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Simulation of global MPPT based on teaching-learning-based optimization technique for partially shaded PV system

ELECTRICAL ENGINEERING 2017年第3期99卷 847-859页

作者： Rezk, Hegazy Fathy, Ahmed Menia Univ Fac Engn Elect Engn Dept Al Minya Egypt Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt

The power against voltage curve for PV power system during shadow condition contains number of local maximum power points (MPPs) and only one global. The classical maximum power point tracking (MPPT) algorithms are designed to follow the global MPP, but they stuck around local MPPs such as fuzzy logic controller (FLC). Therefore, A global MPPT based on teaching-learning-based optimization (TLBO) algorithm has been presented in this paper. The performance of PV system under abnormal conditions such as partial shading has been improved. TLBO algorithm is simple computational steps and faster convergence to optimal solution. A comprehensive assessment of TLBO-based tracker is carried out against FLC and particle swarm optimization (PSO) techniques for same conditions. Six different partial shading patterns have been employed to investigate TLBO performance using MATLAB/Simulink. The parameters of comparison include the tracking speed and overall tracking efficiency. The results confirm that TLBO-based tracker exactly convergence to global MPP under different studied cases. TLBO has best performance compared to the other studied techniques. The tracking speed is increased using TLBO-based tracker;the average tracking time of global MPP is reduced by more than 23.8 % compared with PSO in all studied different partial shading patterns.

关键词： teaching-learning-based optimization Particle swarm optimization Global MPPT Partial shading Fuzzy logic control

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Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

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ENERGY CONVERSION AND MANAGEMENT 2017年第Aug.期145卷 233-246页

作者： Yu, Kunjie Chen, Xu Wang, Xin Wang, Zhenlei East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Photovoltaic model Parameter identification teaching-learning-based optimization learning strategy

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An improved teaching-learning-based optimization algorithm and itsapplication to a combinatorial optimization problem in foundry industry

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APPLIED SOFT COMPUTING 2017年 57卷 504-516页

作者： Ji, Xiaoyuan Ye, Hu Zhou, Jianxin Yin, Yajun Shen, Xu Huazhong Univ Sci & Technol Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Peoples R China

teaching-learning-based optimization (TLBO) algorithm is a novel nature-inspired algorithm that mimics the teaching and learning process. In this paper, an improved version of TLBO algorithm (I-TLBO) is investigated to enhance the performance of original TLBO by achieving a balance between exploitation and exploration ability. Inspired by the concept of historical population, two new phases, namely self feedback learning phase as well as mutation and crossover phase, are introduced in I-TLBO algorithm. In self-feedback learning phase, a learner can improve his result based on the historical experience if his present state is better than the historical state. In mutation and crossover phase, the learners update their positions with probability based on the new population obtained by the crossover and mutation operations between present population and historical population. The design of self-feedback learning phase seeks the maintaining of good exploitation ability while the introduction of the mutation and crossover phase aims at the improvement of exploration ability in original TLBO. The effectiveness of proposed I-TLBO algorithm is tested on some benchmark functions and a combinatorial optimization problem of heat treating in foundry industry. The comparative results with some other improved TLBO algorithms and classic algorithms show that I-TLBO algorithm has significant advantages due to the balance between exploitation and exploration ability. (C) 2017 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Historical population Self-feedback learning Mutation and crossover Combinatorial optimization

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optimization of surface roughness in ball-end milling using teaching-learning-based optimization and response surface methodology

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2017年第14期231卷 2596-2607页

作者： Dikshit, Mithilesh K. Puri, Asit B. Maity, Atanu Natl Inst Technol Durgapur Dept Mech Engn MG Ave Durgapur 713209 W Bengal India CSIR CMERI Adv Design & Optimizat Durgapur India

Surface roughness is one of the most important requirements of the finished products in machining process. The determination of optimal cutting parameters is very important to minimize the surface roughness of a product. This article describes the development process of a surface roughness model in high-speed ball-end milling using response surface methodology based on design of experiment. Composite desirability function and teaching-learning-based optimization algorithm have been used for determining optimal cutting process parameters. The experiments have been planned and conducted using rotatable central composite design under dry condition. Mathematical model for surface roughness has been developed in terms of cutting speed, feed per tooth, axial depth of cut and radial depth of cut as the cutting process parameters. Analysis of variance has been performed for analysing the effect of cutting parameters on surface roughness. A second-order full quadratic model is used for mathematical modelling. The analysis of the results shows that the developed model is adequate enough and good to be accepted. Analysis of variance for the individual terms revealed that surface roughness is mostly affected by the cutting speed with a percentage contribution of 47.18% followed by axial depth of cut by 10.83%. The optimum values of cutting process parameters obtained through teaching-learning-based optimization are feed per tooth (f(z))=0.06mm, axial depth of cut (A(p))=0.74mm, cutting speed (V-c)=145.8m/min, and radial depth of cut (A(e))=0.38mm. The optimum value of surface roughness at the optimum parametric setting is 1.11 mu m and has been validated by confirmation experiments.

关键词： Ball-end milling composite desirability function optimization response surface methodology surface roughness teaching-learning-based optimization

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Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization

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COMPUTERS & CHEMICAL ENGINEERING 2017年 99卷 314-324页

作者： Yu, Kunjie While, Lyndon Reynolds, Mark Wang, Xin Wang, Zhenlei East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Univ Western Australia Sch Comp Sci & Software Engn Nedlands WA 6009 Australia Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

The ethylene cracking furnace system is central to an olefin plant. Multiple cracking furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a cracking furnace system, with consideration of different feeds, multiple cracking furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the cracking furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Ethylene cracking furnace Cyclic scheduling teaching-learning-based optimization

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Stacking Sequence optimization for Maximum Fundamental Frequency of Simply Supported Antisymmetric Laminated Composite Plates using teaching-learning-based optimization

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KSCE JOURNAL OF CIVIL ENGINEERING 2017年第6期21卷 2281-2288页

作者： Topal, Umut Dede, Tayfun Ozturk, Hasan Tahsin Karadeniz Tech Univ Fac Technol Dept Civil Engn TR-61830 Trabzon Turkey Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey

This paper proposed a technique based on teaching-learning-based optimization (TLBO) for fundamental frequency optimization of simply supported antisymmetric laminated composite plates. The fibre orientations of the layers are selected as the optimization design variables with the aim to find the optimal laminated plates. The first order shear deformation theory is used to calculate the natural frequencies of laminates. In order to perform the optimization based on the TLBO, a special code is written in MATLAB software environment. Several numerical examples are presented to show the efficiency of the proposed algorithm. Also, the optimization problems are solved using other optimization algorithm such as Artifical Bee Colony (ABC) algorithm and the results are compared. It is concluded that TLBO algorithm can effectively be used for fundamental frequency optimization of antisymmetric laminated plates. The CPU time and the number of function evaluations required by TLBO are much smaller than those of the other algorithms tested. This indicates that TLBO is a competitive optimization algorithm for this class of problems.

关键词： laminated composite plates teaching-learning-based optimization fundamental frequency optimization

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MTLBO: A Multi-objective Multi-course teaching-learning-based optimization Algorithm

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淡江理工学刊 2018年第3期21卷 331-342页

作者： Wenwen Lin Lei Wang Guangdong Tian Yuejun Zhang

This paper presents a multi-objective evolutionary algorithm, which is called multi-course teaching-learning-based optimization algorithm. It consists of four phases: teacher, leaner, teacher training, and exchange student phases. Three novel modifications are introduced into the basic teaching-learning-based optimization algorithm. It helps diversify evolutionary population, enhance stochastic search ability, and avoid premature convergence. Eight widely used benchmark problems are employed in order to investigate the performance of the proposed algorithm. The experimental results indicate that it finds better Pareto optimal solutions than some other state-of-the-art algorithms for the majority of problems.

关键词： teaching-learning-based optimization Multi-objective optimization Evolutionary Algorithm Heuristic Algorithm

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A novel fuzzy adaptive teaching-learning-based optimization (FATLBO) for solving structural optimization problems

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ENGINEERING WITH COMPUTERS 2017年第1期33卷 55-69页

作者： Cheng, Min-Yuan Prayogo, Doddy Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn 43Sec 4Keelung Rd Taipei 106 Taiwan Petra Christian Univ Dept Civil Engn Jalan Siwalankerto 121-131 Surabaya 60236 Indonesia

This paper presents a new optimization algorithm called fuzzy adaptive teaching-learning-based optimization (FATLBO) for solving numerical structural problems. This new algorithm introduces three new mechanisms for increasing the searching capability of teaching-learning-based optimization namely status monitor, fuzzy adaptive teaching-learning strategies, and remedial operator. The performance of FATLBO is compared with well-known optimization methods on 26 unconstrained mathematical problems and five structural engineering design problems. based on the obtained results, it can be concluded that FATLBO is able to deliver excellence and competitive performance in solving various structural optimization problems.

关键词： optimization Fuzzy logic teaching-learning-based optimization Structural design problems

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Multi-objective optimization of condensation heat transfer using teaching-learning-based optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2017年第7期231卷 666-675页

作者： Kumar, Ravindra Kumar, Parmanand Natl Inst Technol Jamshedpur Jharkhand India

In this paper, the multi-objective optimization of R-245fa vapour condensation inside horizontal tube has been carried out using teaching-learning-based optimization algorithm. The teaching-learning-based optimization algorithm is teaching-learning procedure motivated and works on the impact of a teacher on the outcome of students in a class. Heat transfer coefficient and pressure drop with two parameters have been considered to evaluate the performance of the tube. The mass flux and vapour quality of refrigerant are taken as the parameters. The limit of mass flux and vapour quality are from 100 to 300 kg/m(2)s and 0.1 to 0.8, respectively. The optimum values of heat transfer coefficient 2820.5 W/m(2)K and pressure drop 1360.2 Pa are obtained with mass flux 137.65 kg/m(2)s and vapour quality 0.77 using teaching-learning-based optimization algorithm.

关键词： Condensation heat transfer coefficient pressure drop teaching-learning-based optimization

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Modified teaching-learning-based optimization for Combined Heat and Power Economic Dispatch

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2017年第5期18卷 1-22页

作者： Pattanaik, Jagat Kishore Basu, Mousumi Dash, Deba Prasad Jadavpur Univ Power Engn Dept Sect 3 Kolkata 700098 W Bengal India Orissa Engn Coll Elect Engn Dept Bhubaneswar Orissa India

This paper develops and suggests modified teaching-learning-based optimization to solve non-smooth non-convex combined heat and power economic dispatch (CHPED) problem. The valve-point loading and forbidden working regions of conventional thermal generator are taken into account. Modified teaching-learning-based optimization (MTLBO) introduces Gaussian random variables in the 'Teacher phase' and 'Learner phase' which perks up search efficiency and assurances a high probability of acquiring the global optimum without considerably impairing the convergence speed and the simplicity of the structure of teaching-learning-based optimization (TLBO). The efficacy of the suggested MTLBO technique has been confirmed on three test systems and 15 benchmark functions. Test results of the suggested technique have been matched up to those achieved by other evolutionary techniques. It has been observed from the comparison that the suggested MTLBO technique has the ability to endow with superior solution.

关键词： modified teaching-learning-based optimization teaching-learning-based optimization combined heat and power economic dispatch cogeneration forbidden working region valve-point loading

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