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Modelling of photovoltaic cells using Boole's rule-based multi-objective genetic algorithm implemented in Indoor hardware setup

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期45卷 5075-5092页

作者： IssanRaj, R. Visalakshi, S. SRM Valliammai Engn Coll Dept EIE Kattankulathur Tamilnadu India

Triple Diode Solar Cell Module (TDSCM) circuit with nine parameters for various environmental circumstances represents the behavior and practical performance of solar *** precise extraction of photovoltaic (PV) module parameters is essential for optimising the energy conversion efficiency of PV systems. Usually the equations describing solar panels are implicit in nature, and parameter extraction has been very complicated. The solar cell is mathematically modelled with nonlinear I-V (Current -Voltage) characteristics behavior, and it cannot be directly determined from the PV's datasheet due to the lack of data offered by the PV manufacturers. On the basis of the technical datasheet of the photovoltaic module (PV), only four equations can be obtained in single diode, double diode, and triple diode parameters. To be implemented with fifth equation, many researchers have been done with multiple approximations and it becomes with low accuracy, complexity of computation, convergence problem. To resolve these issues, a new multi-objective optimization (GA) genetic algorithm method is prescribed to frame the fifth equation using the Boole rules implemented with the curved area concept. The proposed Boole's rule based model offers superior non-linearity performance and high precision modelling, and the error shows a significant reduction when compared to the single and double diode approaches used in the existing approach. The effectiveness of the proposed I-V curve characteristics efficiency was improved by the implementation of the proposed Boole's rule with RMSE error 0.000034.

关键词： Photovoltaic cell model solar cell modelling multi objective genetic algorithm triple diode model boole's rule

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Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for non-Newtonian binary fluids

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 535卷 122409-000页

作者： Wu, Huawei Bagherzadeh, Seyed Amin D'Orazio, Annunziata Habibollahi, Navid Karimipour, Arash Goodarzi, Marjan Quang-Vu Bach Hubei Univ Arts & Sci Hubei Key Lab Power Syst Design & Test Elect Vehi Xiangyang 441053 Peoples R China Hubei Univ Arts & Sci Sch Automot & Traff Engn Xiangyang 441053 Peoples R China Islamic Azad Univ Dept Mech Engn Najafabad Branch Najafabad Iran Sapienza Univ Roma Dipartimento Ingn Astronaut Elettr & Energet Via Eudossiana 18 I-00184 Rome Italy Ton Duc Thang Univ Sustainable Management Nat Resources & Environm R Fac Environm & Labour Safety Ho Chi Minh City Vietnam

This work aims to present a new statistical optimization approach of artificial neural network modified by multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for a non-Newtonian nanofluid composed of Fe3O4 nanoparticles dispersed in liquid paraffin. Hence the mixture pressure lose & convection coefficient are evaluated and then optimized so that to maximize the convection heat transfer and minimize the pressure drop. The results showed that the proposed model of multi objective optimization of GA Pareto optimal front, quantified the trade-offs to handle 2 fitness functions of the considered non-Newtonian pipe flow. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Fe3O4/liquid paraffin Thermo-physical properties multi objective genetic algorithm Optimization

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Thermal and hydraulic optimization of plate heat exchanger using multi objective genetic algorithm

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CASE STUDIES IN THERMAL ENGINEERING 2017年 10卷 570-578页

作者： Imran, Muhammad Pambudi, Nugroho Agung Farooq, Muhammad Univ Management & Technol Dept Energy Engn Sch Engn CII Johar Town Lahore Pakistan Kyushu Univ Int Inst Carbon Neutral Reserch WPI I2CNER Nishi Ku 744 Motooka Fukuoka Fukuoka 8190395 Japan Univ Negeri Sebelas Maret Dept Mech Engn Educ Jl Ahmad Yani 200A Pabelan Indonesia Univ Engn & Technol Lahore Dept Mech Engn KSK Campus Kala Shah Kaku 39020 Pakistan

In this paper thermal and hydraulic optimization of water to water chevron type plate heat exchanger is presented. The optimization is performed using the multi objective genetic algorithm in MATLAB optimization environment. Constrain matrix is a set of different geometrical parameters of plate heat exchanger within the logical bounds. The two objective functions are pressure drop of hot side and heat transfer. Due to conflicting nature of these objective functions, no single solution can satisfy both of the objective function simultaneously. The increase in heat transfer will results in increase in pressure drop, therefore, optimization results are presented as Pareto Front. multi objective genetic algorithm tool was employed to find a set of optimum solution which was trade-off between pressure drop and heat transfer. M the end, sensitivity analysis was performed to analyse the effect of geometrical parameters of heat exchanger on thermal and hydraulic performance. The sensitivity results show that the heat transfer and pressure drop are greatly affected by the vertical port centre distance, plate spacing and number of thermal plates.

关键词： Thermal optimization Hydraulic optimization Plate heat exchanger multi objective genetic algorithm Heat transfer Pressure drop

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Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)approaches

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Information Processing in Agriculture 2016年第3期3卷 190-205页

作者： Behzad Elhami Asadollah Akram Majid Khanali Department of Agricultural Machinery Engineering Faculty of Agricultural Engineering and TechnologyUniversity of TehranKarajIran

Energy consumption in agricultural products and its environmental damages has increased in recent *** cycle assessment(LCA)has been introduced as a suitable tool for evaluation environmental impacts related to a product over its life *** this study,optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis(DEA)and multi objective genetic algorithm(MOGA)*** were collected from 110 chickpea production enterprises using a face to face questionnaire in the cropping season of *** results of optimization revealed that,when applying MOGA,optimum energy requirement for chickpea production was significantly lower compared to application of DEA technique;so that,total energy requirement in optimum situation was found to be 31511.72 and 27570.61 MJ ha^-1 by using DEA and MOGA techniques,respectively;showing a reduction by 5.11%and 17%relative to current situation of energy *** of environmental impacts by application of MOGA resulted in reduction of acidification potential(ACP),eutrophication potential(EUP),global warming potential(GWP),human toxicity potential(HTP)and terrestrial ecotoxicity potential(TEP)by 29%,23%,10%,6%and 36%,*** capable of reducing the energy consumption from machinery,farmyard manure(FYM)diesel fuel and nitrogen fertilizer(the mostly contributed inputs to the environmental emissions)by 59%,28.5%,24.58%and 11.24%,***,the MOGA technique showed a superior performance relative to DEA approach for optimizing energy inputs and reducing environmental impacts of chickpea production system.

关键词： Data envelopment analysis Energy Life cycle assessment multi objective genetic algorithm

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An Investigation on Optimization of Parameters for Injection Molded Polypropylene-Marble Composites with multi objective genetic algorithm

An Investigation on Optimization of Parameters for Injection...

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IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)

作者： Kumar, Deepak Rao, P. N. Dangayach, G. S. Malaviya Natl Inst Technol Mech Engn Dept Jaipur Rajasthan India Univ Northern Iowa Dept Technol Cedar Falls IA 50614 USA

ISBN: (纸本)9781509028078

In the era of sustainability, recycling of industrial waste material by using marble powder as filler in the Polypropylene matrix recommend an interesting opportunity. In this study, composites were fabricated by adding different weight percentages of marble powder (5-15 wt.%) in the Polypropylene by injection molding process. To evaluate the contribution of marble powder in the PP-marble composite, Mechanical characterizations such as Shore hardness test, compressive strength and impact strength were performed. For surface morphological characterizations Scanning Electron Microscopy (SEM) was performed for the Polypropylene-marble composites. Taguchi technique and multi objective genetic algorithm were used to optimize the parameters.

关键词： Polypropylene-Marble composite injection molding morphological characterization mechanical properties Taguchi technique multi objective genetic algorithm

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A Test Suite Generation Approach based on EFSMs using a multi-objective genetic algorithm 19

A Test Suite Generation Approach based on EFSMs using a Mult...

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19th International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Turlea, Ana Ipate, Florentin Lefticaru, Raluca Univ Bucharest Dept Comp Sci Bucharest Romania Univ Bradford Sch Elect Engn & Comp Sci Bradford W Yorkshire England

ISBN: (纸本)9781538626269

Using extended finite state machines for test data generation can be a difficult process because we need to generate paths that are feasible and we also need to find input data that traverse a given path. This paper presents a test suite generation algorithm for extended finite state machines. The algorithm produces a set of feasible transition paths that cover all transitions using a modified multi-objective genetic algorithm (deleting redundant paths and shortening the solutions). The multi-objective problem aims to optimize the transitions coverage and the path feasibility, based on dataflow dependencies. Having a set of paths resulted from this algorithm, we can easily find input parameters for each path.

关键词： genetic algorithm extended finite state machines multi objective genetic algorithm global search transitions coverage paths feasibility transition path test generation test case

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Optimization of small satellite constellation design for continuous mutual regional coverage with multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2016年第4期9卷 627-637页

作者： Meziane-Tani, I. Metris, G. Lion, G. Deschamps, A. Bendimerad, F. T. Bekhti, M. Univ Tlemcen Fac Technol Lab Telecommun Tlemcen BP 230 Tilimsen 13000 Algeria Univ Nice Sophia Antipolis CNRS UMR 7329 GeoazurObserv Cote Azur 250 Rue Albert Einstein F-06560 Sophia Antipolis France CNRS Observ Paris LNE SYRTE UMR8630 61 Ave Observ F-75014 Paris France Algerian Space Agcy ASAL Ctr Satellite Dev POS 50 ILOT T12 Bir El Djir Oran 31000 Algeria

This paper describes the application of an evolutionary optimization method to design satellite constellation for continuous regional coverage without intersatellite links. This configuration, called mutual coverage, is related to some technical limitations that exist on small satellite technology. The coverage of the north Algerian seismological network is taken as an example of application. A multi objective genetic algorithm (MOGA) is used to make a trade-off between the improvement of the coverage rate, the minimization of the total number of satellites and the reduction of the satellites' altitude. First, some experiments have been performed to find the weight distribution of the fitness function that shows the most significant improvement of the average fitness function. Then, some optimized constellation designs are given for different ranges of altitude and it is shown that the size of the MOGA constellation design is significantly reduced compared to the traditional geometrical design.

关键词： multi objective genetic algorithm Constellation Design Regional Coverage Mutual Coverage Small Satellite Constellation

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Thermal Model Correlation of SNIPE Satellite Using genetic-algorithm-Based multi-objective Optimization Method

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JOURNAL OF SPACECRAFT AND ROCKETS 2023年第4期60卷 1333-1342页

作者： Kim, Ji-Seok Lee, Seonho Kim, Hui-Kyung Kim, Hae-Dong Korea Aerosp Res Inst Satellite Integrat & Test Ctr Daejeon 34133 South Korea Korea Aerosp Res Inst Div Satellite Space Explorat Syst Design Daejeon 34133 South Korea Gyeongsang Natl Univ Dept Aerosp & Software Engn Jinju 52828 South Korea

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Optimal simultaneous coordination of PSS and TCSC using multi objective genetic algorithm

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JOURNAL OF ELECTRICAL SYSTEMS 2013年第4期9卷 410-421页

作者： Davarani, Roohalamin Zeinali Ghazi, Reza Grad Univ Adv Technol Haftbagh highway Kerman Iran Grad Univ Adv Technol Dept Elect Engn Kerman Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran

Power system stability enhancement via optimal simultaneous coordinated design of a power system stabilizer (PSS) and a thyristor-controlled series capacitor (TCSC) for electric power systems oscillations damping is investigated in this paper. A SMIB system equipped with PSS and TCSC controllers is considered in this study. Although these controllers are used for stabilization of power system oscillations but the system must preserve its stability when subjected to sever disturbances. Therefore, the overall stability of the system should be considered. To do so, in the present paper the problem of controllers design is formulated as a multi objective optimization problem. Then the multi objective genetic algorithm (MOGA) is explored to solve this optimization problem. Pareto method type of selection is used in the present MOGA approach.

关键词： Power system stabilizer Thyristor-controlled series capacitor multi objective genetic algorithm

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Performance Analysis and multi-objective Optimization of an Offset Plate-Fin Crossflow Heat Exchanger

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2025年第1期39卷 199-215页

作者： Bassily, Ashraf Maurice Ghazy, Ahmed Alexandria Higher Inst Engn & Technol Dept Mechatron Alexandria Egypt Jouf Univ Dept Mech Engn Sakaka Saudi Arabia

A compact crossflow offset plate-fin heat exchanger (CPFHE) is widely used in many industrial applications. In this paper, a local sensitivity analysis was performed and a parametric analysis was used to validate the results of the sensitivity analysis and locate the optimum by utilizing the unidirectional search. A new formula for the mass of the CPFHE that includes the masses of fans was introduced. A practical approach was used to formulate multi-objective optimization problems through which the total annual relative cost, heat rate, effectiveness, and mass of the CPFHE were optimized separately and simultaneously relative to 13 optimization design variables using a genetic algorithm and the direct search method. The main optimization results showed that the effectiveness of the CPFHE could be increased by 9% through performing the optimization process relative to an additional influential variable, achieving a remarkable 0.9812. The actual value for the optimized mass of the CPFHE reported in the literature (a very small volume with huge pressure drops and massive fans) is many times greater than the optimized mass of this study. The distributions of the Pareto-optimal point solutions of multi-objective optimization results for three different conflict-objective functions were plotted and showed that all points are nondominated.

关键词： Plate Fin Heat Exchanger multi objective genetic algorithm Heat Transfer Coefficients Particle Swarm Optimization Cost Effectiveness minimizing cost minimizing wieght maximizing heat rate maximizing effectiveness Pareto-optimal point solutions

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