A MPPT based on optimized FLC using manta ray foraging optimization algorithm for thermo‐electric generation systems

2021 ◽  
Author(s):  
Mokhtar Aly ◽  
Hegazy Rezk
Keyword(s):  
Optimization Algorithm ◽  
Thermo Electric ◽  
Electric Generation ◽  
Manta Ray

scholarly journals Minimization of energy consumption by building shape optimization using an improved Manta-Ray Foraging Optimization algorithm

2021 ◽  
Vol 7 ◽  
pp. 1068-1078
Author(s):  
Jiaying Feng ◽  
Xiaoguang Luo ◽  
Mingzhe Gao ◽  
Adnan Abbas ◽  
Yi-Peng Xu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Shape Optimization ◽  
Optimization Algorithm ◽  
Building Shape ◽  
Manta Ray

scholarly journals System Identification of the PEMFCs based on Balanced Manta-Ray Foraging Optimization algorithm

2020 ◽  
Vol 6 ◽  
pp. 2887-2896
Author(s):  
Biqi Sheng ◽  
Tianhong Pan ◽  
Yun Luo ◽  
Kittisak Jermsittiparsert
Keyword(s):  
System Identification ◽  
Optimization Algorithm ◽  
Manta Ray

Analysis of energy management and battery sizing in an off‐grid hybrid solar thermo‐electric generation system

2022 ◽  
Author(s):  
Muhammad Suleman Malik ◽  
Umer Azmatullah ◽  
Muhammad Omer Khan ◽  
Muhammad Abu Bakr ◽  
Mubasher Ahmad
Keyword(s):  
Energy Management ◽  
Generation System ◽  
Thermo Electric ◽  
Electric Generation

An enhanced manta ray foraging optimization algorithm for shape optimization of complex CCG-Ball curves

2022 ◽  
pp. 108071
Author(s):  
Gang Hu ◽  
Min Li ◽  
Xiaofeng Wang ◽  
Guo Wei ◽  
Ching-Ter Chang
Keyword(s):  
Shape Optimization ◽  
Optimization Algorithm ◽  
Manta Ray

scholarly journals Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3847
Author(s):  
Mahmoud G. Hemeida ◽  
Salem Alkhalaf ◽  
Al-Attar A. Mohamed ◽  
Abdalla Ahmed Ibrahim ◽  
Tomonobu Senjyu
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Stability Index ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Voltage Deviation ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Manta Ray

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

A hybrid concentrated solar thermal collector/thermo-electric generation system

Energy ◽  
2017 ◽  
Vol 134 ◽  
pp. 1001-1012 ◽  
Author(s):  
Moh’d A. Al-Nimr ◽  
Bourhan M. Tashtoush ◽  
Mohammad A. Khasawneh ◽  
Ibrahim Al-Keyyam
Keyword(s):  
Solar Thermal ◽  
Generation System ◽  
Thermo Electric ◽  
Solar Thermal Collector ◽  
Electric Generation

scholarly journals Parameter Identification of Optimized Fractional Maximum Power Point Tracking for Thermoelectric Generation Systems Using Manta Ray Foraging Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2971
Author(s):  
Ahmed Fathy ◽  
Hegazy Rezk ◽  
Dalia Yousri ◽  
Essam H. Houssein ◽  
Rania M. Ghoniem
Keyword(s):  
Steady State ◽  
Optimization Algorithm ◽  
Research Work ◽  
Operating Conditions ◽  
Dynamic Responses ◽  
Basic Unit ◽  
Maximum Efficiency ◽  
Thermoelectric Generation ◽  
Whale Optimization ◽  
Manta Ray

Thermoelectric generation systems (TEGSs) are used to convert temperature difference and heat flow into DC power based on the Seebeck theorem. The basic unit of TEGS is the thermoelectric module (TEM). TEGSs have gained increasing interest in the research fields of sustainable energy. The output power from TEM is mostly reliant on differential temperature between the hot and cold sides of the TEM added to the value of the load. As such, a robust MPPT strategy (MPPTS) is required to ensure that the TEGS is operating near to the MPP while varying the operating conditions. Two main drawbacks may occur in the conventional MPPTSs: low dynamic response, such as in the incremental resistance (INR) method, and oscillations around MPP at steady state, such as in the hill climbing (HC) method. In the current research work, an optimized fractional MPPTS is developed to improve the tracking performance of the TEGS, and remove the two drawbacks of the conventional MPPTSs. The proposed strategy is based on fractional order control (FOC). The main advantage of FOC is that it offers extra flexible time and frequency responses of the control system consent for better and robust performance. The optimal parameters of the optimized fractional MPPTS are identified by a manta ray foraging optimization (MRFO). To verify the robustness of the MRFO, the obtained results are compared with ten other algorithms: particle swarm optimization; whale optimization algorithm; Harris hawks optimization; heap-based optimizer; gradient-based optimizer; grey wolf optimizer; slime mould algorithm; genetic algorithm; seagull optimization algorithm (SOA); and tunicate swarm algorithm. The maximum average cost function of 4.92934 kWh has been achieved by MRFO, followed by SOA (4.5721 kWh). The lowest STD of 0.04867 was also accomplished by MRFO. The maximum efficiency of 99.46% has been obtained by MRFO, whereas the lowest efficiency of 74.01% was obtained by GA. Finally, the main findings proved the superiority of optimized fractional MPPTS compared with conventional methods for both steady-state and dynamic responses.

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