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Symmetric circular-shaped multiband hybrid fractal antenna using TLBO approach: design and measurement

INTERNATIONAL JOURNAL OF ELECTRONICS 2022年第8期109卷 1443-1460页

作者： Kaur, Manpreet Sivia, Jagtar Singh Kaur, Navneet Punjabi Univ Guru Kashi Campus YCoE Talwandi Sabo India Punjabi Univ Patiala Punjab India

This paper describes the multiband behaviour of several circular-shaped hybrid fractal antennas. Initially, a circular-shaped fractal antenna (antenna-A) is designed and its performance is scrutinised. Afterwards, hybrid fractal approach is utilised in the design process to enhance the operational performance and the resultant structure is named as circular-shaped hybrid fractal antenna (antenna-B). The volumetric dimensions exhibited by the antennas are 38 x 38 x 1.6 mm(3). The ground plane dimension 'L-PG' has been optimised specifically with a recently developed teaching-learning-based optimisation algorithm. Fabrication and testing of antenna-B is done in order to justify the proposed strategy. Measured results depict that the fabricated antenna operates at six fundamental resonances with acceptable gain values. A bandwidth of 1.97%, 7.47%, 5.46%, 4.04%, 4.37% and 4.53% is evaluated at the respective bands. Radiation patterns provided by antenna-B are arbitrary bidirectional/ omnidirectional. In addition, the influence of specific design patterns on the performance of antenna-B is studied. Experimental measurements are presented and compared with the simulated ones. From practical outcomes, it is suggested that the proposed antenna-B can be useful for several wireless communication applications such as Satellite communication for downlink, Fixed satellite communication, Radiolocation, Direct broadcast satellite communication and Radars.

关键词： Hybrid fractal antenna multiband behaviour symmetrical circular fractal patterns teaching-learning-based optimisation radiation pattern wireless applications

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Digital twin-based production logistics resource optimisation configuration method in smart cloud manufacturing environment

IET COLLABORATIVE INTELLIGENT MANUFACTURING

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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2024年第4期6卷

作者： Zhang, Zhongfei Qu, Ting Zhang, Kai Zhao, Kuo Zhang, Yongheng Liu, Lei Liang, Jianhua Huang, George Q. Jinan Univ Sch Management Guangzhou Peoples R China Jinan Univ Guangdong Int Cooperat Base Sci & Technol GBA Smar Zhuhai Peoples R China Jinan Univ Inst Phys Internet Zhuhai Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai Peoples R China Jinan Univ Zhuhai Res Inst Zhuhai Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen Peoples R China Super Telecom Co Ltd Guangzhou Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

To adapt to the dynamic, diverse, and personalised needs of customers, manufacturing enterprises face the challenge of continuously adjusting their resource structure. This has led manufacturers to shift towards a smart cloud manufacturing mode in order to build highly flexible production logistics (PL) systems. In these systems, the optimal configuring of PL resources is fundamental for daily logistics planning and vehicle scheduling control, providing necessary resources for the entire PL segment. However, traditional resource configuration methods face limitations, such as incomplete information acquisition, slow response in resource configuration, and suboptimal configuration results, leading to high subsequent operational costs and inefficient logistics transportation. These issues limit the performance of the PL system. To address these challenges, the authors propose a digital twin-based optimisation model and method for smart cloud PL resources. The approach begins with constructing an optimisation model for the PL system considering the quality of service for a cloud resource is constructed, aiming to minimise the number of logistics vehicles and the total cost of the PL system. Additionally, a DT-based decision framework for optimising smart cloud PL resources is proposed. Alongside a DT-based dynamic configuration strategy for smart cloud PL resources is designed. By developing a multi-teacher grouping teaching strategy and a cross-learning strategy, the teaching and learning strategies of the standard teaching-learning-based optimisation algorithm are improved. Finally, numerical simulation experiments were conducted on the logistics transportation process of a cooperating enterprise, verifying the feasibility and effectiveness of the proposed algorithms and strategies. The findings of this study provide valuable references for the management of PL resources and algorithm design in advanced manufacturing modes.

关键词： cloud manufacturing digital twin optimal configuration production logistics resource teaching-learning-based optimisation

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Modelling of magnetostrictive vibration and acoustics in converter transformer

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IET ELECTRIC POWER APPLICATIONS 2021年第3期15卷 332-347页

作者： Yadav, Suman Mehta, Ram Krishna N Eastern Reg Inst Sci & Technol Dept Elect Engn Nirjuli Arunachal Prade India

The converter transformers are susceptible to more noise and vibration when compared to power transformers due to the presence of DC bias in the DC transmission line. DC bias occurs mostly due to inaccuracies in valve firing resulting in a small residual DC oscillating around zero. Measurement of magnetostriction becomes significant as it influences the vibration and noise from the core. Hence, a magnetostrictive model of a high-voltage DC converter transformer has been developed. This work analyses the vibration and noise acoustics under such an occurrence. First, the core of the transformer model is designed in the stepped configuration for 240 MVA;then, magnetostrictive vibration is analysed by using suitable modules of COMSOL Multiphysics at different magnitudes of DC bias. The physics of noise has been interfaced using the Acoustics Module, and the results are recorded. Finally, artificial neural network model is developed for the prediction of vibration and noise characteristics of the model. The fitting process of neural network was then remodelled using various optimisation techniques, namely teaching-learning-based optimisation, particle swarm optimisation, biogeography-based optimisation, simulated annealing and binary coded genetic algorithm, and their results were compared to obtain the best-suited method using % mean-squared-error evaluation.

关键词： optimisation techniques mean-squared-error evaluation particle swarm optimisation artificial neural network model power engineering computing DC transmission line Transformers and reactors binary coded genetic algorithm apparent power 240.0 MVA DC transformers neural nets power transformers noise characteristics biogeography-based optimisation Power convertors and power supplies to apparatus COMSOL multiphysics DC bias power convertors genetic algorithms high-voltage DC converter transformer magnetostrictive vibration mean square error methods Interpolation and function approximation (numerical analysis) Power engineering computing noise acoustics simulated annealing residual DC acoustics module teaching-learning-based optimisation

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Exergy-based optimisation of a phase change materials integrated hybrid renewable system for active cooling applications using supervised machine learning method

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SOLAR ENERGY 2020年 195卷 514-526页

作者： Tang, Liang Zhou, Yuekuan Zheng, Siqian Zhang, Guoqiang Hunan Univ Coll Architecture Changsha 410082 Hunan Peoples R China Changsha Univ Dept Civil Engn Changsha 410022 Peoples R China Hong Kong Polytech Univ Fac Construct & Environm Dept Bldg Serv Engn Hong Kong Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Hunan Univ Natl Ctr Int Res Collaborat Bldg Safety & Environ Changsha 410082 Hunan Peoples R China Hunan Univ Coll Civil Engn Changsha 410082 Hunan Peoples R China

The active cooling, latent thermal storage and advanced energy conversions are effective solutions to high-efficiently utilise renewable energy for building applications, whereas the electricity consumption of active facilities for the thermal performance enhancement needs to be considered. In this study, the exergy analysis of a hybrid renewable system, with on-site thermal and electric energy forms, sensible and latent heat storages, was investigated, in terms of technical feasibility of proposed active cooling solutions. The contradiction between the increased electricity consumption of active cooling facilities and the enhancement of renewable generations has been presented, discussed, together with effective solutions, from the perspective of exergy. A machine-learning based optimisation methodology was proposed and used, to address the contradiction and to maximise the overall exergy, with the integration of an advanced optimisation algorithm. The results showed that, in regard to the contradiction, effective solutions include the active water-based cooling and the optimal design of the geometric and operating parameters. Furthermore, with the adoption of optimal parameters through the machine-learning based optimisation, the overall exergy of the hybrid renewable system is 872.06 kWh, which is 2.6% higher than the maximum overall exergy through the Taguchi standard orthogonal array (849.9 kWh). This study demonstrates an effective solution to the contradiction of an active renewable system, together with a machine-learning based optimisation methodology, which can promote the practical feasibility and applicability of active renewable systems in renewable and sustainable buildings.

关键词： Exergy analysis Phase Change Materials (PCMs) Sensible and latent storage On-site thermal and electric energy Machine learning teaching-learning-based optimisation

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Stochastic uncertainty-based optimisation on an aerogel glazing building in China using supervised learning surrogate model and a heuristic optimisation algorithm

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RENEWABLE ENERGY 2020年 155卷 810-826页

作者： Zhou, Yuekuan Zheng, Siqian Hong Kong Polytech Univ Fac Construct & Environm Dept Bldg Serv Engn Hong Kong Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Hunan Univ Coll Civil Engn Minist Educ Key Lab Bldg Safety & Energy Efficiency Changsha 410082 Hunan Peoples R China

Scenario parameters of aerogel glazing systems are with uncertainties in the real operation, whereas current literature fails to characterise the thermal and energy responses regarding stochastic scenario uncertainties. Furthermore, multi-level uncertainty-based optimisation has been rarely studied for the robustness improvement. In this study, a general method for stochastic uncertainties-based optimisation is proposed. A machine-learning based surrogate model is developed for uncertainty analysis. Furthermore, a multi-level uncertainty-based optimisation function is characterized and integrated with the heuristic teaching-learning-based algorithm to search for the optimal design. Research results indicated that, in the multi-level uncertainty-based optimal scenario, average values of RoC, thickness of aerogel layer, extinction coefficient and thermal conductivity are 306253.4 J/(K m(3)), 24.5 mm, 0.092, and 0.0214 W/(m K). Compared to the deterministic case, the stochastic uncertainty case can decrease the heat flux from 237.16 to 190 kWh/m(2) .a by 19.9%, and total heat gain from 267.18 to 222.04 kWh/m(2).a by 16.9%. Furthermore, by adopting the multi-level uncertainty-based optimisation, the heat flux can be further reduced to 162.54 kWh/m(2).a by 31.5%, and the total heat gain to 191.56 kWh/m(2).a by 28.3%. The proposed technique can improve the reliability of aerogel glazing systems in green buildings. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Aerogel glazing Multi-level uncertainty-based analysis Supervised machine-learning Optimal design and robust operation teaching-learning-based optimisation

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Accurate and comprehensive fault location algorithm for two-terminal transmission lines

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第19期12卷 4334-4340页

作者： Poudineh-Ebrahimi, Farhad Ghazizadeh-Ahsaee, Mahdi Univ Zabol Fac Engn Elect Engn Dept Zabol Iran

In this study, a novel accurate fault location algorithm is presented for two-terminal transmission lines. In contrast to conventional methods, the proposed algorithm not only utilises asynchronous samples recorded during the fault but also needs no line parameters and identification of fault type. In the presented fault-locating method, distributed parameter line model in the time domain and asynchronous data of the terminals collected during fault are applied. Fault locating as an optimisation problem has been solved by the heuristic algorithm of teaching-learning-based optimisation, and the decision variables of fault location, synchronisation time and line parameters are estimated simultaneously. The performance of the presented method was tested with different fault incidence angles, a variety of fault types, and under several system and fault conditions using the MATLAB/Simulink. These tests demonstrate the high accuracy of the presented method. Also, the proposed method did not show any dependence on the impedance of the Thevenin sources of the two sides of the line, the fault impedance and the fault incidence angle. Furthermore, it was not affected by the high resistance of the fault and the network structure.

关键词： fault location optimisation power transmission faults comprehensive fault location algorithm two-terminal transmission lines distributed parameter line model heuristic algorithm teaching-learning-based optimisation fault impedance fault incidence angles accurate fault location algorithm time domain asynchronous data synchronisation time Matlab-Simulink Thevenin sources

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A new hybrid algorithm for unconstrained optimisation problems

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第3期46卷 187-194页

作者： Huang, Jida Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

A new optimisation algorithm which hybridises cuckoo search (CS) with teachinglearning-based optimisation (TLBO) is proposed for solving unconstrained optimisation problems. The new algorithm involves the concept of Lvy flight of the solutions and the information exchange based on teaching-learning process between the best solutions. The proposed method, combining the advantage of CS and TLBO, can strengthen the local search ability and accelerate the convergence rate. The effectiveness and performance of the method is evaluated on several large scale non-linear benchmark functions with different characteristics, and the results are compared with CS and TLBO. The experimental results show that the proposed algorithm outperforms other two algorithms and has achieved satisfactory improvement.

关键词： cuckoo search teaching-learning-based optimisation TLBO hybrid algorithm unconstrained optimisation problems

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