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Research on Improved Droop Control Strategy for Primary Frequency Regulation of Doubly-fed Wind Turbine Based on gray wolf algorithm 3

Research on Improved Droop Control Strategy for Primary Freq...

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3rd International Conference on Smart Power and Internet Energy Systems (SPIES) - Towards A Sustainable and Affordable Energy Transition

作者： Song Shan Wang Yang Bao Xin Wei Shurong Miao Shuxin State Grid Jiangsu Elect Power Design Consulting State Grid Jiangsu Elect Power Co Ltd Econ Res Inst Nanjing Peoples R China Shanghai Univ Elect Power Elect Power Engn Shanghai Peoples R China

ISBN: (纸本)9780738146331

Under the background of the dual-carbon strategy, the penetration rate of wind power is high, and the large-scale grid connection of wind power leads to a decrease in system frequency response capability, which can easily cause problems such as a secondary drop in system frequency or excessive response of wind turbines, which seriously affects system stability. A variable droop adjustment coefficient control method for doubly-fed wind turbines participating in the primary frequency regulation of the power grid based on the gray wolf optimization algorithm is proposed. It can adjust and optimize the droop control coefficients of the doubly-fed wind turbines for different wind speeds and improve the control accuracy. To improve the droop characteristics of wind turbines, reduce the frequency overshoot in the frequency modulation process, and effectively avoid the secondary frequency drop, providing technical support for the construction of a new power system with new energy as the main body.

关键词： double-fed wind turbine gray wolf algorithm variable sag coefficient primary frequency modulation frequency response

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Adaptive multi-UAV path planning method based on improved gray wolf algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2022年第PartA期104卷

作者： Jiaqi, Shi Li, Tan Hongtao, Zhang Xiaofeng, Lian Tianying, Xu Beijing Technol & Business Univ Coll Comp Sci & Engn Beijing Peoples R China Beijing Technol & Business Univ Sch Artificial Intelligence Beijing 100048 Peoples R China

Due to the slow convergence and insufficient flight path in path planning, we proposes an adaptive multi-UAV path planning method (AP-GWO) that improves the gray wolf algorithm. The spiral update position method is introduced using the whale algorithm as reference, while the probability of selecting the update method is set to the golden ratio of 0.618. Afterwards, in the iterative process, a different number of leadership levels is used to update the position of the individual, and the leadership is adjusted using an adaptive mechanism. The number of strata balances the process of encirclement and attack. The experimental results show that the proposed AP-GWO method can shorten the flight time of the UAV by an average of 22.8%, shorten the convergence time of the algorithm, and make the flight path of the UAV smoother.

关键词： gray wolf algorithm Path planning Adaptive Convergence time Smoothness

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Improved gray wolf algorithm Based on Dynamic Inertia Weight and Adaptive Search 7

Improved Gray Wolf Algorithm Based on Dynamic Inertia Weight...

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2020 7th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation, CDMMS 2020

作者： Cai, Pengchen Zhang, Damin College of Big Data and Information Engineering Guizhou University Guiyang China

An improved ISAGWO algorithm is proposed to solve the problem that the traditional grey wolf algorithm is easy to fall into local optimum, which leads to low search accuracy. By introducing the weight factor before the vector coefficient, the position vector updating formula and the adaptive search strategy of the dynamic modulation algorithm, the search accuracy of the algorithm and the ability of jumping out of the local optimum are improved. The efficiency of the improved grey wolf algorithm is evaluated by statistical analysis, convergence rate analysis and classical reference function. The results show that the improved grey wolf algorithm has better global search ability and solution robustness, and the optimization precision and convergence speed are also improved compared with the standard grey wolf algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Adaptive search Dynamic Inertia Weight gray wolf algorithm

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The Bent-Tube Nozzle Optimization of Force-Spinning With the gray wolf algorithm

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2021年 9卷 807287页

作者： Liu, Kang Li, Wenhui Ye, Peiyan Zhang, Zhiming Ji, Qiaoling Wu, Zijun Wuhan Text Univ Hubei Digital Text Equipment Key Lab Wuhan Peoples R China Wuhan Text Univ Sch Mech Engn & Automat Wuhan Peoples R China

Force-spinning is a popular way to fabricate various fine fibers such as polymer and metal nanofibers, which are being widely employed in medical and industrial manufacture. The spinneret is the key of the device for spinning fibers, and the physical performance and morphology of the spun nanofibers are largely determined by its structure parameters. In this article, the effect of spinneret parameters on the outlet velocity is explored and the spinneret parameters are also optimized to obtain the maximum outlet velocity. The mathematical model of the solution flow in four areas is established at first, and the relationship between outlet velocity and structure parameters is acquired. This model can directly reflect the flow velocity of the solution in each area. Then, the optimal parameters of outlet diameter, bending angle, and curvature radius are obtained combined with the gray wolf algorithm (GWA). It is found that a curved-tube nozzle with a bending angle of 9.1 degrees, nozzle diameter of 0.6 mm, and curvature radius of 10 mm can obtain the maximum outlet velocity and better velocity distribution. Subsequently, the simulation is utilized to analyze and compare the velocity situation of different parameters. Finally, the fiber of 5 wt% PEO solution is manufactured by a straight-tube nozzle and optimized bent-tube nozzle in the laboratory, and the morphology and diameter distribution were observed using a scanning electron microscope (SEM). The results showed that the outlet velocity was dramatically improved after the bent-tube parameters were optimized by GWA, and nanofibers of better surface quality could be obtained using optimized bent-tube nozzles.

关键词： force-spinning nanofibers spinneret optimization gray wolf algorithm

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Photovoltaic Power Prediction Based on RBF Neural Network Optimized by gray wolf algorithm 3

Photovoltaic Power Prediction Based on RBF Neural Network Op...

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3rd International Conference on Control and Robots (ICCR)

作者： Xin, Wang Xin, Wang Jilin Elect Power Co Ltd Yanbian Power Supply Co Yanbian Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai Peoples R China

ISBN: (纸本)9781728175621

With the vigorous development of photovoltaic power generation, the prediction accuracy of photovoltaic power generation output is getting higher and higher. Therefore, this paper proposes a photovoltaic power prediction model based on the gray wolf algorithm optimized RBF neural network. First, select the similar daily sample data through the gray correlation analysis method;then use the gray wolf algorithm to globally optimize the number of hidden layer nodes of the RBF neural network, and use the global optimal solution obtained by the gray wolf algorithm as the parameter of the RBF neural network. Finally, predict through the optimized RBF neural network. The simulation results show that the RBF neural network optimized by the gray wolf algorithm has greatly improved the prediction accuracy, and has certain significance for the actual photovoltaic power prediction.

关键词： photovoltaic power prediction gray correlation degree gray wolf algorithm RBF neural network

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Design of Reverse Logistics Network for Remanufacturing Waste Machine Tools Based on Multi-Objective gray wolf Optimization algorithm

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IEEE ACCESS 2020年 8卷 141046-141056页

作者： Jiang, Xingyu Li, Jiazhen Lu, Yitao Tian, Guangdong Shenyang Univ Technol Sch Mech Engn Shenyang 110870 Peoples R China Jilin Univ Coll Transportat Changchun 130022 Peoples R China Shandong Univ Key Lab High Efficiency & Clean Mech Manufacture Minist Educ Jinan 250100 Peoples R China

The high uncertainty of the recovery time, quantity and quality of waste machine tools has led to dynamic changes in the recycling logistics network and is difficult to plan. Considering factors such as recycling efficiency, cost, and carbon emissions, an optimized model for the recycling network of waste machine tool recycling with the goal of minimizing total operating costs and total carbon tax penalties was proposed. The optimization of the combination of recycling efficiency, cost and carbon emissions of waste machine tools has been achieved. For model solving, an optimization model solving algorithm based on the multi-object gray wolf algorithm was proposed. Problems that are difficult to apply due to too slow convergence speed and too many solving parameters were solved. Finally, the recycling process of waste machine tools of a machine tool remanufacturing enterprise was taken as an example, and the proposed model and algorithm were used to optimize the logistics network of waste machine tools recycling. The results show that the optimal scheme of the optimization model of the recycling network of waste machine tools can be obtained from the proposed model. The gray wolf algorithm is superior to the multi-objective non-dominated sorting genetic algorithm in both the convergence speed and the total cost of recovered logistics. Therefore, the validity and feasibility of the model and algorithm in this paper have been verified.

关键词： Waste machine tool reverse logsitics network recycle gray wolf algorithm

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Wireless Sensor Network Localization Incorporating gray wolf Optimization and DV-Hop algorithm

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IEEE ACCESS 2024年 12卷 168594-168606页

作者： Xu, Ziqi Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China

The rapid advancement of wireless sensor networks has made network node localization technology a crucial area of study. However, the current network node localization methods have low accuracy. To address this situation, the study introduces the concepts of hopping distance error and automatic adjustment of communication radius on the basis of the traditional DV-Hop algorithm. Weighting coefficients are introduced to assign weights to the hopping distance error, thus realizing the hopping distance correction. The planning of hopping distance between nodes is also realized by the method of multi-communication radius adjustment. Then the research uses the gray wolf algorithm to optimize the matrix operation method in the positioning algorithm. On the basis of the traditional gray wolf algorithm, the research also uses the idea of the particle swarm algorithm to improve its updating method. The results indicated that the research-designed model showed more satisfactory localization effects in different scenarios. Specifically, the average localization error of the research-designed model was kept below 0.18m, and its average running time was 0.787s. Comprehensively, the research-designed model is able to have better performance in wireless sensor network node localization, which can provide new technical support for the field of wireless networks.

关键词： Wireless sensor network DV-Hop localization localization gray wolf algorithm gray wolf algorithm weighting coeffi cients weighting coeffi cients communication radius particle swarm algorithm particle swarm algorithm particle swarm algorithm

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Developing a Grey wolf Optimization-Based gray Box Model for Cash Flow Forecasting: A Study on Tehran Stock Exchange Companies

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2025年

作者： Ahmadi, Ahmad Nassirzadeh, Farzaneh Hadavandi, Esmaeil Nejad, Mohammad Chavosh Ghorbani, Arash Islamic Azad Univ Dept Accounting Birjand Branch Birjand Iran Ferdowsi Univ Mashhad Dept Accounting Mashhad Iran Birjand Univ Technol Dept Ind Engn Birjand Iran Aalborg Univ Dept Mat & Prod Aalborg Denmark Islamic Azad Univ Dept Accounting Bojnourd Branch Bojnourd Iran

Cash flow forecasting is a critical aspect of financial planning and has long been of interest to investors and creditors. The gray Box (GB) method is a widely used tool for forecasting, but a significant challenge in developing a grey box model is parameter estimation. In this study, we introduce a novel approach to cash flow forecasting called the Grey wolf Optimization-based Grey Box model (GWOGB). The GWOGB employs a GWO algorithm as a global search method to determine the parameters of the GB model. To enhance the accuracy of future cash flow forecasting, we incorporate firm-level control variables as well as market and financial control variables into the classical model. To evaluate the performance of the proposed model, we use a sample of 250 firms listed on the Tehran Stock Exchange. Our empirical findings indicate that the GWOGB outperforms the generalized method of moments (GMM). Additionally, we employ sensitivity analysis to discern the source of forecast error and find that the inclusion of the nonlinear effect of sales growth rate on working capital accruals and future cash flow significantly reduces forecast error. The results show that using a nonlinear form of the GWOGB model is a promising approach for modeling the complex relationships between cash flow and working capital accruals.

关键词： Cash flow forecast generalized method of moments gray box method gray wolf algorithm sensitivity analysis

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Multi-objective optimization of cancer treatment using the multi-objective gray wolf optimizer (MOGWO)

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 1857-1866页

作者： Chen, Linkai Fan, Honghui Zhu, Hongjin Jiangsu Univ Technol Sch Comp & Engn Zhongwu Rd 1801 Changzhou 213001 Peoples R China

The use of mathematical modeling to study biological phenomena is one of the best methods available for studying these phenomena. The development of mathematical models for simulating, controlling, and predicting phenomena has always been significant. Applying mathematical models in optimization is one of the advantages of using them. In the context of cancer treatment, the goal is to reduce the concentration of cancer cells during the treatment period through optimal control. An important issue not considered in the previous studies was the concentration of the active drug, which had a significant influence on the patients' clinical health. The current study aimed to establish a protocol for optimal drug administration by minimizing the concentration of cancer cells and the concentration of the drug. The multi-objective gray wolf optimizer algorithm was used for the first time to solve this multi-objective problem, and the results were compared to those obtained with the NSGA-II algorithm.

关键词： Cancer Optimization gray wolf algorithm Concentration of cancer cells

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Landslide susceptibility prediction by gray wolf optimized support vector machine model under different factor states

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JOURNAL OF APPLIED REMOTE SENSING 2023年第4期17卷

作者： Duan, Gonghao Hu, Jie Deng, Lixu Fu, Jie Wuhan Inst Technol Sch Comp Sci & Engn Wuhan Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan Peoples R China China Three Gorges Univ Key Lab Geol Hazards Three Gorges Reservoir Minist Educ Yichang Peoples R China China Geol Survey Hydrogeol & Environm Geol Survey Ctr Baoding Peoples R China Engn Res Ctr Intelligent Technol Geoexplorat Wuhan Peoples R China

Landslide susceptibility prediction (LSP) is crucial for hazard prevention and geological risk assessment. Support vector machine (SVM) is widely used for LSP, but its parameter optimization problem affects the prediction accuracy and generalization ability of the model, and variations in parameter combinations may result in different prediction outcomes, which brings some challenges to the application of the model. We present a procedure for LSP using the gray wolf optimization (GWO) algorithm to optimize SVM models in the Sanshui District, Foshan City, China. Fifteen factors affecting landslide susceptibility are selected and processed by the natural breakpoint method and normalization method. To prevent overfitting and improve the generalization ability of the model, the five factors with high correlation are excluded using the Pearson correlation coefficient. The grid search method and GWO are used to optimize the SVM parameters and establish the GWO-SVM model. The results indicated that the GWO-SVM model, which incorporated the normalization method (referred to as GWO-SVM-NOR), demonstrated superior predictive accuracy, achieving an impressive area under the curve value of 0.886. The gray wolf algorithm improves the fitting accuracy of SVM and optimizes the model prediction performance with better stability, which is suitable for predicting areas susceptible to landslides.

关键词： landslide susceptibility prediction grid search method gray wolf algorithm support vector machine

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