A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem

Shaheen, Mohamed A. M.; Yousri, Dalia; Fathy, Ahmed; Hasanien, Hany M.; Alkuhayli, Abdulaziz; Muyeen, S. M.

doi:10.3390/en13215679

Cited by 49 publications

(16 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Owing to the responsive impact of such parameter, tuning the PI controller faces great difficulties. Therefore, many metaheuristic algorithms have been improved in order to overcome those difficulties, such as particle swarm optimization (PSO) [8], sunflower optimization algorithm (SFO) [9][10], hybrid GWO-PSO optimization technique [11], genetic algorithm (GA) [12], hybrid firefly and particle swarm optimization technique [13], Harris hawks optimization Method [14], marine predators algorithm [15], hierarchical model predictive control [16], Tabu search [17], quasi-oppositional selfish herd optimization (QSHO) [18], Cuttlefish optimization algorithm (CFA) [19], and teaching-learning based optimization [20]. Each of those techniques has its benefits and drawbacks [21].…”

Section: A Literature Reviewmentioning

confidence: 99%

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

et al. 2021

Self Cite

View full text Add to dashboard Cite

Microgrids take a large part in power networks thanks to their operational and economic benefits. This research introduces a novel implementation of an adaptive proportional plus integral (PI) controller to boost the autonomous microgrid operation efficiency. The least mean and square roots of the exponential algorithm are utilized in the adaptive PI control strategy. The multi-objective function for both sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms is obtained by The Response Surface Methodology. The system is evaluated under different environments, which are stated as follows: 1) disconnect the system from the grid (islanding), 2) autonomous system exposure to load variability, and 3) autonomous system exposure to a symmetrical fault. The proposed practicality of the control plan is shown by the data of the simulation, which is extracted from PSCAD/EMTDC software. The strength of the suggested adaptive control is confirmed through matching its results with those obtained using the SFO and PSO based optimal PI controllers. INDEX TERMS microgrid; optimization; power systems; renewable energy. Ahmed Al-Durra (S'07-M'10-SM'14) received his PhD in ECE from Ohio State University in 2010.He is a Professor in the EECS Department at Khalifa University, UAE. His research interests are applications of control and estimation theory on power systems stability, micro and smart grids, renewable energy systems and integration, and process control. He has one US patent, one edited book, 12 book chapters, and over 210 scientific articles in top-tier journals and refereed international conference proceedings. He has supervised/co-supervised

show abstract

Section: A Literature Reviewmentioning

confidence: 99%

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, the methods applied to this optimization problem are atom search optimizer, 4 shuffled frog‐leaping (SFL), 5 firefly, and imperialist competitive optimizers. Moreover, harmony search algorithm, 19 SFO, 20 salp swarm algorithm, 21 biogeography‐based optimizer, 22 GA, 23 NNO, 24 DE algorithm, 25 cuckoo search algorithm, 26 HFPSO, 27 GWO, 28 shark smell optimizer, 17 HHO, 29 cuttle fish optimization algorithm, 30 MPA, 31 Tree‐seed optimization, 32 GHO, 33 Water Cycle Algorithm, 34 and Transient Search Optimization 35 are also examples of the meta heuristic techniques used in optimization process. These strategies also have their own strong and weak points.…”

Section: Introductionmentioning

confidence: 99%

Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm

et al. 2021

Self Cite

View full text Add to dashboard Cite

Summary The accuracy of the modeling of the fuel cell is important for achieving precise simulation results. This article presents a newly developed optimization method “Chaotic MayFly optimization algorithm” (CMOA) for obtaining the proton exchange membrane fuel cell (PEMFC) parameters. This research mainly targets an accurate modeling of the PEMFC that provides good match between the simulation results and those measured practically. In this regard, the I–V characteristics of the PEMFC's are non‐linear, and there are seven design variables are considered because of the manufacturer's shortage in providing such information. The optimization problem formulated in this study is a non‐linear problem. The objective function is mathematically expressed as the total squared error between the PEMFC terminal voltage measured in the laboratory vs the estimated terminal voltage from the simulation of the model. Since the metaheuristic optimization techniques are significantly influenced by the problem initialization, a new hybridization between the chaotic mapping and the MOA is employed to tackle the problem of the PEMFC design variables estimation and achieving better results. The CMOA is applied to find the best solution of the objective function that satisfies the preset conditions. The accurateness of the PEMFC approximated model is verified numerically using the optimal design variables. The simulation results are verified under various conditions of temperature and pressure. The estimated numerical results are compared with the measured data in case of many standard PEMFCs, such as Ballard, Mark V 5 kW, 500 W BCS, and 250 W stacks. The robustness of the proposed CMOA applied to the PEMFC model is also tested. The findings of the simulations of the proposed CMOA are compared with other findings obtained by other optimization methods. Applying the CMOA results in an accurate development of the PEMFC model.

show abstract

“…Moreover, the MPA was applied to predict the COVID-19 infected cases in [53,54]. Another application of the MPA was presented in [55] to solve the problem of the optimized reactive power dispatch in power systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper presents a fully optimized fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of a hybrid microgrid system. The Marine Predator Algorithm (MPA), a recent optimization algorithm, is used to optimize the gains as well as the input scaling factors and membership functions of the proposed fuzzy PIDF controller. The controller performance is tested on a two-area hybrid microgrid system containing various renewable energy sources and energy storage devices. The effectiveness of the MPA based FPIDF controller is compared with conventional PIDF and FPIDF controllers based on other heuristic techniques presented in literatures. Moreover, different scenarios are implemented in this study to verify the robustness and sensitivity of the proposed controller to different step load perturbations, system parameters variations, wind speed fluctuation and solar irradiance variation. The dynamic response of the system is compared using different controllers in terms of settling time, maximum overshoot and undershoot values. The results are presented in the form of time domain simulations conducted via MATLAB/SIMULINK. INDEX TERMSFuzzy control, load frequency control, marine predator algorithm, microgrid system, PIDF controller, renewable energy, storage systems. NOMENCLATURE A Rotor swept area ACE Area control error B1, B2 Frequency bias coefficients BES Battery energy storage CE Change of error Predator step size control ratio Power coefficient COR Competition over resources E Error FF Fitness function FLC Fuzzy logic controller FPIDF Fuzzy proportional-integral-derivative with filter G Solar radiation ( ) Controller transfer function Solar irradiance under standard test conditions 1 , 2 Input scaling gains 12

show abstract

A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem

Cited by 49 publications

References 65 publications

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm

Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

Contact Info

Product

Resources

About