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Optimal energy management and scheduling of a microgrid considering hydrogen storage and PEMFC with uncertainties

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 88卷 1017-1033页

作者： Hai, Tao Aksoy, Muammer Rezvani, Alireza Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Ajman Univ Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates Al Mustaqbal Univ Coll Sci Cyber Secur Dept Babylon 51001 Iraq Ahmed Bin Mohammed Mil Coll Comp Informat Syst Dept POB 22988 Doha Qatar Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Sch Engn & Technol Da Nang Vietnam

Renewable energy sources have been widely installed and operated in power systems, particularly in microgrids in the form of distributed generation units. This issue requires efficient energy management tools which take into account the inherent uncertainties of such energy resources. Thus, this paper presents a stochastic framework aimed at scheduling the renewable energy-based and thermal units in a coordinated way. The generation units comprise fuel cell units with proton exchange membrane known as PEMFC-CHP producing heat and power, concurrently. Moreover, the uncertainties arising from wind and solar power as well as market prices are characterized by deploying scenario-based optimization. The mentioned framework considers storing hydrogen and the model is presented within a stochastic mixed-integer nonlinear programming (MINLP) framework. The resulting problem is simulated on a modified 33-bus distribution network and tackled using the modified marine predators algorithm (MMPA)algorithm. The obtained results indicate that the revenue increases by more than 5% compared to other optimization algorithms. Furthermore, taking into account CHP will increase the total profit of the system by more than 15%.

关键词： Microgrid Optimal coordinated scheduling Renewable energy sources Hydrogen storage strategy improved algorithm

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Intelligent analysis of e-government influence factors based on improved machine learning

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NEURAL COMPUTING & APPLICATIONS 2022年第15期34卷 12241-12256页

作者： Wei, Lili South China Univ Technol Dept Elect Business Guangzhou 510000 Peoples R China

In order to improve the effect of e-government work, it is necessary to analyze its influencing factors. E-government is affected by many social factors, so it is necessary to combine intelligent models to improve the effect of factor analysis. This paper combines the essence of e-government influencing factor data to improve the machine learning algorithm and uses the EM algorithm to derive the parameter estimation formula of the data in the case of missing data to improve the accuracy of data analysis. Moreover, this article combines the structure of the e-government system to build the main structure of the intelligent analysis model of the influence factors of e-government. According to the key influencing factor model of e-government adoption and multi-dimensional research on technology, organization and implementation, this paper puts forward the model and promotion mode of the e-government system based on cloud computing and conducts a simulation. From the simulation results, the effect of the model proposed in this paper is more significant.

关键词： Machine learning improved algorithm E-government Influencing factors

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RETRACTED: Research on English score analysis system based on improved decision tree algorithm and fuzzy set (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5673-5685页

作者： Zhang, Weiying Chengdu Sport Univ Chengdu 610041 Peoples R China

At present, the data mining technology is introduced into the analysis of English scores, the data is deeply explored and analyzed reasonably, and the analysis results are used to guide the smooth development of teaching, which is conducive to improving the quality of English teaching. The main work of this thesis is based on the background of this study: taking the academic performance of college students as the application background, this paper first introduces the basic theoretical knowledge of data mining and the application status of data mining technology in education field. Secondly, this paper establishes a student performance database and uses data mining technology to carry out in-depth mining of the established performance database. Finally, the mining results are analyzed, and the factors affecting students' academic performance are obtained. These analysis results have important reference value for the future improvement of teaching work in colleges and universities.

关键词： Data mining decision tree English scores improved algorithm

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improved butterfly optimization algorithm for CCHP driven by PEMFC

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APPLIED THERMAL ENGINEERING 2020年 173卷 114766-114766页

作者： Yuan Zhi Wang Weiqing Wang Haiyun Khodaei, Hossein Xinjiang Univ Engn Res Ctr Educ Minist Renewable Energy Power Generat & Grid Urumqi 830047 Xinjiang Peoples R China Islamic Azad Univ Dept Engn Ardabil Branch Ardebil Iran

This study presents an optimal design of a combined cooling, heating and power generation system consists of a heat recovery system, a 5 kW proton exchange membrane stack, a small absorption chiller, a humidifier, and a gas compressor based on multi-criteria assessment for apartment simultaneously. The system is analyzed in terms of economics, environment, and thermodynamics. An improved version of butterfly optimization algorithm is employed for optimizing the system performance. The system efficiency is analyzed based on annual cost, exergy and energy efficiencies, and pollutant emission reduction. Final results illustrated that high relative humidity, pressure of inlet gases, and low operating temperature develop GHG emission reduction and system exergy performance. The optimized values of annual GHG reduction and exergy efficiency are 2.67e7 g and 47.1%, respectively, meanwhile, annual cost can be decreased to 3.139e3 $.

关键词： Combined cooling, heating and power generation Proton exchange membrane improved algorithm Butterfly optimization algorithm Multi-objective optimization

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Generation of Multigroup Cross Sections with an improved Monte Carlo algorithm

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NUCLEAR SCIENCE AND ENGINEERING 2020年第1期194卷 44-55页

作者： Cheng, Li Zhong, Bin Shen, Huayun Hu, Zehua Li, Baiwen Inst Appl Phys & Computat Math Beijing 100094 Peoples R China Inst Appl Phys & Computat Math Natl Key Lab Computat Phys Beijing 100088 Peoples R China

We propose an improved algorithm of generating scattering matrices based on the Monte Carlo method. The new algorithm can greatly improve convergence compared to the traditional approach of the collision estimator. The formula for estimating statistical errors in the new algorithm is given. How the new algorithm benefits the convergence without investing large neutron samples is analyzed, and we also point out that with properly partitioned energy groups, the precision of scattering matrices can get close to that of total scattering cross sections. The new algorithm has been implemented in the neutron transport code NPTS and validated with a number of critical benchmark problems.

关键词： Multigroup cross sections Monte Carlo method improved algorithm

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Research on path planning of mobile robot in complex environment

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DISCOVER APPLIED SCIENCES 2025年第4期7卷 1-18页

作者： Liu, Haibin Cao, Jingjing Wang, Zhiyuan Hebei Univ Engn Handan Peoples R China

To address inefficiencies in search performance, slow convergence, and redundant node generation in mobile robot path planning within complex environments, this paper introduces an enhanced A* pathfinding algorithm. The proposed algorithm improves search efficiency and accuracy by segmenting the path planning process into distinct stages, applying different heuristic functions at each stage, and integrating an artificial potential field to guide traversal, reducing unnecessary node exploration. Additionally, a random escape strategy prevents the algorithm from getting trapped in local minima. Various optimization methods refine the final path for practical applications. Simulation results demonstrate that, compared to heuristic A*, potential field, Weighted A*, and D* algorithms, the improved approach significantly reduces node traversal, execution time, and enhances planning success rates, making it well-suited for complex environments.

关键词： Move robot improved algorithm Path planning Complex environment

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FAULT DIAGNOSIS MODELLING OF POWER SYSTEM CONTROLLER BASED ON PLC TECHNOLOGY

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MECHATRONIC SYSTEMS AND CONTROL 2025年第1期53卷 32-41页

作者： She, Dong Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

As the significant part of the system, the power system bears the function of power transmission and activation. Taking the ship's power system as an example, the starting and stopping of the entire ship require the power system to function. This research is aimed at the fault analysis of the ship power system. The programmable controller is used to code the algorithm parameters, and an improved algorithm model combining the multi-layer feedforward and radial basis function (RNF) neural networks is built. The new algorithm applies the least square method to calculate the weights of the hidden and the input layers, and adds the genetic algorithm to re-code the threshold of the multi-layer feedforward neural network. The research outcomes indicate that the accuracy of the output value comparison of the improved algorithm is 9% higher than that of the multi-layer feedforward neural network, and 6% higher than that of the radial basis neural network. The approximation error of the improved algorithm is 0.033 less than that of the multi-layer feedforward neural network, and the radial basis neural network is 0.019 less. The improved algorithm has higher accuracy and better precision in power systems' fault diagnosis, and the diagnostic findings are significantly better than the other two algorithms.

关键词： Power system programmable controller multi-layer feedforward neural network improved algorithm fault diagnosis precision

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Single-iterative algorithm based on first arrival waves for the forward modeling of two-dimensional tomography

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ACTA GEOPHYSICA 2024年第3期72卷 1601-1616页

作者： Lu, Jiangbo Tang, Shenghua Zhang, Guogang Zheng, Hui Lu, Lizhi Survey & Design Inst Co Ltd Hunan Prov Commun Planning Changsha 410082 Hunan Peoples R China Xiangtan Univ Coll Civil Engn & Mech Xiangtan Hunan Peoples R China Hunan Univ Technol Coll Civil Engn Zhuzhou Hunan Peoples R China

Two-dimensional tomographic forward modeling based on first arrivals requires the calculation of the minimum travel times of multiple emission points in a single iteration. The conventional method, which calculates each emission point one by one, produces many unuseful calculations and has low computational efficiency. To solve this problem, given the characteristic that the velocity distribution of a model does not change during a single iteration of forward modeling, an improved algorithm was proposed based on the reciprocity principle and Fermat's principle. The ray tracing results of a small number of emission points were used to constrain the calculation area of other emission points, which reduced the number of unuseful calculations and improved the calculation efficiency of a single-iteration tomographic forward modeling simulation. Theoretical analysis and numerical examples showed that for a homogeneous model, when the transmitting and receiving points were located on two long sides, three adjacent sides (i.e., two long sides and one short side), and four sides, the computational efficiency of the improved algorithm was about 2 times, 2 times, and 1.5 times, respectively, that of the conventional method. For heterogeneous models, the computational efficiency of the improved algorithm was usually more significant.

关键词： First arrival wave Ray tracing Forward modeling Computational efficiency improved algorithm Single iteration Seismic tomography

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Analysis of industry convergence based on improved neural network

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SOFT COMPUTING 2022年第16期26卷 7437-7448页

作者： Ma, Nan Fuzhou Univ Int Studies & Trade Fuzhou 350202 Fujian Peoples R China

Economic growth in the age of information is no longer a stage driven by unipolarity. It has entered to a multi-polar driving stage characterized by integration, fusion, and integrated development on a larger scale between regions, and the trend of group competition with urban agglomerations as carriers has become increasingly obvious. This paper improves the neural network algorithm based on the needs of industrial economic integration in the digital age and proposes an industry convergence analysis model based on the improved neural network algorithm. Moreover, this article combines industry models to analyze actual needs, constructs an industry convergence analysis model based on improved neural networks, and analyzes the integration of different industries. In addition, this article conducts experiments through multiple sets of data and combines the neural network model of this article to conduct research. Through experimental research, we know that the model constructed in this paper can play an important role in the analysis of industry convergence.

关键词： Neural network improved algorithm Industry convergence Intelligent model

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YOLOv8 algorithm Improvement and Its Application in Small Target Detection

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TRAITEMENT DU SIGNAL 2025年第2期42卷 985-994页

作者： Gao, Lu Zhao, Junwei Bohai Univ Coll Phys Sci & Technol Jinzhou 121000 Peoples R China North China Inst Sci & Technol Coll Comp Sci & Technol Langfang 065201 Peoples R China

In the trend of the continuous development of AI, the application range of small target detection (STD) is very wide. Improving the accuracy of small target detection is the focus of research, which has positive significance for the improvement of computer vision technology and multiple application scenarios. Based on the overall structure of YOLOv8 network, this paper introduces the deformable convolution and attention mechanism of DCNv4 to improve it. DCNv4 enhances the ability of the network to capture spatial structure, especially for objects of different scales or positions, so that the kernel can sample from any position in the input feature map, which can improve the performance of small target detection to a certain extent. The attention mechanism improves the ability of the network to focus on the key information in the detection task, and improves the efficiency and accuracy of the network detection. The experimental results show that the improved YOLOv8 algorithm significantly improves the detection performance of small targets, and achieves a good balance between detection accuracy and computational efficiency.

关键词： YOLOv8 small target detection (STD) improved algorithm DCNv4 deformable convolution attention mechanism

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