Chai Kim; Zabdur Rehman; S. Tauqeer Ul Islam Rizvi. 2020. "Design and Fabrication of Solar Thermal Energy Storage System Using Potash Alum as a PCM." Energies 13, no. 23: 6169.
Recently, solar photovoltaic (PV) is becoming widespread overall the world as it is a renewable free source of energy. PV alone is considered as a non-dispatchable source as it relies on variable source during the day. Therefore, the primary goal of this paper is to integrate the battery energy storage (BES) with PV as a dispatchable source in radial distribution system (RDS). In addition, this paper proposes a modified version of Henry gas solubility optimization algorithm (modified HGSO) to improve the performance of the conventional HGSO algorithm. This modified version is created by inserting the simulated annealing (SA) algorithm into the conventional HGSO algorithm. The proposed algorithm is used to determine the best size of PV and BES, considering the probabilistic of PV generation and time-varying load, in order to minimize the total system power loss. IEEE 69-bus RDS is used to demonstrate the effectiveness of proposed algorithm. The results show that integration of PV and BES in RDS reduces the system power loss, enhances the system voltage and increases the system capacity. The results also prove that the proposed algorithm is highly effective in integrating multiple PV and BES units in distribution system compared with the conventional algorithms.
H. Abdel-Mawgoud; Salah Kamel; Mansur Khasanov; Tahir Khurshaid. A strategy for PV and BESS allocation considering uncertainty based on a modified Henry gas solubility optimizer. Electric Power Systems Research 2020, 191, 106886 .
AMA StyleH. Abdel-Mawgoud, Salah Kamel, Mansur Khasanov, Tahir Khurshaid. A strategy for PV and BESS allocation considering uncertainty based on a modified Henry gas solubility optimizer. Electric Power Systems Research. 2020; 191 ():106886.
Chicago/Turabian StyleH. Abdel-Mawgoud; Salah Kamel; Mansur Khasanov; Tahir Khurshaid. 2020. "A strategy for PV and BESS allocation considering uncertainty based on a modified Henry gas solubility optimizer." Electric Power Systems Research 191, no. : 106886.
Optimal allocation of distributed generations (DGs) is vital to the proper operation of the distribution systems, which leads to power loss minimization and acceptable voltage regulation. In this paper, an Enhanced Artificial Ecosystem-based Optimization (EAEO) algorithm is proposed and used to solve the optimization problem of DG allocations to minimize the power loss in distribution systems. In the suggested algorithm, the search space is reduced using operator G and sine-cosine function. The G-operator affects the balance between explorative and exploitative phases. At the same time, it gradually decreases during the iterative process in order to converge to the optimal global solutions. On the other hand, the sine-cosine function creates different and random solutions. The EAEO algorithm is applied for solving the standard 33-bus 69-bus, and 119-bus distribution systems with the aim of minimizing the total power losses. Multiple DG units operating at various power factors, including unity-, fixed-, and optimal-power factors, are considered. Both single and multiple objectives are considered to minimize the total voltage deviation (TVD), maximize the system stability, and reduce the total power losses. The obtained results are compared with those obtained by the AEO and other algorithms. The results demonstrate a significant reduction of total power losses and improvement of the voltage profile of the network, especially for the DGs operating at their optimal power factors. Comparisons show the dominance of the proposed EAEO algorithm against other analytical, metaheuristic, or hybrid algorithms. Moreover, the EAEO outperforms the original AEO algorithm with a faster convergence speed and better system performance.
Ahmad Eid; Salah Kamel; Ahmed Korashy; Tahir Khurshaid. An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access 2020, 8, 178493 -178513.
AMA StyleAhmad Eid, Salah Kamel, Ahmed Korashy, Tahir Khurshaid. An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access. 2020; 8 (99):178493-178513.
Chicago/Turabian StyleAhmad Eid; Salah Kamel; Ahmed Korashy; Tahir Khurshaid. 2020. "An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations." IEEE Access 8, no. 99: 178493-178513.
To ensure a safe and trustworthy pattern in contradiction to the possible faults, a precise, reliable, and fast relaying strategy is of high importance in an electrical power system. These challenges give the impression of being more refined in multi-loop distribution systems. More recently, overcurrent relays (OCRs) have evolved as proficient counteragents for such cases. In this way, inaugurating an optimal protection coordination strategy is accepted as the primary precondition in guaranteeing the safe protection of the coordination strategy. This study is aimed at lessening the overall operational time of the main relays in order to reduce the power outages. The coordination problem is conducted by adjusting only one parameter, namely the time multiplier setting (TMS). In electrical power relaying coordination, the objective function to be minimized is the sum of the overall operational time of the main relays. In the prescribed work, the coordination of the OCRs in the single- and multi-loop distribution network is realized as an optimization issue. The optimization is accomplished by means of JAYA algorithm. The suggested technique depends on the idea that the result acquired for a certain issue ought to pass near the finest result and avert the worst result. This technique involves only the common control factors and does not involve specific control factors. JAYA is adopted to OCR problem and run 20 times with the same initial condition for each case study, and it has been realized that for every run, the JAYA algorithm converges to the global optimum values requiring less number of iterations and computational time. The results obtained from JAYA algorithm are compared with other evolutionary and up-to-date algorithms, and it was determined that JAYA outperforms the other techniques.
Abdul Wadood; Saeid Gholami Farkoush; Tahir Khurshaid; Jiang-Tao Yu; Chang-Hwan Kim; Sang-Bong Rhee. Application of the JAYA Algorithm in Solving the Problem of the Optimal Coordination of Overcurrent Relays in Single- and Multi-Loop Distribution Systems. Complexity 2019, 2019, 1 -13.
AMA StyleAbdul Wadood, Saeid Gholami Farkoush, Tahir Khurshaid, Jiang-Tao Yu, Chang-Hwan Kim, Sang-Bong Rhee. Application of the JAYA Algorithm in Solving the Problem of the Optimal Coordination of Overcurrent Relays in Single- and Multi-Loop Distribution Systems. Complexity. 2019; 2019 ():1-13.
Chicago/Turabian StyleAbdul Wadood; Saeid Gholami Farkoush; Tahir Khurshaid; Jiang-Tao Yu; Chang-Hwan Kim; Sang-Bong Rhee. 2019. "Application of the JAYA Algorithm in Solving the Problem of the Optimal Coordination of Overcurrent Relays in Single- and Multi-Loop Distribution Systems." Complexity 2019, no. : 1-13.
The directional overcurrent relays (DOCRs) coordination is a useful tool in guaranteeing the safe protection of the power system by the proper coordination of primary and backup protection systems. The optimization model of this problem is non-linear and highly constrained. The main objective of this paper is to develop a hybridized version of the Whale optimization algorithm referred to as HWOA for the optimal coordination of the DOCRs. The hybridization is done by deploying the simulated annealing (SA) in the WOA algorithm in order to improve the best solution found after each iteration and enhance the exploitation by searching the most promising regions located by the WOA algorithm, which leads toward a globally optimum solution. The proposed algorithm has been applied to five test systems, including the IEEE 3-bus, 8-bus, 9-bus, 15-bus, and 30-bus test systems. Furthermore, the results obtained using the proposed HWOA are compared with those obtained using the traditional WOA and a number of up-to-date algorithms. The obtained results show the effectiveness of the proposed HWOA in minimizing the relay operating time for the optimal coordination of the DOCRs.
Tahir Khurshaid; Abdul Wadood; Saeid Gholami Farkoush; Jiangtao Yu; Chang-Hwan Kim; Sang-Bong Rhee. An Improved Optimal Solution for the Directional Overcurrent Relays Coordination Using Hybridized Whale Optimization Algorithm in Complex Power Systems. IEEE Access 2019, 7, 90418 -90435.
AMA StyleTahir Khurshaid, Abdul Wadood, Saeid Gholami Farkoush, Jiangtao Yu, Chang-Hwan Kim, Sang-Bong Rhee. An Improved Optimal Solution for the Directional Overcurrent Relays Coordination Using Hybridized Whale Optimization Algorithm in Complex Power Systems. IEEE Access. 2019; 7 ():90418-90435.
Chicago/Turabian StyleTahir Khurshaid; Abdul Wadood; Saeid Gholami Farkoush; Jiangtao Yu; Chang-Hwan Kim; Sang-Bong Rhee. 2019. "An Improved Optimal Solution for the Directional Overcurrent Relays Coordination Using Hybridized Whale Optimization Algorithm in Complex Power Systems." IEEE Access 7, no. : 90418-90435.
This paper discusses the step and touch voltages, based on body resistance, in a grounding grid after a lightning strike at a 434/21 kV substation. To ensure grounding grid safety, the maximum step and touch voltage should not exceed the safety criteria defined by IEEE Std. 80. In this paper, ATP-EMTP and a genetic algorithm are used to analyze and optimize the step and touch voltages in a power grid. The voltages are calculated under normal conditions of a lightning strike at a power system. Thevenin’s theorem is applied to calculate the step and touch voltages. A genetic algorithm is applied in ATP-EMTP to obtain the minimum level of step and touch voltages in the grounding grid after lightning strikes the power system. The step and touch voltages at different positions of the grounding grid are explained in this paper using ATP-EMTP and a genetic algorithm. The computer simulation shows that the proposed scheme is highly feasible and technically attractive.
Saeid Gholami Farkoush; Abdul Wadood; Tahir Khurshaid; Chang-Hwan Kim; Muhammad Irfan; Sang-Bong Rhee. Reducing the Effect of Lightning on Step and Touch Voltages in a Grounding Grid Using a Nature-Inspired Genetic Algorithm With ATP-EMTP. IEEE Access 2019, 7, 81903 -81910.
AMA StyleSaeid Gholami Farkoush, Abdul Wadood, Tahir Khurshaid, Chang-Hwan Kim, Muhammad Irfan, Sang-Bong Rhee. Reducing the Effect of Lightning on Step and Touch Voltages in a Grounding Grid Using a Nature-Inspired Genetic Algorithm With ATP-EMTP. IEEE Access. 2019; 7 (99):81903-81910.
Chicago/Turabian StyleSaeid Gholami Farkoush; Abdul Wadood; Tahir Khurshaid; Chang-Hwan Kim; Muhammad Irfan; Sang-Bong Rhee. 2019. "Reducing the Effect of Lightning on Step and Touch Voltages in a Grounding Grid Using a Nature-Inspired Genetic Algorithm With ATP-EMTP." IEEE Access 7, no. 99: 81903-81910.
In power systems protection, the optimal coordination of directional overcurrent relays (DOCRs) is of paramount importance. The coordination of DOCRs in a multi-loop power system is formulated as an optimization problem. The main objective of this paper is to develop the whale optimization algorithm (WOA) for the optimal coordination of DOCRs and minimize the sum of the operating times of all primary relays. The WOA is inspired by the bubble-net hunting strategy of humpback whales which leads toward global minima. The proposed algorithm has been applied to six IEEE test systems including the IEEE three-bus, eight-bus, nine-bus, 14-bus, 15-bus, and 30-bus test systems. Furthermore, the results obtained using the proposed WOA are compared with those obtained by other up-to-date algorithms. The obtained results show the effectiveness of the proposed WOA to minimize the relay operating time for the optimal coordination of DOCRs.
Abdul Wadood; Tahir Khurshaid; Saeid GholamiFarkoush; Jiangtao Yu; Chang-Hwan Kim; Sang-Bong Rhee. Nature-Inspired Whale Optimization Algorithm for Optimal Coordination of Directional Overcurrent Relays in Power Systems. Energies 2019, 12, 2297 .
AMA StyleAbdul Wadood, Tahir Khurshaid, Saeid GholamiFarkoush, Jiangtao Yu, Chang-Hwan Kim, Sang-Bong Rhee. Nature-Inspired Whale Optimization Algorithm for Optimal Coordination of Directional Overcurrent Relays in Power Systems. Energies. 2019; 12 (12):2297.
Chicago/Turabian StyleAbdul Wadood; Tahir Khurshaid; Saeid GholamiFarkoush; Jiangtao Yu; Chang-Hwan Kim; Sang-Bong Rhee. 2019. "Nature-Inspired Whale Optimization Algorithm for Optimal Coordination of Directional Overcurrent Relays in Power Systems." Energies 12, no. 12: 2297.
In an electrical power network linear and non-linear models are used for directional overcurrent relay (DOCR) coordination issue by applying different heuristic techniques. Nature inspired algorithms (NIA) have found great interest in power system optimization issues. This article proposes the recently developed meta-heuristic technique known as Firefly Algorithm (FA) that mimics the flashing behavior of fireflies. The implementation of the proposed algorithm has been utilized to solve the coordination of Directional Over-current Relay (DOCR) problems. The main aim of this work is to find out the optimum values of the Time Dial Setting (TDS) to minimize the relay operating time. The modifications to original FA has been implemented in this paper to solve the DOCR coordination issues. Self-adaptive weight and experience-based learning strategy are added in the original FA, named as improved firefly algorithm (IFA). In IFA, a self-adaptive weight is presented to change the propensity of moving the best solution and ignoring the worst solution. In addition, an experience-based learning system is created and utilized arbitrarily to keep up the populace-assorted variety and improve the exploration capacity. The IFA has been tested on IEEE 6 and 30-bus systems and tested on IEEE 9-bus system for numerical DOCRs and the results had been verified by a comparative study with other optimization techniques. The obtained results show that the IFA provides efficient and promising results compared to other meta-heuristic techniques mentioned in the literature. The IFA has been successfully implemented on MATLAB software programming.
Tahir Khurshaid; Abdul Wadood; Saeid Gholami Farkoush; Chang-Hwan Kim; Jiangtao Yu; Sang-Bong Rhee. Improved Firefly Algorithm for the Optimal Coordination of Directional Overcurrent Relays. IEEE Access 2019, 7, 78503 -78514.
AMA StyleTahir Khurshaid, Abdul Wadood, Saeid Gholami Farkoush, Chang-Hwan Kim, Jiangtao Yu, Sang-Bong Rhee. Improved Firefly Algorithm for the Optimal Coordination of Directional Overcurrent Relays. IEEE Access. 2019; 7 (99):78503-78514.
Chicago/Turabian StyleTahir Khurshaid; Abdul Wadood; Saeid Gholami Farkoush; Chang-Hwan Kim; Jiangtao Yu; Sang-Bong Rhee. 2019. "Improved Firefly Algorithm for the Optimal Coordination of Directional Overcurrent Relays." IEEE Access 7, no. 99: 78503-78514.