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algorithms to Model and Optimize a Stand-Alone Photovoltaic-Diesel-Battery System: An Application in Rural Libya

TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第2期28卷 523-529页

作者： Elbaz, Abdurazaq Tahir Guneser, Muhammet Karabuk Univ Engn Fac Elect & Elect Dept TR-78100 Karabuk Turkey

This paper introduces a new optimum calculation technique for a stand-alone hybrid photovoltaic-diesel-battery system (PDBS), which meets the energy requirements of a small village in southern Libya. The bat algorithm design strategy is applied to reduce the annual cost of the system, taking into consideration the controlled electricity restriction and the optimal numbers of PV panels, diesel generators, and batteries. Comparative tests are performed using MATLAB for the bat algorithm with the grey wolf search algorithm and particle swarm optimization, demonstrating that the bat algorithm determines the optimum size of the PDBS effectively at a lower expense. Results then indicate that, taking into account the reliability characteristics, this has a significant effect on optimum capacity, load supply, and cost.

关键词： annual system cost bat algorithm grey wolf optimization algorithm particle swarm optimization algorithm photovoltaic-diesel-battery system

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Research on error analysis and calibration method of 3-PUU parallel robot

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2024年

作者： Chen, Mingfang Liang, Hongjian He, Chaoyin Zhang, Yongxia Huang, Liang'en Kunming Univ Sci & Technol Fac Mech & Elect Engn 727 Jingming South RdWujiaying St Kunming 650500 Yunnan Peoples R China

Kinematic calibration is necessary for the pose accuracy improvement of the parallel robots. However, difficulties occur in traditional calibration methods, such as exceeding a large number of error parameters and error accumulation. In this paper, a 3-PUU parallel robot is taken as the research object, and the closed-loop vector method and differential theory are employed to establish an error model. Through quantitative analysis of geometric parametric errors, redundant parameters within the calibration algorithm are meticulously eliminated. By treating the dominant error terms as optimization variables for the mechanism's parameters, the calibration problem is transformed into a nonlinear system optimization challenge, which effectively avoids the problem of multiple error parameters and accumulation of errors in the traditional method. Further, using the particle swarm algorithm to compute the minimum value of the objective function, one can obtain the actual structural parametric errors of the robot. These errors are then utilized to correct the kinematic model, enabling the calibration of the mechanism's parameters and ultimately enhancing operation accuracy. Lastly, simulation and experimental validation of the algorithm are carried out. The simulation results indicate that the end-effector position error converges to zero infinitely;and the experimental results indicate that the maximum error of the end-effector in the x, y, z directions and the maximum position error are reduced from 8.53, 11.67, 3.29, and 12.56 mm to 1.09, 1.32, 0.98, and 1.75 mm, respectively. The standard deviation of the position error is reduced from 2.43 to 0.32 mm. The mean error is reduced from 7.76 to 1.02 mm. To sum up, the operation accuracy and stability of the robot are greatly improved.

关键词： Parallel robot error model kinematic calibration particle swarm optimization algorithm pose accuracy

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Testing study of creep model for tailing cemented backfill and its secondary development based on FLAC^3D

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ROCK AND SOIL MECHANICS 2012年第SUPPL. 1期33卷 112-116页

作者： Zhao Kui He Wen Xiong Liang-xiao Yang Xin Wang Xiao-jun Jiangxi Univ Sci & Technol Engn Res Inst Ganzhou 341000 Jiangxi Peoples R China Jiangxi Univ Sci & Technol Minist Educ Engn Res Ctr High Efficiency Dev & Applicat Tech Ganzhou 341000 Jiangxi Peoples R China Ningbo Univ Fac Architectural Civil Engn & Enviroment Ningbo 315211 Zhejiang Peoples R China Chongqing Univ Coll Civil Engn Chongqing 400030 Peoples R China Jiangxi Univ Sci & Technol Sch Resources & Environm Engn Ganzhou 341000 Jiangxi Peoples R China

Great many results about the creep properties of rock have been achieved, while few reports on the cemented backfill's creep characters were obtained. In this paper, creep property of cemented backfill is characterized by Hoek-Kelvin model. Cemented backfill creep experiments are firstly conducted in the laboratory, then the parameters of Hoek-Kelvin creep model are identified using particle swarm optimization algorithm. Meanwhile, the sensitivity of creep parameters to the stress level is studied. Results show that the numerical value of EH and. vary little at different loading stress levels, and EK is very sensitive to the change of loading stress level. The proposed creep model for backfill is developed with the software FLAC(3D);and it has been used to numerically simulate multi-stage loading on cemented backfill. The numerical results agree well with the experimental ones.

关键词： cemented backfill creep model particle swarm optimization algorithm FLAC(3D)

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Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval optimization

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ENERGIES 2024年第15期17卷 3623页

作者： Su, Lei Wu, Wenxiang Feng, Wanli Qin, Junda Ao, Yuqi State Grid Hubei Elect Power Res Inst Wuhan 430074 Peoples R China State Grid Smart Grid Res Inst Co Ltd State Key Lab Adv Power Transmiss Technol Beijing 102209 Peoples R China

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method.

关键词： data center uncertainty interval optimization collaborative planning particle swarm optimization algorithm gravitational search algorithm

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Design and key technology of the energy consumption management system for the liquid cooling data center

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ENERGY SCIENCE & ENGINEERING 2023年第3期11卷 1284-1293页

作者： Lai, Yibo Fang, Xiang Han, Rongjie Xuan, Yi Huang, Jiabin Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou 310009 Peoples R China

In view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.

关键词： energy consumption management system energy consumption sampling circuit liquid cooling data center particle swarm optimization algorithm resource configurator

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Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty

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MECHANICS & INDUSTRY 2021年第1期22卷

作者： Kang, Jianxiong Lu, Yanjun Luo, Hongbo Li, Jie Hou, Yutao Zhang, Yongfang Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Univ Technol Sch Printing Packaging Engn & Digital Media Techn Xian 710048 Peoples R China

The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessment model is proposed based on the support vector regression, and the fuzzy uncertainty is modeled to describe the random behavior under small sample. To verify the proposed model, the sample data of cylinder liner wear is applied. For best results, the particle swarm optimization (PSO) algorithm is used to optimize the model parameters. A back propagation neural network (BPNN) is employed to verify the effectiveness of the proposed model. The results show that the novel support vector regression has better prediction accuracy than other methods for cylinder wear in this paper, the proposed model can evaluate the cylinder liner wear of the ICEs effectively. The work provides a technical support for evaluating the service performance of the piston ring-cylinder liner and a reference for regular maintenance of the ships.

关键词： Wear assessment cylinder liner support vector regression fuzzy uncertainty particle swarm optimization algorithm

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A fuzzy logic-based method for solving the scheduling problem in the cloud environments using a non-dominated sorted algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第17期31卷 e5185-e5185页

作者： Wakil, Karzan Badfar, Arshad Dehghani, Pooyan Sadati, Seyed Mojtaba Shoja Navimipour, Nima Jafari Sulaimani Polytech Univ Res Ctr Sulaimani Iraq Islamic Azad Univ Dept Comp Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Ind Management South Tehran Branch Tehran Iran Shahid Beheshti Univ Elect Engn Dept Tehran Iran Islamic Azad Univ Islamshahr Branch Young Researchers & Elite Club Islamshahr *** Iran

Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta-heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non-dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta-heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution. Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms.

关键词： cloud computing multi-objective optimization particle swarm optimization algorithm task scheduling

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Optimized Control Strategy for Photovoltaic Hydrogen Generation System with particle swarm algorithm

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ENERGIES 2022年第4期15卷 1472-1472页

作者： He, Hongyang Lu, Zhigang Guo, Xiaoqiang Shi, Changli Jia, Dongqiang Chen, Chao Guerrero, Josep M. Yanshan Univ Elect Engn Dept Qinhuangdao 066000 Hebei Peoples R China Chinese Acad Sci Inst Elect Engn Beijing 100190 Peoples R China State Grid Beijing Elect Power Co Beijing 100051 Peoples R China State Grid Jiaxing Elect Power Supply Co Jiaxing 314033 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

关键词： particle swarm optimization algorithm hybrid energy storage hydrogen generation system optimized energy storage capacity configuration

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Two-stage hybrid model for hydrological series prediction based on a new method of partitioning datasets

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JOURNAL OF HYDROLOGY 2022年第PartA期612卷

作者： Xu, Hanbing Song, Songbai Guo, Tianli Wang, Huimin Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Area Minist Educ Yangling 712100 Peoples R China

Obtaining accurate point estimates and reliable interval prediction results for rainfall and runoff series is important to aid in water resource decision-making and planning management in a changing environment. In this paper, we propose a two-stage hybrid model (P-CVEE-LDBNN). The model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods for data preprocessing and a Bayesian neural network (LDBNN) based on Langevin dynamics as a prediction model. Then, a new dataset partitioning method based on a particle swarm optimization algorithm, which is different from the traditional dataset partitioning method, is applied. A one-stage hybrid model (T-CEELDBNN) that integrates the CEEMDAN method and the traditional method of partitioning datasets, a one-stage hybrid model (P-CEE-LDBNN) that integrates the CEEMDAN method and the new method of partitioning datasets, and the two-stage hybrid P-CVEE-LDBNN model were compared. These models were applied to monthly runoff and monthly precipitation series from seven hydrological and meteorological stations in the Yellow River basin. The mean absolute error (MAE), absolute root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R) were used to evaluate the predictive ability of the models. The containment ratio (CR), the average bandwidth (IW), and the average asymmetry degree (S) were used to evaluate the interval prediction results of the models. The results show that (1) the subset obtained with the new method of data partitioning considering statistical properties is more favorable for model prediction. (2) The second decomposition approach based on VMD is a reliable method that can significantly improve the prediction accuracy of the final model. (3) Compared with traditional neural networks that can only obtain deterministic point prediction results, Bayesian methods can provide intervals with prediction resu

关键词： Two -stage hybrid model Langevin dynamics Bayesian neural networks Data portioning particle swarm optimization algorithm Yellow River basin

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船用固体氧化物燃料电池与二冲程低速双燃料发动机集成动力系统的设计与优化

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哈尔滨工程大学学报（英文版） 2023年第4期22卷 837-849页

作者：曲金博冯永明吴云金朱元清邬斌扬肖忠旭 College of Power and Energy Engineering Harbin Engineering UniversityHarbin 150001China State Key Laboratory of Engines Tianjin UniversityTianjin 300072China

A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this ***,a 0-D model of SOFC and a 1-D model of ICE are built as agent ***,parameter analysis of the system is conducted based on SOFC and ICE *** show that the number of cells,current density,and fuel utilization can influence SOFC and ***,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this *** results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power ***,promising improvements in efficiency of 5.1%are achieved compared to the original ***,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.

关键词： Combined system SOFC-ICE integrated cycle Data-driven model particle swarm optimization algorithm

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