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Single Server Batch Arrival Bernoulli Feedback Queueing System with Waiting Server, K-Variant Vacations and Impatient Customers

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Operations Research Forum 2021年第1期2卷 14页

作者： Bouchentouf, Amina Angelika Guendouzi, Abdelhak Laboratory of Mathematics Djillali Liabes University of Sidi Bel Abbes Sidi Bel Abbes 22000 Algeria Laboratory of Stochastic Models Statistic and Applications University of Saida Dr. Moulay Tahar. B. P. 138 En-Nasr Saida Algeria

We consider an infinite capacity batch arrival single server Markovian Bernoulli feedback queueing system with waiting server, K-variant vacations, impatient customers and retention of reneged customers. The model is analyzed using probability generating function (PGF) technique. Various queueing system characteristics are derived. Then, by setting the appropriate parameters, some special cases are discussed. Moreover, a cost model for the queueing system is developed. The parameter optimization is numerically illustrated using particle swarm optimization (pso). Finally, numerical results are provided to explore the impact of system parameters on performance measures and costs of the queueing system. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.

关键词： Bernoulli feedback Impatient customers Optimization Probability generating function pso algorithm Variant multiple vacations

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Research on Optimization Method of High Speed Permanent Magnet Synchronous Motor Based on Surrogate Model 24

Research on Optimization Method of High Speed Permanent Magn...

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24th International Conference on Electrical Machines and Systems (ICEMS)

作者： Bi, Chengjiang Xu, Zhenyao Xie, Bingchuan Zhang, Fengge Shenyang Univ Technol Sch Elect Engn Shenyang Peoples R China

ISBN: (纸本)9788986510218

High speed permanent magnet synchronous motor (HSPMSM) is a typical nonlinear device. The high-accuracy finite element analysis of the HSPMSM may take a lot of computer resources, so the optimization process of the HSPMSM is always time-consuming. Therefore, in this paper, the surrogate model is applied to replace the finite element (FE) model, which could simplify the optimization process. Besides, the model is combined with particle swarm optimization (pso) algorithm optimize the motor. And in the process of establishing the optimization model, a combined surrogate model is proposed, and the accuracy of the model is compared with four different single typical surrogate models. Finally, the FE analysis of the results before and after optimization is carried out to verify the validity of the above method.

关键词： Optimization design HSPMSM Surrogate model pso algorithm

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Energy, exergy, economic, and exergoeconomic analyses and optimization of a solar Kalina cycle using particle swarm optimization algorithm

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ENERGY CONVERSION AND MANAGEMENT-X 2023年 18卷

作者： Kojur, Nima Bahreini Namdar, Mehdi Nasero, Mehdi Jabareh Aminian, Saman Koosha, Naser Zarei, Kavan Islamic Azad Univ Dept Mech Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Mech Engn Neyriz Branch Fars Iran Shiraz Univ Sch Elect & Comp Engn Shiraz Iran Tarbiat Modares Univ Fac Mech Engn Tehran Iran Univ Kurdistan Dept Mech Engn Sanandaj Iran

In the present research, a Kalina cycle has been analyzed from the energy, exergy, economic and exergoeconomic points of view. In this cycle, the water-ammonia solution is used as the working fluid. Firstly, the mass balance, energy, and exergy equations are solved, then the initial cost and exergy destruction cost have been calculated using the cost balance equations in different components. Finally, the optimization of solar Kalina's performance in terms of target functions, i.e., total cost rate and exergy efficiency, has been performed. To this end, a relationship between design parameters and target functions is derived using the neural network algorithm;then, the multiobjective optimization of the cycle is performed using the particle swarm optimization (pso) algorithm. The results show that total work, net irreversibility, and total exergy efficiency in the base case are 296.2kW, 3671kW, and 0.0738, respectively. The exergoeconomic analysis indicates that the total cost rate was 366 $h, and it was found that the solar collector and Kalina evaporator have respectively the maximum cost rate. The parametric analysis has been performed in terms of energy, exergy, and exergoeconomic to examine the effect of turbine's inlet temperature and pressure, and ammonia content on the cycle performance. The optimization results indicate that exergy efficiency and total cost rate are 0.08824, and 318.58 $h, respectively. This represents an 19.5% increase in the exergy and a 12.95% reduction in cost rate compared to the base case.

关键词： Solar energy Kalina cycle Exergoeconomic analysis pso algorithm Neural network algorithm

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An Optimized Particle Swarm Optimization(pso) algorithm Smoothing the Output of Wind Power

An Optimized Particle Swarm Optimization(PSO) Algorithm Smoo...

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2015 International Conference on Computer Information Systems and Industrial Applications(CISIA2015)

作者： X.Y.Zhang N.Zhu M.L.Yuan J.Y.Xu H.T.Dai Guodian United Power Technology Company LTD

ISBN: (纸本)9781510805750

The output power of wind turbine caused by the variation of wind fluctuates badly,which causes instability of the Power *** smooth the output of wind power to Grid,an Energy Storage System(ESS) solves this problem by absorbing and releasing the wind turbine *** smooth performance depends on the State of Charge(SOC) of the *** the SOC of the ESS,an optimized Particle Swarm Optimization(pso) algorithm is designed in this paper,where the SOC of the ESS is the boundary condition.A penalty mechanism is designed to ensure the wind power system's *** wind power system with ESS based on MATLAB is *** optimized pso algorithm is used to control output wind *** simulation results verified it.

关键词： wind power smoothing Energy Storage System(ESS) pso algorithm

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Application of Particle Swarm Optimization algorithm for Scheduling Of Capacitor Bank Switching To Improve Voltage

Application of Particle Swarm Optimization Algorithm for Sch...

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IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)

作者： Delfianti, Rezi Penangsang, Ontoseno Soeprijanto, Adi Rohiem, Nasyith Hananur Putra, Novian Patria Uman Suheta, Titiek Inst Teknol Sepuluh Nopember Dept Elect Engn Surabaya Indonesia

ISBN: (纸本)9781665442930

One of the most critical aspects of power system research is voltage stability. One solution for maintaining the power factor value in compliance with the set of specifications and producing a voltage value that meets the device requirements determines the size and placement of capacitor banks. Changing load demand necessitates the use of capacitor banks to be switched on or off. The most critical aspect in achieving optimum capacitor bank utilization is the switching arrangement. The Particle swarm optimization algorithm is used in this study to determine the size, placement, and switching on or off the capacitor banks in the Surabaya distribution system. The switching will be easier if the capacitor banks required at specific hours can be minimized. Therefore, we can extend the capacitor bank's lifetime and optimize its usage. Besides, switching on or off the capacitor banks will increase the quality of voltage and provide pricing efficiency. This study is a simulation carried out with the aid of Matlab Software.

关键词： sizing and placement capacitor bank switching pso algorithm voltage

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Adaptive Noise Canceller Design Based on Chaotic Simulated Annealing Particle Swarm Optimization algorithm 15

Adaptive Noise Canceller Design Based on Chaotic Simulated A...

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15th International Symposium on Medical Information and Communication Technology (ISMICT)

作者： Zhang, Jie Wen, Peng Cheng Shen, Yan Chengdu Univ Informat Technol Sch Automat Engn Chengdu Peoples R China Chengdu Univ Informat Technol Sch Comp Sci Chengdu Peoples R China

ISBN: (纸本)9781728177243

The traditional filter design based on Least Mean Square (LMS) algorithm which depends on step heavily, good performance cannot be achieved in some models. To solve this problem, mathematical model based on the searching lb,optimization coefficients of the filter is constructed, three improved algorithms based on the basic Particle Swarm Optimization(pso) algorithm including Simulated Annealing pso(SApso), Chaotic Local Search pso(CLSpso)and Chaotic Simulated Annealing pso(CSApso)are applied to this problem. The simulation result shows that the CSApso algorithm can obtain optimization values efficiently and has better performance of convergence and effectiveness.

关键词： pso algorithm Simulated Annealing chaos noise cancellation filter design

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Particle Swarm Optimization algorithm Presented in SysML and Applied in Multi-UAV System

Particle Swarm Optimization Algorithm Presented in SysML and...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Aljehani, Maher Inoue, Masahiro Yokemura, Taketoshi Shibaura Inst Technol Grad Sch Engn & Sci Saitama Japan

ISBN: (纸本)9781728197661

The use of unmanned aerial vehicles (UAVs) is expanding fast in both military and civilian roles. In this paper, authors propose a Particle Swarm Optimization (pso) algorithm to evaluate Multi-UAV on different airframes. SysML activity diagram was used to depict the sequence process of the pso algorithm. Moreover, the UAV airframes are presented and discussed, then an evaluation of the performance of the selected airframes is simulated.

关键词： UAV pso algorithm SysML analysis simulation

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Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the pso-ALS-optimized GRU network

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ENERGY 2021年 233卷 121082-121082页

作者： Wang, Jun Cao, Junxing Yuan, Shan Cheng, Ming Chengdu Univ Technol State Key Lab Oil & Gas Reservoir Geol & Exploita Chengdu 610059 Peoples R China Chengdu Univ Technol Sch Geophys Chengdu 610059 Sichuan Peoples R China Chengdu Univ Technol Sch Earth Sci Chengdu 610059 Sichuan Peoples R China

With the continuous growth of the global natural gas trade, the accurate prediction of natural gas prices has become one of the most critical issues in the planning and operation of public utilities. In order to further improve the prediction accuracy of natural gas prices, we have developed a novel hybrid method based on the CEEMDAN-SE (complete ensemble empirical mode decomposition with an adaptive noise sample entropy) and the pso-ALS-GRU (gated recurrent unit network optimized by the particle swarm optimization algorithm with an adaptive learning strategy (pso-ALS)) for predicting the natural gas prices. The proposed approach can address the limitations of the traditional forecasting approaches and perform accurate predictions. First, the original natural gas price series is decomposed into a series of sub-sequences with obvious differences in a complex degree by using the CEEMDAN-SE. Then, the forecasting model pso-ALS-GRU is developed, and each sub-sequence is individually predicted. Finally, the prediction results of each sub-sequence are superimposed and reconstructed in order to form the overall forecast result. This hybrid model combines the methodology of complex systems with deep learning techniques, making it more appropriate for analyzing relationships such as long-term dependences and solving complex nonlinear problems. By finding the key hyperparameters in the GRU network using the pso-ALS, the data feature of natural gas prices matches the network topology structure, and the prediction accuracy of the model is improved. For illustration and verification purposes, the simulation is performed by using real data. The results show that the novel hybrid model can accurately predict the weekly prices of natural gas. In a comparison of prediction errors with other individual models, the proposed model demonstrates the highest prediction ability among all of the investigated models. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Deep learning CEEMDAN-SE decomposition pso algorithm Adaptive learning strategy GRU neural networks Natural gas price forecasting

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Developing a pso-Based Projection algorithm for a Porosity Detection System Using X-RayCT Images of Permeable Concrete

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IEEE ACCESS 2018年 6卷 64406-64415页

作者： Hsieh, Yi-Zeng Su, Mu-Chun Chen, Jieh-Haur Badjie, Bevan Annuerine Su, Yu-Min Natl Taiwan Ocean Univ Dept Elect Engn Keelung 20224 Taiwan Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan 32001 Taiwan Natl Cent Univ Inst Construct Engn & Management Taoyuan 32001 Taiwan Natl Kaohsiung Univ Appl Sci Dept Civil Engn Kaohsiung 80778 Taiwan

Permeable concrete is widely used as a road surfacing material due to its sturdiness and ability to be quickly repaired. The porosity of concrete has been used as a predictive indicator for the properties of the concrete. Traditional methods for measuring this porosity are feasible but can be time-consuming. In this paper, we propose a particle swarm optimization-based projection algorithm for visualization of the high-dimensional data as a 2-D scatter plot for detecting porosity in permeable concrete from X-ray computerized tomography images. We regard the proposed projection algorithm as an improved version of Sammon's nonlinear mapping. The projected scatter plot allows for a straightforward analysis of the inherent structure of clusters within scanned images. Several data sets, including artificial data sets and real-life imaging data, were tested to demonstrate the performance of the proposed projection algorithm. The model created in this paper can augment the traditional methods for examining porosity by providing visual images for decision makers to make correct decisions for future problems. With an accuracy of >99%, the visualized images provide a clearer understanding of the inner structure of pervious concrete and enhance the study of the correlation between the properties of the concrete.

关键词： Cluster analysis projection algorithm pso algorithm X-ray computerized tomography (CT) automation computational intelligence

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Coordinated Robust Control Scheme for Rotor angle and Voltage Oscillations Damping via STATCOM based on pso 28

Coordinated Robust Control Scheme for Rotor angle and Voltag...

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28th Iranian Conference on Electrical Engineering (ICEE)

作者： Kashani, Mahsa Alfi, Alireza Shahrood Univ Technol Fac Elect & Robot Engn Shahrood Iran

ISBN: (纸本)9781728172965

STATCOM is a controllable reactive current source applied to control reactive flow in transmission lines and compensate reactive power in power system nodes. This paper gives a novel robust control scheme for designing STATCOM controller to enhance the stability of the power gird. In order to mitigate oscillations, the coordinated controller is carried out both speed and voltage control loop. The proposed scheme is defined as an optimization problem, and the robustness criteria are used for evaluating the controller stability and performance. Also, Single Machin Infinite Bus (SMIB) system are applied to analyze the dynamic stability. The employed controller is a PID controller and its coefficients are tuned by pso algorithm. The simulation results show that the proposed controller extremely obtains significant damping characteristics during the operation process.

关键词： Power system stability STATCOM PID controller pso algorithm

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