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Design and investigation of PV string/central architecture for bayesian fusion technique using grey wolf optimization and flower pollination optimized algorithm

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期286卷

作者： Hemalatha, S. Renoald, A. Johny Banu, G. Indirajith, K. St Josephs Inst Technol Dept EEE Chennai Tamilnadu India Erode Sengunthar Engn Coll Dept EEE Perundurai Tamilnadu India VSB Coll Engn Tech Campus Dept EEE Coimbatore Tamilnadu India

One of the most essential factors in the current study is effectively harvesting the Maximum Power Extraction (MPE) from the Photovoltaic (PV) panel. The primary difficulties in extracting solar power is occurrence of partial shading which causes the panel to significantly increases power loss. These will mainly occur due to when partially shaded solar PV array kept under certain critical conditions for obtaining maximum output power. Many researcher have suggested by connecting bypass diodes in anti-parallel to the PV modules hotspots in the modules can be avoided. Out of all techniques, the proposed Bayesian Fusion Technique (BFT) is a hybrid optimization algorithm that combines the grey wolf optimization (GWO) and Flower Pollination algorithm (FPA) techniques to optimize the performance of solar panels in photovoltaic (PV) systems. The combination of GWO and FPA forms an ideal combination that is beneficial for optimizing the performance of PV systems is determined in this work. In this study real 6*6 PV array string and irregular PV array configuration such as central and parallel-series PV string combination of various partial shading pattern is compared and found to be effective for reducing the hotspots problems. The performance of these configuration under different shading patterns have been compared and analyzed with the different parameters like output power, conversion efficiency and tracking efficiency. This article state about the influence of partial darkening and the crucial point that reduce the sensitivity to shading heaviness. For better understanding for reader the MATLAB/Simulink software is used to validate the simulation result with real time data. Overall, this article states the BFT is an efficient and reliable approach to improve the efficiency of PV systems, by combining two optimization techniques like GWO and FPA hybrid algorithm. This article gives clear insight to the researchers for choosing BFTGWO algorithm in order to decrease the c

关键词： grey wolf optimization algorithm Flower Pollination algorithm Partially Shading Condition Bayesian Fusion Technique Maximum Power Point Photovoltaic array

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Multi-strategy enhanced grey wolf algorithm for obstacle-aware WSNs coverage optimization

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AD HOC NETWORKS 2024年 152卷

作者： Wang, Zhendong Huang, Lili Yang, Shuxin Luo, Xiao He, Daojing Chan, Sammy Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Jiangxi Peoples R China Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou 341000 Jiangxi Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy grey wolf optimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability.

关键词： Hybrid higher-order coverage awareness model grey wolf optimization algorithm Obstacles Coverage optimization Network lifetime

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Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 289-334页

作者： Xiangyang Cao Yaojie Zheng Hanbin Xiao Min Xiao School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430063China Sanya Science and Education Innovation Park Wuhan University of TechnologySanya572024China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan430063China

This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode *** algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low *** offshore wind power generation system model is presented to verify the algorithm *** offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/*** with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational ***,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation *** results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.

关键词： Offshore wind power generation efficiency maximum power point tracking(MPPT) integral sliding mode control grey wolf optimization algorithm offshore photovoltaic cells

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Federated learning and GWO-enabled consumer-centric healthcare internet of things for pancreatic tumour

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ALEXANDRIA ENGINEERING JOURNAL 2025年 122卷 344-354页

作者： Alruwais, Nuha Elhessewi, Ghada Moh. Samir Saeed, Muhammad Kashif Alshammeri, Menwa Alrusaini, Othman Alkharashi, Abdulwhab Al Zanin, Samah Said, Yahia King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn POB 22459 Riyadh 11495 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Hlth & Rehabil Sci Dept Hlth Sci POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Dept Comp Sci Muhayil Saudi Arabia Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakakah Saudi Arabia Umm Al Qura Univ Makkah Appl Coll Dept Engn & Appl Sci Mecca Saudi Arabia Saudi Elect Univ Dept Comp Sci Coll Comp & Informat Riyadh Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj Saudi Arabia Northern Border Univ Ctr Sci Res & Entrepreneurship Ar Ar 73213 Saudi Arabia

In order to get a correct diagnosis and choose the best treatment options before it becomes deadly, early detection and classification of pancreatic tumours are essential. Grading can be a tedious and time-consuming process for experts and doctors when the case is complex. In such cases, experts usually look at the tumour and pinpoint its exact position. Moreover, it could be required to compare the tumor's cells to those in the vicinity. The end goal is to confirm that the growth is a tumour and, if possible, to ascertain the exact type and grade of the tumour. However, due to the high amounts of weights sent and received from the client-side trained models, federated learning techniques incur substantial communication overhead. This study aims to address this problem by introducing a unified framework that integrates the inherent capabilities of Federated Learning (FL) with the unique characteristics of the grey wolf Optimisation algorithm. The pancreatic tumour dataset is used to evaluate the GWO-enabled FL framework. The proposed model was more network efficient, performed better in data imbalance scenarios, and led to lower communication costs than the currently available federated average model. Following validation, the proposed framework attained a prediction accuracy of 98.9%. For pancreatic tumour classification, the data obtained from the proposed system can be a useful component.

关键词： Pancreatic Tumour Classification Federated Learning grey wolf optimization algorithm CNN

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Capacity configuration and control optimization of off-grid wind solar hydrogen storage system

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ENERGY 2025年 324卷

作者： Zhao, Yuyang Zhao, Yuhuan Cao, Song Zhu, Zhengzheng Sun, Hexu Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Peoples R China Hebei Engn Lab Wind Power & Photovolta Coupling Hy Shijiazhuang 050018 Peoples R China

The configuration and operational validation of wind solar hydrogen storage integrated systems are critical for achieving efficient energy utilization, ensuring economic viability, and maintaining system stability. This study proposed an off-grid multi-energy system capacity configuration and control optimization framework based on the grey wolf optimization (GWO) algorithm, which enhances system revenue through an improved capacity allocation model. The results demonstrate the following: Firstly, the proposed system achieves a significant financial improvement, with an annual revenue increase of 33.79 % compared to a hydrogen production scheme relying solely on wind power without energy storage. Secondly, the adoption of a wind solar complementary hydrogen production approach increases the annual revenue of the system by 33.33 % compared to the single wind power hydrogen production scheme. Furthermore, a system operation control strategy based on the State of Charge (SOC) of lithium batteries is proposed. Using operational data from the Zhangjiakou Chongli wind solar complementary coupling hydrogen production project, the effectiveness of the proposed control strategy is validated, demonstrating its ability to ensure stable system operation. Results show that in wind and solar power excess scenarios, the SOC rises from 45.8 % to 49.8 %, and declines to 47.2 % when there's a power deficit, indicating its potential for peak shaving and valley filling, thereby mitigating fluctuations in wind and solar power output.

关键词： Wind power Solar power Energy storage Capacity configuration Power prediction grey wolf optimization algorithm

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A novel structural adaptive seasonal grey Bernoulli model in natural gas production forecasting

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 148卷

作者： Yan, Shuli Peng, Mengna Wu, Lifeng Xiong, Pingping Nanjing Univ Informat Sci & Technol Res Inst Risk Governance & Emergency Decis Making Collaborat Innovat Ctr Forecast & Evaluat Meteorol Sch Management Sci & Engn Nanjing 210044 Peoples R China Univ Granada Dept Comp Sci & Artificial Intelligence Granada 18071 Spain Hebei Univ Engn Coll Management Engn & Business Handan 056038 Peoples R China

Energy data are characterized by nonlinearity, seasonality and growth, which render prediction challenging, making it difficult for Chinese government to formulate relevant policies. Considering that the grey Bernoulli model can deal with nonlinear and growing data, and utilizing the newly proposed seasonal factor, we propose a novel structural adaptive seasonal grey Bernoulli model, which is limited to application to nonlinear, seasonal, and growing time sequence. Firstly, the traditional seasonal factors are logarithmically transformed to ensure more refined seasonal factor sequence. Then, the grey Bernoulli model and the grey structural adaptive model are combined in a multiplicative manner to create a new model, thereby refining its structural adaptability. And the grey wolf optimization algorithm is used to optimize the parameters in the model. Finally, the natural gas production prediction is implemented to verify the feasibility of this model. The mean absolute percentage errors of the proposed model are below 5% both in the experiment and in the robustness test. Compared with other models, this model demonstrates an improvement of 29.75% in prediction accuracyand 15.80% in robustness test. Additionally, the Diebold & Mariano statistics exhibit values that are essentially significant at the 10% level. The experimental results substantiate the validity, superiority, and robustness of the model proposed in this paper, which can be effectively applied to the prediction of seasonal natural gas production.

关键词： grey Bernoulli model Structural adaptive grey model Seasonal natural gas production grey wolf optimization algorithm

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Research on the motion trajectory control of industrial manipulators by the incremental PID control optimization algorithm

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MECHANICS & INDUSTRY 2025年 26卷

作者： Wang, Jicai Chen, Fangyan Jiaxing Nanyang Polytech Inst Jiaxing 314031 Zhejiang Peoples R China

With the wide application of industrial manipulators, the research of its moving trajectory control method has become more and more important content. In this paper, the motor closed-loop control strategy of an industrial 6R manipulator was briefly introduced, the proportional, integral, and differential ( (PID) algorithm commonly used in motor control was analyzed, and its shortcomings were pointed out. Then, the incremental PID algorithm was introduced to design a grey wolf optimization (IGWO) algorithm to achieve parameter tuning. Simulation experiments were carried out in a MATLAB environment. The results showed that the incremental PID algorithm generated a smaller control deviation than the PID algorithm. The average deviation of the three axes of the incremental PID algorithm undergoing IGWO parameter tuning was less than 0.2 mm, and the moving time was between 1.1 s and 1.3 s. The control accuracy and efficiency of the incremental PID algorithm undergoing IGWO parameter tuning were better than those of the incremental PID algorithm whose parameters were tuned using the trial-and-error method and the grey wolf optimization algorithm respectively. Moreover, better stability and anti-interference were observed in step response and sinusoidal response. The results prove that the incremental PID algorithm combined with the IGWO algorithm is reliable in the moving trajectory control of industrial manipulators and can be applied in practice.

关键词： Incremental PID control industrial manipulator grey wolf optimization algorithm sine response

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Research on dynamic flatness feedback control strategy based on IGWO control efficiency identification for cold tandem rolling mill

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年 1-21页

作者： Zhou, Xiaomin Li, Liqi Wang, Shuaikun Xiong, Qingxia Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan 528399 Peoples R China

Flatness control is an important means to improve the quality of the strip, and modern tandem cold rolling mills are equipped with various control actuators. The existing flatness control efficiency is determined offline and remains constant throughout the manufacturing process. During actual manufacturing, equipment, and process states may change, making it necessary to identify flatness control efficiency online. The main flatness feedback control strategy is a fixed priority sequence in the existing CVC (Continuous Variable Crown) cold tandem rolling mill. The control system does not have the flexibility to adjust the sequence based on real-time flatness. Aiming at the above problems, in order to improve the effect of flatness control, this paper proposes a dynamic flatness feedback control strategy based on control efficiency identification. To address the issue of high cost and difficulty in updating the on-site flatness control efficiency, based on historical actual data, the IGWO (improved grey wolf optimization) algorithm is used to intelligently identify control efficiency. Data post-processing through IF (isolated forests) and CLT (central limit theorem) obtains results on control efficiency. At the same time, a similarity index is proposed to dynamically adjust the priority sequence of feedback control based on the identification results of the control efficiency and the measured flatness deviations. Finally, the field application results show that the flatness control accuracy is improved, the reduction value of the flatness deviations increased by 13.41%, and the reduction rate of the flatness deviations increased by 9.66%.

关键词： Flatness control efficiency grey wolf optimization algorithm Flatness feedback control Feedback control strategy

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A novel time-varying parameters structural adaptive Hausdorff fractional discrete grey model and its application in renewable energy production and consumption prediction

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ENERGY 2025年 318卷

作者： Wang, Yong Zhang, Zejia Wang, Yunhui Sun, Lang Yang, Rui He, Wenao Sapnken, Flavian Emmanuel Li, Hong-Li Southwest Petr Univ Sch Sci Chengdu 610500 Sichuan Peoples R China Neijiang Normal Univ Key Lab Numer Simulat Sichuan Prov Univ Sch Math & Informat Sci Neijiang 641000 Sichuan Peoples R China Univ Douala Univ Inst Technol Lab Technol & Appl Sci POB 8698 Douala Cameroon Xinjiang Univ Coll Math & Syst Sci Urumqi 830017 Xinjiang Peoples R China

The role of energy is essential in driving human development and achieving economic and social progress. It's crucial to make precise predictions about how traditional and new energy sources will be used in the future, as this information can help a country create effective energy strategies and make adjustments to its energy structure accordingly. Considering the common presence of volatility, nonlinearity and periodicity in long-term energy data series, this paper proposes a novel time-varying parameters adaptive discrete grey prediction model FHATDGM (1,1) based on Hausdorff fractional derivative. This model further explores the possible periodic features in the system, in order to more comprehensively and accurately describe the development trend of the time series. In addition, the grey wolf optimization algorithm (GWO) is selected to optimize the parameters of the model. Then, for the robustness of the results obtained by introducing intelligent optimization algorithm, this paper uses the method of combining Monte Carlo simulation and probability density analysis to test, indicating that the proposed model has better stability and accuracy compared with the existing six models. Finally, three actual cases of China's solar power generation, United States hydroelectric power generation and China's consumption of coal are predicted. The results show that the FHATDGM (1,1) has the highest fitting and forecasting accuracy. The reduction in prediction error compared to the other six models ranged from 1.46 % to 37.49 %.

关键词： grey model grey wolf optimization algorithm Hausdorff fractional derivative Monte-carlo simulation Renewable energy production and consumption

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Construction of source load uncertainty economic dispatch model based on distributed robust opportunity constraints

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Energy Informatics 2025年第1期8卷 1-14页

作者： Li, Jinjian Information Center Guangxi Zhuang Autonomous Region Company of China National Tobacco Corporation Nanning530000 China

With the increasing demand for electricity, the power system is facing enormous challenges. To ensure the equilibrium between supply and demand in the electricity market and the safety and stability of the power grid, a source load uncertainty economic dispatch model based on distributed robust opportunity constraints is proposed to cope with the uncertainty of sustainable energy resources such as wind power and photovoltaics. By introducing an improved Elman network and grey wolf optimization algorithm, high-precision prediction of short-term loads is achieved, providing data support for scheduling models. The experiment outcomes indicate that the prediction model grounded on the improved Elman network and grey wolf optimization algorithm performs the best in scheduling performance on both the training and testing sets, with the lowest cost, the highest utilization rates of wind and solar power, and the lowest probability of constraint default. In addition, the economic dispatch model proposed by the research has significant advantages in reducing total dispatch costs, improving wind and photovoltaic utilization rates, and constraining default probability control. In typical load scenarios, the total scheduling cost of the model is $1,308,469, with wind and photovoltaic utilization rates reaching 90.5% and 86.1% respectively, and a default probability of only 0.9%. The research results indicate that the model exhibits superiority in real-time response time, especially suitable for scenarios with high load fluctuations. The research provides important theoretical basis and application value for the economic dispatch of power systems. © The Author(s) 2025.

关键词： Distributed robust opportunity constraint Elman network grey wolf optimization algorithm Power system Source load uncertainty

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