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Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm

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APPLIED ENERGY 2019年 250卷 109-117页

作者： Qais, Mohammed H. Hasanien, Hany M. Alghuwainem, Saad King Saud Univ Fac Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt

This article proposes an accurate and straightforward method for modeling and simulation of photovoltaic (PV) modules. The main target is to find the nine-parameter of a three-diode (TD) model based on the datasheet parameters, which are given by all commercial PV modules. The objective function is formulated based on short circuit, open circuit, power derivative, and maximum power equations. Two parameters (parallel resistance and photo-generated current) are calculated analytically and rest parameters are optimally designed using the sunflower optimization (SFO) algorithm. The presented method is applied to model three types of commercial PV modules (multicrystal KC200GT, poly-crystalline MSX-60, and mono-crystalline CS6K-280M). The optimal nine-parameters obtained in this paper are paralleled with that attained by other approaches. In order to assess the efficiency of the offered approach, I-V and P-V characteristics are validated with measured data under various temperatures and solar irradiations. The error among these results records a value less than 0.5%. Therefore, the simulation results indicate an excellent agreement with the measured data. This proposed approach can be utilized to model any marketable PV module based on given datasheet parameters only.

关键词： Photovoltaic modeling Solar energy sunflower optimization algorithm Three-diode model

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Parameter identification and state-of-charge estimation for lithium-polymer battery cells using enhanced sunflower optimization algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第15期45卷 8833-8842页

作者： El-Sehiemy, Ragab A. Hamida, M. A. Mesbahi, T. Kafrelsheikh Univ Fac Engn Dept Elect Engn Kafr Al Sheikh Egypt Ecole Cent Nantes LS2N UMR CNRS 6004 Nantes France INSA Strasbourg ICube UMR CNRS 7357 Strasbourg France

This paper is concerned with the investigation of accurate parameter identification method and state of charge (SoC) estimation for Lion Lithium battery. The proposed identification method is implemented using an accurate state space model obtained from electric equivalent circuit. The process of parameter identification is expressed as nonlinear optimization problem. An Enhanced sunflower optimization algorithm (ESFOA) is employed to solve such problem. The search space is managed by applying the reduction strategy. This strategy is accomplished with the sunflower optimization algorithm to enhance the solution quality. Three cases studied are considered as single and multiobjective frameworks. In these cases, battery voltage or SoC or combined between them as objective functions are optimized for the three cases studied. Numerical simulations as well as experimental implementation are executed on 40 Ah Kokam Li-Ion Battery to prove the capability of the proposed parameter identification method. The ability of the proposed ESFOA is accomplished with high accuracy is proven compared with Water-Cycle and Whale optimization algorithms for two driving cycle profiles. Added to that, high closeness is achieved compared with the experimental measurements for battery parameters and SoC. The solution quality improvement of the proposed ESFOA is noticed as it achieves the lowest the fitness function levels (in the range 60-90%) of the cases studied compared with the competitive optimization algorithms. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Lithium-polymer battery cells sunflower optimization algorithm Parameter identification State of charge estimation Driving cycle Reduction strategy

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New Chaotic sunflower optimization algorithm for Optimal Tuning of Power System Stabilizers

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2020年第5期15卷 1985-1997页

作者： Alshammari, Badr M. Guesmi, Tawfik Univ Hail Coll Engn Hail Saudi Arabia Univ Sfax Natl Engn Sch Sfax Sfax Tunisia

Purpose The problem of stability is generally caused by insufficient damping of electromechanical oscillations (EMOs). Power system stabilizers (PSSs) are the most advised and efficient devices to increase the system damping and enhance the dynamic characteristics of power networks during abnormal conditions. Unfortunately, the performance of the PSS controller is mostly dependent on the parameters of the lead-lag compensator. Within this context, this study presents a new chaotic-based sunflower optimization algorithm with local search (CSFO-LS) for optimum design of PSS controllers. Methodology In the proposed algorithm symbolized by CSFO-LS, the random parameters of the original SFO are substituted by chaotic sequences to avoid premature convergence at local optima and improve the accuracy of the optimum solution. Firstly, the CSFO-LS is tested and evaluated on various benchmark functions with different characteristics such as multimodality, separability and regularity. Then, it is applied for selecting the optimum parameters of the PSS controllers. These parameters are tuned in order to shift all electromechanical modes in a pre-specified zone in the left side of thes-plan. Results Simulation results based on eight benchmark functions show that CSFO-LS outperforms all the algorithms used for comparison. Moreover, to demonstrate the applicability and performance of the proposed method for providing good damping of low frequency oscillations, a standard power system test under various operating conditions and severe fault is used. Obtained results are compared with those obtained using the original SFO and other recent optimization techniques. Originality In this study, an improved version of the SFO is proposed for providing optimum EMOs damping. All EMOs have to be shifted as much as possible to the left side of thes-plan instead of shifting them to a fixed zone. To our best knowledge, this technique is not suggested or used for any power system problem.

关键词： Chaotic sequences sunflower optimization algorithm Electromechanical modes Power system stabilizers

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Efficiency analysis of mechanical reducer equipment of material handling industry using sunflower optimization algorithm and Material Generation 2nd

Efficiency analysis of mechanical reducer equipment of mater...

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2nd International Conference on Functional Material, Manufacturing and Performances (ICFMMP)

作者： Jena, Sourav Jeet, Siddharth Bagal, Dilip Kumar Baliarsingh, Asini Kumar Nayak, Dillip Ranjan Barua, Abhishek Govt Coll Engn Dept Mech Engn Bhawanipatna Odisha India BPUT Dept Mech Engn Ctr Adv Post Grad Studies Rourkela Odisha India Govt Coll Engn Dept Elect Engn Bhawanipatna Odisha India Govt Coll Engn Dept Comp Sci Engn Bhawanipatna Odisha India

ABSTR A C T Gear reducers are commonly used in cross-industrial applications. These include a range of advanced and basic processes, requiring the delivery of a controlled torque output. Mainly, industrial reducers are used in material handling units as it controls the speed of the machineries such as conveyors, cranes, hoist, mixers etc. For effective operation and to reduce the downtime due to any fault, optimal operating con-ditions are needed to define. In this paper the effectiveness and yield power are thought of, as the main attributes of the Industrial reducer, and their optimization was performed. As the impacting factors, the viscosity of lubricant, the initial parametric no. of revolutions and the current force intensity on the con-trol unit, were considered. Test tests were performed based on the L27 Taguchi orthogonal array. For maximizing the output power and efficiency of mechanical reducer, recently formulate bio-inspired meta-heuristic algorithms i.e. Material Generation algorithm and sunflower optimization algorithm were employed besides Taguchi technique for optimization. It was found that current intensity played a significant role in maximizing the output power and efficiency of industrial reducer in accordance to analysis of variance results. Also both Material Generation algorithm and sunflower optimization algorithm resulted to be precise in providing better output as compared to Taguchi method for maximiz-ing the output power and efficiency of the industrial reducer gearbox. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Con-ference on Functional Material, Manufacturing and Performances

关键词： Mechanical Reducer Taguchi Method Material Generation algorithm sunflower optimization algorithm

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Parametric appraisal of collapsibility and core shrinkage of phenol binded unbaked casting moulds using Taguchi-sunflower optimization algorithm 2nd

Parametric appraisal of collapsibility and core shrinkage of...

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2nd International Conference on Functional Material, Manufacturing and Performances (ICFMMP)

作者： Malik, Soubhagya Jeet, Siddharth Bagal, Dilip Kumar Nayak, Rajendra Prasad Barua, Abhishek Mahapatra, Siba Sankar Govt Coll Engn Dept Mech Engn Bhawanipatna Odisha India BPUT Ctr Adv Post Grad Studies Dept Mech Engn Rourkela Odisha India Govt Coll Engn Dept Comp Sci & Engn Bhawanipatna Odisha India Natl Inst Technol Dept Mech Engn Rourkela Odisha India

When compared to green sand moulds, resin bound sand moulds and cores have higher mechanical characteristics and create more dimensionally exact castings, and are thus increasingly preferred for near net form metal components. The effect of catalyst %, sand particle size and no. of strokes for compression on collapsibility and core shrinkage of no-bake resin bonded mould core was studied in this study using lab testing. Their collapsibility were discovered to decrease and core shrinkage increase with the addition of catalyst amount and bigger grain size. Microscopic study of cross-linked resin bridges between sand grains also supports this. To get the best blend of mould characteristics, the results were optimized using the Taguchi technique and sunflower optimization algorithm. Tests were used to successfully validate the model and its findings. It was reported that the sunflower optimization algorithm made more precise prediction than Taguchi method in maximization of collapsibility and minimization of core shrinkage. This study lays the groundwork for optimizing the moulding parameters of resin reinforced sand mould cores in order to achieve the best quality. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Functional Material, Manufacturing and Performances

关键词： No-Bake Resin Bonded Mould Collapsibility Core Shrinkage Taguchi Method sunflower optimization algorithm

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Automatic Generation Control in Deregulated Power Market Using sunflower optimization algorithm 1

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1st International Conference on Innovation in Electrical Power Engineering, Communication, and Computing Technology (IEPCCT)

作者： Sahoo, Abhilipsa Hota, Prakash Kumar Mohanty, B. Veer Surendra Sai Univ Technol VSSUT Burla Odisha India

ISBN: (数字)9789811523052

ISBN: (纸本)9789811523052;9789811523045

The restructuring of electrical power industries creates competitiveness among the market players, due to which the complexity of load frequency issues is gradually increasing in nature. So for mitigating these load frequency issues controllers are used. This chapter proposes automatic generation control (AGC) for two interconnected control areas, each consisting of two power generation sources, i.e. reheat steam turbine in conjunction with nonlinear generation rate constraint and gas turbine generation in a restructured market environment. sunflower optimization (SFO) algorithm is used for optimal tuning of proportional, integral and derivative (PID) controller considering the integral square error as the objective function. For analysing the market dynamics, the concept of area participation matrix (APM) and DISCO participation matrix (DPM) has been simulated. The effectiveness of this two-area system has been tested with various market scenarios like poolco trading, bilateral trading and contract violation. The yield of the proposed algorithm shows better performance in contrast to the other methods used for tuning the PID controller.

关键词： AGC DISCO participation matrix sunflower optimization algorithm Deregulated power market

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Parametric appraisal of collapsibility and core shrinkage of phenol binded unbaked casting moulds using Taguchi-sunflower optimization algorithm

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Materials Today: Proceedings 2022年 50卷 1138-1144页

作者： Soubhagya Malik Siddharth Jeet Dilip Kumar Bagal Rajendra Prasad Nayak Abhishek Barua Siba Sankar Mahapatra Department of Mechanical Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India Department of Mechanical Engineering Centre for Advanced Post Graduate Studies BPUT Rourkela Odisha India Department of Computer Science and Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India Department of Mechanical Engineering National Institute of Technology Rourkela Odisha India

关键词： No-Bake Resin Bonded Mould Collapsibility Core Shrinkage Taguchi Method sunflower optimization algorithm

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Efficiency analysis of mechanical reducer equipment of material handling industry using sunflower optimization algorithm and Material Generation algorithm

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Materials Today: Proceedings 2022年 50卷 1113-1122页

作者： Sourav Jena Siddharth Jeet Dilip Kumar Bagal Asini Kumar Baliarsingh Dillip Ranjan Nayak Abhishek Barua Department of Mechanical Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India Department of Mechanical Engineering Centre for Advanced Post Graduate Studies BPUT Rourkela Odisha India Department of Electrical Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India Department of Computer Science Engineering Government College of Engineering Kalahandi Bhawanipatna Odisha India

Gear reducers are commonly used in cross-industrial applications. These include a range of advanced and basic processes, requiring the delivery of a controlled torque output. Mainly, industrial reducers are used in material handling units as it controls the speed of the machineries such as conveyors, cranes, hoist, mixers etc. For effective operation and to reduce the downtime due to any fault, optimal operating conditions are needed to define. In this paper the effectiveness and yield power are thought of, as the main attributes of the Industrial reducer, and their optimization was performed. As the impacting factors, the viscosity of lubricant, the initial parametric no. of revolutions and the current force intensity on the control unit, were considered. Test tests were performed based on the L27 Taguchi orthogonal array. For maximizing the output power and efficiency of mechanical reducer, recently formulate bio-inspired meta -heuristic algorithms i.e. Material Generation algorithm and sunflower optimization algorithm were employed besides Taguchi technique for optimization. It was found that current intensity played a significant role in maximizing the output power and efficiency of industrial reducer in accordance to analysis of variance results. Also both Material Generation algorithm and sunflower optimization algorithm resulted to be precise in providing better output as compared to Taguchi method for maximizing the output power and efficiency of the industrial reducer gearbox.

关键词： Mechanical Reducer Taguchi Method Material Generation algorithm sunflower optimization algorithm

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Orientation angle based online motion control of an Aldebaran NAO humanoid robot in V-REP software environment using novel sunflower optimization (SFO) algorithm

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International Journal of Information Technology (Singapore) 2021年第6期13卷 2175-2183页

作者： Gupta, Aanjaney Adhikari, Rounak Pandey, Anish Kashyap, Abhishek Kumar School of Mechanical Engineering KIIT Deemed To Be University Patia Bhubaneswar 751024 India

This paper presents the novel sunflower optimization (SFO) algorithm based online motion and orientation planner for an Aldebaran NAO humanoid robot (NAO robot) in the Virtual Robot Experimentation Platform (V-REP) software environment. For this purpose, we have taken the rear-time sonar sensors data of obstacles as inputs and chosen the complete orientation angle as an output of the NAO robot to make the objective function for the SFO algorithm. The designed objective function controls the orientation angle of the NAO robot during navigation and obstacle avoidance. Next, the programming of the SFO algorithm and forward kinematic equations have been written in MATLAB scripts. Through the application programming interface functions, these scripts give the orientation angle control command to the NAO robot in the V-REP software environment between obstacles. The two-dimensional (2D) and three-dimensional (3D) experimental results of the proposed SFO algorithm-controlled NAO robot are performed in the MATLAB and V-REP software environments. A comparative analysis between the SFO algorithm and the Adaptive Particle Swarm optimization algorithm has also been done to show the effectiveness and efficiency of the proposed algorithm. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Forward kinematic equation NAO robot Orientation angle Sonar sensor sunflower optimization algorithm Virtual robot experimentation platform software

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Rider and sunflower optimization-driven neural network for image classification

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WEB INTELLIGENCE 2021年第1-2期19卷 41-61页

作者： Nadendla, Hanumantha Rao Srikrishna, A. Rao, K. Gangadhara JNTUK Dept Comp Sci & Engn Kakinada Andhra Pradesh India RVR & JC Coll Engn Dept Comp Sci & Engn Chowdavaram Andhra Pradesh India RVR & JC Coll Engn Dept Informat Technol Guntur Andhra Pradesh India Acharya Nagarjuna Univ Dept Comp Sci & Engn Guntur Andhra Pradesh India

Image classification is the classical issue in computer vision, machine learning, and image processing. The image classification is measured by differentiating the image into the prescribed category based on the content of the vision. In this paper, a novel classifier named RideSFO-NN is developed for image classification. The proposed method performs the image classification by undergoing two steps, namely feature extraction and classification. Initially, the images from various sources are provided to the proposed Weighted Shape-Size Pattern Spectra for pattern analysis. From the pattern analysis, the significant features are obtained for the classification. Here, the proposed Weighted Shape-Size Pattern Spectra is designed by modifying the gray-scale decomposition withWeight-Shape decomposition. Then, the classification is done based on Neural Network (NN) classifier, which is trained using an optimization approach. The optimization will be done by the proposed Ride sunflower optimization (RideSFO) algorithm, which is the integration of Rider optimization algorithm (ROA), and sunflower optimization algorithm (SFO). Finally, the image classification performance is evaluated using RideSFO-NN based on sensitivity, specificity, and accuracy. The developed RideSFO-NN method achieves the maximal accuracy of 94%, maximal sensitivity of 93.87%, and maximal specificity of 90.52% based on K-Fold.

关键词： Image classification Weighted Shape-Size Pattern Spectra Rider optimization algorithm sunflower optimization algorithm neural network

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