An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification

Houssein, Essam H.; Helmy, Bahaa El-din; Rezk, Hegazy; Nassef, Ahmed M.

doi:10.1016/j.engappai.2021.104309

Cited by 84 publications

(22 citation statements)

References 53 publications

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“…The AOA is a newly introduced population-based metaheuristic optimization tactic that is utilized to solve various optimization problems in a short duration of time [46][47][48][49][50].…”

Section: Aoa Overviewmentioning

confidence: 99%

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Stability Enhancement of Wind Energy Conversion Systems Based on Optimal Superconducting Magnetic Energy Storage Systems Using the Archimedes Optimization Algorithm

et al. 2022

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Throughout the past several years, the renewable energy contribution and particularly the contribution of wind energy to electrical grid systems increased significantly, along with the problem of keeping the systems stable. This article presents a new optimization technique entitled the Archimedes optimization algorithm (AOA) that enhances the wind energy conversion system’s stability, integrated with a superconducting magnetic energy storage (SMES) system that uses a proportional integral (PI) controller. The AOA is a modern population technique based on Archimedes’ law of physics. The SMES system has a big impact in integrating wind generators with the electrical grid by regulating the output of wind generators and strengthening the power system’s performance. In this study, the AOA was employed to determine the optimum conditions of the PI controller that regulates the charging and discharging of the SMES system. The simulation outcomes of the AOA, the genetic algorithm (GA), and particle swarm optimization (PSO) were compared to ensure the efficacy of the introduced optimization algorithm. The simulation results showed the effectiveness of the optimally controlled SMES system, using the AOA in smoothing the output power variations and increasing the stability of the system under various operating conditions.

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“…The AOA is a newly introduced population-based metaheuristic optimization tactic that is utilized to solve various optimization problems in a short duration of time [46][47][48][49][50].…”

Section: Aoa Overviewmentioning

confidence: 99%

“…The AOA goes through six stages until reaching a seventh stage, which is a global solution; these seven stages are thoroughly discussed in the following section [42,[46][47][48]:…”

Section: Aoa Stages and Mathematical Modellingmentioning

confidence: 99%

Stability Enhancement of Wind Energy Conversion Systems Based on Optimal Superconducting Magnetic Energy Storage Systems Using the Archimedes Optimization Algorithm

et al. 2022

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“…Generation of electricity directly from solar energy is accomplished by photovoltaic (PV) systems [7] and their behaviour is successfully simulated based on several PV models [8,9]. They have applications such as heating and cooling [10], fuel cells [11], cost-effective emission dispatches [12] and water desalination [13].…”

Section: Introductionmentioning

confidence: 99%

Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

et al. 2021

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Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively.

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“…The progress of energy is a main objective in human life [1][2][3][4][5]. Electrical vehicles (EV) are spread out in most countries due to the emissions of gases from internal combustion vehicles [6].…”

Section: Introductionmentioning

confidence: 99%

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

et al. 2021

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The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer.

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An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification

Cited by 84 publications

References 53 publications

Stability Enhancement of Wind Energy Conversion Systems Based on Optimal Superconducting Magnetic Energy Storage Systems Using the Archimedes Optimization Algorithm

Stability Enhancement of Wind Energy Conversion Systems Based on Optimal Superconducting Magnetic Energy Storage Systems Using the Archimedes Optimization Algorithm

Identification of Parameters in Photovoltaic Models through a Runge Kutta Optimizer

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

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