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Maximum Power Point Tracking of Photovoltaic Module Arrays Based on a Modified gray wolf optimization algorithm

ENERGIES 2023年第11期16卷 4329-4329页

作者： Huang, Kuo-Hua Chao, Kuei-Hsiang Kuo, Ying-Piao Chen, Hong-Han Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan

In this study, a modified gray wolf optimization algorithm (GWOA) was proposed to facilitate the maximum power point tracking (MPPT) of photovoltaic module arrays (PMAs). To increase the voltage conversion ratio and achieve a voltage boost through reduced duty cycles, a high-voltage step-up converter with a coupled inductor was used to replace the conventional energy storage inductor. To achieve global MPPT, the iteration parameters of the proposed GWOA were adjusted according to the slope of the PMA power-voltage (P-V) curve. According to the simulation results, the modified GWOA is more effective in MPPT than the perturbation and observation algorithm and conventional GWOA when multiple peaks appear in the P-V curve of a shaded PMA. In addition, the modified GWOA exhibits an improved tracking speed response and steady-state response.

关键词： gray wolf optimization algorithm photovoltaic module array global maximum power point tracking partial shading coupled inductor high-voltage step-up converter

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Improved gray wolf optimization algorithm Based on tanh Activation Inertia Weights 23

Improved Gray Wolf Optimization Algorithm Based on tanh Acti...

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Proceedings of the 2023 5th International Conference on Big Data Engineering and Technology

作者： Zeqiang Zhuang School of Computer Science and Engineering Hunan University of Science and Technology China

ISBN: (纸本)9798400707537

Aiming at the drawbacks of gray wolf optimization (GWO) algorithm, such as low accuracy, slow convergence, and easy to fall into local optimum, an improved gray wolf optimization algorithm based on tanh activation inertia weight (TGWO) is proposed. TGWO makes use of the characteristics of tanh function to adjust the inertia weight and balance the global optimization and local optimization, thus improving the efficiency of GWO. To verify the performance of TGWO algorithm, 8 typical test functions were used to compare the performance of TGWO with 5 classical swarm intelligence algorithms. The results show that TGWO has better optimization performance than the other 5 classical swarm intelligence algorithms.

关键词： Convergence rate gray wolf optimization algorithm Inertia weight Solution accuracy Tanh function

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Adaptive gray wolf optimization algorithm based on Gompertz Inertia Weight Strategy

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第11期14卷 210-221页

作者： Pan, Qiuhua ShanghaiTech Univ Inst Math Sci Shanghai 201210 Peoples R China

To solve the problems that the gray wolf Optimizer (GWO) convergence speed is not fast enough and the solution accuracy is not high enough, this paper proposes an Adaptive gray wolf Optimizer based on Gompertz inertia weighting strategy (GGWO). GGWO uses the characteristics of the Gompertz function to achieve nonlinear adjustment of the inertia weight, which better balances the speed of global search and accuracy of local search of the GWO algorithm. At the same time, the Gompertz function is used to realize the adaptive adjustment of the individual gray wolf's position and to better update the gray wolves' position according to the fitness values of different gray wolf individuals. Use 6 classic test functions to compare the performance of GGWO in optimization and 10 other classic or improved swarm intelligence algorithms. Results show that GGWO has better solution accuracy, stability, and faster convergence than all other 10 swarm intelligence algorithms.

关键词： gray wolf optimization algorithm inertia weight adaptive Gompertz function swarm intelligence algorithm

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Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm

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ENERGY 2022年第0期240卷 122800-122800页

作者： Wang, Jian Xu, Yi-Peng She, Chen Xu, Ping Bagal, Hamid Asadi Chinese Acad Sci Inst Sci Beijing 100190 Peoples R China Chinese Acad Sci Inst Dev Beijing 100190 Peoples R China Shanwei Inst Technol Sch Engn Shanwei 516600 Guangdong Peoples R China Tiangong Univ Sch Math Sci Tianjin 300387 Peoples R China TianGong Univ Sch Econ & Management Tianjin 300387 Peoples R China Guangzhou Sport Univ Sch Leisure Sports & Management Guangzhou 510500 Peoples R China Shanwei Inst Technol Sch Teacher Educ Shanwei 516600 Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

Industrial and commercial use of fuel cells as a source of clean energy production are two important goals of researchers in the field of energy today. Therefore, researchers in this science are always looking for new methods for industrial production and at a reasonable price for fuel cells. This study presents a new well-organized methodology for model identification of a Solid Oxide Fuel Cell (SOFC) stack by providing optimal selection of the unknown variables in the model. The main objective here is to minimize the sum of squared error value between the designed model output voltage and the experimental data. Here, to provide an optimization process, a modified version of gray wolf optimization (MGWO) algorithm has been utilized. This algorithm is then utilized to improve the algorithm efficiency and to get better results. To show the system reliability, two scenarios based on temperature and pressure variations have been utilized. The technique has been finally compared with several other techniques to verify its prominence. With considering the achieved results, it can be observed that the sum of square error for different temperature values based on the proposed method is too small, such that for the temperatures with values 553.4 degrees C, 652.3 degrees C, 669.8 degrees C, 754.6 degrees C, and 800 degrees C the SSE value is 5.27 e-4, 2.66 e-4, 3.91 e-6, 4.19 e-3, 2.07 e-4, respectively. Furthermore, the pressure value variations from 1 atm to 5 atm with 1.44 e-3, 3.20 e-3, 5.84 e-3, 3.36 e-3, 2.67 e-3, respectively indicate its higher efficiency toward the other studied methods. Final results designate that the proposed technique delivers outstanding efficiency toward the compared methods. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： System identification Parameter estimation optimization Solid oxide fuel cell gray wolf optimization algorithm

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Improved gray wolf optimization algorithm for solving placement and sizing of electrical energy storage system in micro-grids

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ISA TRANSACTIONS 2020年 102卷 376-387页

作者： Miao, Di Hossain, Sarmistha Shenzhen Polytech Shenzhen 518055 Guangdong Peoples R China Chittagong Univ Engn & Technol Chittagong 4349 Bangladesh

Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Micro-grids are small-scale networks at low voltage levels that are use to provide thermal and electrical loads of small locations where there is no access to the main electrical grid. Given the environmental and economic issues for these areas, micro-grids can be a good solution for energy production. In this paper, determining the size and location of optimal electrical energy storage systems is presented. In other side, a new method based on the cost benefit analysis for optimal sizing of an energy storage system in a microgrid (MG) is proposed. The uncertainties associated with renewable energy sources and the occurrence of defects in the grid connection network and the effect of the contribution of load responses in a micro-grid are taken into account. The combined system consists of wind turbines and fuel cells. Basically, wind power is not definitively available. The new proposed method is based on two-stage randomization design (TSRD) for modeling the effect of wind power uncertainty so that the predicted wind energy error is considered as the main random parameter in the model. A standard probability distribution function is used to represent the error variations. Given the continuity of the mentioned function, the probability error function is extracted using the new discrete method and a certain number of scenarios with a certain probability. Finally, the problem has been transformed into an optimization problem, and a gray wolf optimization (GWO) algorithm has been used to solve it. In the proposed developed model based on local and global search, the algorithm tries to reach the final result in the shortest possible time and with the most precision. The results of the simulation show the efficiency of the proposed method in solving the micro-grid problem. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Optimum storage locating and measurement Renewable resources gray wolf optimization algorithm Micro-grid

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Indoor Infrared Sensor Layout optimization for Elderly Monitoring Based on Fused Genetic gray wolf optimization (FGGWO) algorithm

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SENSORS 2024年第16期24卷 5393页

作者： Chen, Shuwang Chen, Yajiang Feng, Meng Hebei Univ Sci & Technol Sch informat Sci & Engn Shijiazhuang 050018 Peoples R China Hebei Prov Hosp Tradit Chinese Med Dept Acupuncture Shijiazhuang 050011 Peoples R China

With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic gray wolf optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic algorithm (GA) and the local search capability of gray wolf optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities.

关键词： elderly monitoring sensor layout genetic algorithm gray wolf optimization algorithm monitoring system optimization

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Solve fractional differential equations via a hybrid method between variational iteration method and gray wolf optimization algorithm

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ASIAN-EUROPEAN JOURNAL OF MATHEMATICS 2021年第8期14卷

作者： Entesar, Ahmed Qasim, Omar Saber Univ Mosul Coll Comp Sci & Math Dept Math Mosul Iraq

In this paper, a hybrid method between Variational Iteration Method (VIM) and gray wolf optimization algorithm (GWO) was proposed to solve the fractional differential equations (FDE), where the optimal parameter value (lambda) for the VIM was estimated by the GWO. The solutions in the proposed method, GWO-VIM, demonstrated the efficiency and reliability compared to the default method VIM, by calculating the maximum absolute errors (MAE) and mean square error (MSE).

关键词： Variational iteration method gray wolf optimization algorithm nonlinear fractional differentiation equations

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Improving the Mechanical Property of Dissimilar Al/Mg Zn-Added Ultrasound-Assisted Friction Stir Lap Welding Joint by Back Propagation Neural Network-gray wolf optimization algorithm

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ADVANCED ENGINEERING MATERIALS 2019年第12期21卷 1900973-1900973页

作者： Song, Qi Ren, Zhaoxu Ji, Shude Niu, Shiyu Qi, Weiwei Chen, Mingfei Shenyang Aerosp Univ Coll Aerosp Engn Shenyang 110136 Liaoning Peoples R China Liaoning Zhuanglong UAV Technol Co Ltd Zhuanglong Ind Grade Multirotor Uav Profess Techn Shenyang 110136 Liaoning Peoples R China

Process parameters of rotating velocity, welding speed, Zn interlayer thickness, and ultrasound power are optimized by the hybrid of back propagation neural network (BPNN) and gray wolf optimization algorithm (GWOA) to obtain a high-quality Zn-added ultrasound-assisted friction stir lap welding joint of 7075-T6 Al/AZ31B Mg dissimilar alloys. The results state that the prediction accuracy of the trained BPNN model is acceptable. The optimal process parameters combination is obtained by the GWOA which is combined with the trained BPNN. The verification tests are performed under the executable optimal solution, which consists of the rotating velocity of 1054 rpm, the welding speed of 54 mm min(-1), the Zn interlayer thickness of 0.05 mm, and the ultrasound power of 1568 W. The tensile shear load of the joint reaches 9.05 kN, and the strength is 11.8% larger than that of the reported optimal joint. The artificial intelligence optimization method of GWOA combined with BPNN can accurately predict and optimize the joint strength, which has great time and economic advantages.

关键词： Al Mg dissimilar alloys back propagation neural network friction stir lap welding gray wolf optimization algorithm process parameters optimization

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Optimally sized design of a wind/photovoltaic/fuel cell off-grid hybrid energy system by modified-gray wolf optimization algorithm

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ENERGY & ENVIRONMENT 2018年第6期29卷 1053-1070页

作者： Barenji, Reza Vatankhah Nejad, Mazyar Ghadiri Asghari, Iraj Hacettepe Univ Dept Ind Engn TR-06800 Ankara Turkey Eastern Mediterranean Univ Dept Ind Engn Famagusta Turkey Hacettepe Univ Dept Clean & Renewable Energies Ankara Turkey

This study deals with an off-grid hybrid energy generation system composed of wind turbines, photovoltaic cells, and fuel cells to supply a specific load. The purpose is to minimize the cost of energy generation over a period of lifetime while satisfying a set of system reliability constraints in the system. The stated objective is to determine the optimal value of system components, that is, the number of wind turbines, the number and angle of photovoltaic arrays, the size of electrolyzer, hydrogen tanks, fuel cells, and DC/AC converters. The costs incorporated into this design included net present value of investment, costs of equipment, replacement and maintenance, and the costs arising from power supply interruption, all for a period of 20 years considered as the system lifetime. The data pertaining to load demand, sunlight and wind speed were considered to be known and deterministic. This design considered the failure of three main system components, namely, wind turbines, photovoltaic cells, and AC/DC converter, and incorporated a number of cost factors such as initial investment, operating and maintenance expenses, and value of lost load. The wind and solar data used in this study pertained to Ankara, Turkey. The gray wolf optimization algorithm for the first time is used to optimize such a system, and the results are compared with the ones obtained by particle swarm optimization algorithm. A new hybrid metaheuristic algorithm based on the modified-gray wolf optimization algorithm and the traditional particle swarm optimization algorithms is proposed to solve the problem. The results indicate that the gray wolf optimization algorithm achieves better optimal results in comparison to the well-known particle swarm optimization algorithm and the developed hybrid method performs better in comparison to the gray wolf optimization and particle swarm optimization algorithms for this specific optimization problem.

关键词： gray wolf optimization algorithm hybrid systems renewable energies wind turbines photovoltaic arrays fuel cell

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A Route Planning Method for Underwater Terrain Aided Positioning Based on gray wolf optimization algorithm 1

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17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Shen, Jian Shi, Jing Xiong, Lu Naval Acad Armament Beijing Peoples R China Wuhan Ordnance NCO Acad Wuhan Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

Underwater terrain aided positioning technology is an effective method to improve the navigation accuracy of underwater vehicle. The route pre-planning on digit map can reduce matching time and improve matching accuracy for underwater terrain aided positioning. In this paper, a kind of route planning method for underwater terrain aided positioning based on gray wolf optimization (GWO) algorithm is proposed. Firstly, the GWO algorithm was introduced. The objective functions and route planning method was researched combining with terrain matching problem. Secondly, the calculation formulas of underwater terrain entropy were introduced as well as the terrain information distribution. Thirdly, simulation parameters were set and the best planning route was get using GWO route planning method. Finally, the terrain matching simulation of ICCP was implemented along with the planned route which proved the feasibility of the planning method.

关键词： Underwater terrain aided positioning Route planning gray wolf optimization algorithm Terrain entropy ICCP

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