- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Modeling and parameters extraction of photovoltaic cell and modules using the genetic algorithms with lambert W-function as objective function
摘要： In this paper, a method based on genetic algorithms is proposed for improving the accuracy of solar cell parameters extracted using novel technique. We propose a computational based binary-coded genetic algorithm (GA) to extract the parameters (I0, Iph, n, Rs and Rsh) for a single diode model of solar cell from its current-voltage (I–V) characteristic. The algorithm was implemented using Matlab as a programming tool and validated by applying it to the I–V curve synthesized from the literature using reported values. The characterization, current-voltage data used was generated by simulating a one-diode solar cell model of speci?ed parameters. The new approach is based on formulating I–V equation of solar cell, with Lambert function, the parameter extraction as a search and optimization problem. Compared with other optimization techniques in literatures, the approach proposed for the determination of parameters are in good agreement.
关键词： Photovoltaic solar cell,genetic algorithm,optimization,parameter extraction,Lambert function
更新于2025-09-23 15:21:01
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Techno-economic optimization of grid-connected, ground-mounted photovoltaic power plants by genetic algorithm based on a comprehensive mathematical model
摘要： The increasing penetration of photovoltaic (PV) technology calls for the development of an effective method for optimization of grid-connected photovoltaic power plants. This paper presents a simultaneous optimization method of ten important design parameters of a PV plant, including the module power, inverter sizing, support structure dimensions, cable losses, module orientation and row spacing. A mathematical PV performance model taking into account the important effects and losses and an economic cost model were developed and presented in detail. The objective function is the internal rate of return and the optimization is performed by a genetic algorithm. The results show that the proposed models and method are capable to optimize the grid-connected PV plant and provide reliable results after a 6–7 min calculation time. The method was demonstrated in detail for a Hungarian location, including the losses and cost structure of the optimal plant configuration. The optimization was also performed for 5 additional sites around the world to assess the effect of location and meteorology. The impact of the decreasing PV module prices on the optimal design is calculated to identify the expected future trends in PV plant design. The presented optimization method can be utilized to facilitate the optimal design of commercial PV plants and for research purposes.
关键词： Modeling,Optimization,Genetic algorithm,Grid-connected photovoltaic plants
更新于2025-09-23 15:21:01
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Using adaptive neuro-fuzzy and genetic algorithm for simultaneously estimating the dye and AgNP concentrations of treated silk fabrics with nanosilver
摘要： Purpose – Arti?cial intelligence (AI) methods, such as genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS), are capable of providing superior solutions for the simulation and the modeling of complex problems. The purpose of this study is to estimate the dye and the silver nanoparticle (AgNP) concentrations of silver nanoparticle-treated silk fabrics by the aforementioned methods. Design/methodology/approach – In this study, the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics were matched by using the GA technique based on spectrophotometric color matching. The ANFIS method was also used; this method is based on the grid partitioning algorithm across four different methods. The ?rst and second methods are provided for dye concentration prediction, and the third and the fourth methods are given for AgNP concentration prediction. Findings – The mean of absolute error and root mean square (RMS) of the best dye concentration prediction by the ANFIS method based on the second method are 0.087 and 0.103, respectively. In addition, the mean of the absolute error and the RMS of the best results for AgNP concentration prediction by the ANFIS method by using the third method is 0.002 and 0.003, respectively. The obtained results indicate that the performance of the ANFIS method is better than the GA method. Originality value – The simultaneous prediction of the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics was performed by using the GA and the ANFIS. The suggested method led to acceptable accuracy for color and antibacterial matching.
关键词： Prediction,Silver nanoparticle,Genetic algorithm,Neuro-fuzzy,Dye
更新于2025-09-23 15:21:01
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A biased random-key genetic algorithm for routing and wavelength assignment under a sliding scheduled traffic model
摘要： The problem of routing and wavelength assignment in optical networks consists in minimizing the number of wavelengths that are needed to route a set of demands, such that demands routed using lightpaths that share common links are assigned to different wavelengths. We present a biased random-key genetic algorithm for approximately solving the problem of routing and wavelength assignment of sliding scheduled lightpath demands in optical networks. In this problem variant, each demand is characterized not only by a source and a destination, but also by a duration and a time window in which it has to be met. Computational experiments show that the numerical results obtained by the proposed heuristic improved upon those obtained by a multistart constructive heuristic. In addition, the biased random-key genetic algorithm obtained much better results than an existing algorithm for the problem, finding solutions that use roughly 50% of the number of wavelengths determined by the latter.
关键词： Routing and wavelength assignment,Sliding scheduled lightpath demands,Optical networks,Scheduled lightpath demands,Metaheuristics,Biased random-key genetic algorithm
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 5th International Conference for Convergence in Technology (I2CT) - Bombay, India (2019.3.29-2019.3.31)] 2019 IEEE 5th International Conference for Convergence in Technology (I2CT) - A Simulink Model For Three Switch Single Phase Switched Coupled Inductor Inverter for PV System
摘要： Music is everywhere in the world, and its applications in commerce are extremely versatile. Generally speaking, in order to create some music for background music, it is necessary to engage sound recordists and instrumental performers. However, the process is very time-consuming and costly. In this paper, a real-time emotion-based music accompaniment system is proposed to solve this issue. For different emotions, a fuzzy logic controller is designed to adjust the tempo of the music, and an adaptive partition evolutionary genetic algorithm is developed to create corresponding melodies. The chord progressions are generated via music theory, and the instrumentation is disposed by the conception of the probability. What is noteworthy is that all the processes can be output by Virtual Studio Technology in real time so that users can listen directly to the composing results from any emotions. From the experimental results, the proposed adaptive partition evolutionary genetic algorithm performs better than other optimal algorithms in such topics.
关键词： genetic algorithm,emotion,music,fuzzy,melody,Accompaniment
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Influence of Functional Layers Thickness on CdTe Based Flexible Solar Cells Efficiency
摘要： An intelligent hybrid Taguchi-genetic algorithm (IHTGA) is used to optimize bearing offsets and shaft alignment in a marine vessel propulsion system. The objectives are to minimize normal shaft stress and shear force. The constraints are permissible reaction force, bearing stress, shear force, and bending moment in the shaft thrust ?ange under cold and hot operating conditions. Accurate alignment of the shaft for a main propulsion system is important for ensuring the safe operation of a vessel. To obtain a set of acceptable forces and stresses for the bearings and shaft under operating conditions, the optimal bearing offsets must be determined. Instead of the time-consuming classical local search methods with some trial-and-error procedures used in most shipyards to optimize bearing offsets, this paper used IHTGA. The proposed IHTGA performs Taguchi method between the crossover operation of the conventional GA. Incorporating the systematic reasoning ability of Taguchi method in the crossover operation enables intelligent selection of genes used to achieve crossover, which enhances the performance of the IHTGA in terms of robustness, statistical performance, and convergence speed. A penalty function method is performed using the ?tness function as a pseudo-objective function comprising a linear combination of design objectives and constraints. A ?nite-element method is also used to determine the reaction forces and stresses in the bearings and to determine normal stresses, bending moments, and shear forces in the shaft. Computational experiments in a 2200 TEU container vessel show that the results obtained by the proposed IHTGA are signi?cantly better than those obtained by the conventional local search methods with some trial-and-error procedures.
关键词： genetic algorithm,shaft alignment,Marine vessel propulsion system,bearing offsets,optimal design
更新于2025-09-23 15:19:57
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Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells
摘要： Thin-?lm solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best ef?ciency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers’ thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a signi?cantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100% accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use 60.84% fewer simulations than the brute-force method.
关键词： optical modelling,?nite difference time domain,genetic algorithm,solar cell optimization
更新于2025-09-23 15:19:57
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Study on coupling optimization model of node enterprises for energy storage-involved photovoltaic value chain in China
摘要： In recent years, with continuous focus on clean energy and environmental protection, the scale of photovoltaic generation industry in China has been gradually expanded, making great achievements. However, it also faces huge challenges and problems such as fierce market competition, serious photovoltaic curtailment, high cost, etc. In order to promote the sustainable development of photovoltaic industry, this paper constructs an energy storage-involved photovoltaic value chain (ES-PVC) consisting of three nodes for upstream, midstream and downstream, in which photovoltaic power suppliers, battery energy storage business and electric vehicle manufacturers locate respectively. The coupling problem for node enterprises in the value chain is studied by multi-objective optimization and G1 method, which is measured eventually via a single value transformed by ideal point method. Considering the particularity of issue, the paper proposes an improved genetic algorithm (IGA) to fit and solve the model. A best value chain is acquired via proposed approach whose feasibility and applicability are verified through comparative analysis with analytic hierarchy process (AHP) and linear assignment method (LAM) and sensitivity analysis. The implications and limitations are presented in the conclusion, aiming to provide theoretical reference for value management of photovoltaic enterprise and to get further research.
关键词： Improved genetic algorithm,Photovoltaic value chain,Node coupling optimization,Energy storage,G1 method,Multi-objective decision-making
更新于2025-09-23 15:19:57
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Rate Equation Modeling of Interband Cascade Lasers on Modulation and Noise Dynamics
摘要： An intelligent hybrid Taguchi-genetic algorithm (IHTGA) is used to optimize bearing offsets and shaft alignment in a marine vessel propulsion system. The objectives are to minimize normal shaft stress and shear force. The constraints are permissible reaction force, bearing stress, shear force, and bending moment in the shaft thrust ?ange under cold and hot operating conditions. Accurate alignment of the shaft for a main propulsion system is important for ensuring the safe operation of a vessel. To obtain a set of acceptable forces and stresses for the bearings and shaft under operating conditions, the optimal bearing offsets must be determined. Instead of the time-consuming classical local search methods with some trial-and-error procedures used in most shipyards to optimize bearing offsets, this paper used IHTGA. The proposed IHTGA performs Taguchi method between the crossover operation of the conventional GA. Incorporating the systematic reasoning ability of Taguchi method in the crossover operation enables intelligent selection of genes used to achieve crossover, which enhances the performance of the IHTGA in terms of robustness, statistical performance, and convergence speed. A penalty function method is performed using the ?tness function as a pseudo-objective function comprising a linear combination of design objectives and constraints. A ?nite-element method is also used to determine the reaction forces and stresses in the bearings and to determine normal stresses, bending moments, and shear forces in the shaft. Computational experiments in a 2200 TEU container vessel show that the results obtained by the proposed IHTGA are signi?cantly better than those obtained by the conventional local search methods with some trial-and-error procedures.
关键词： bearing offsets,optimal design,Marine vessel propulsion system,shaft alignment,genetic algorithm
更新于2025-09-23 15:19:57
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Visual Tracking with Breeding Fireflies using Brightness from Background-Foreground Information
摘要： Visual target tracking involves object localization in image sequences. This is achieved by optimizing image feature similarity based objective functions in object state space. Meta-heuristic algorithms have shown promising results in solving hard optimization problems where gradients are not available. This motivated us to use Firefly algorithms in visual object tracking. The object state is represented by its bounding box parameters and the target is modeled by its color distribution. This work has two significant contributions. First, we propose a hybrid firefly algorithm where genetic operations are performed using Real-coded Genetic Algorithm (RGA). Here, the crossover operation is modified by incorporating parent velocity information. Second, the firefly brightness is computed from both foreground and background information (as opposed to only foreground). This helps in handling scale implosion and explosion problems. The proposed approach is benchmarked on challenging sequences from VOT2014 dataset and is compared against other baseline trackers and metaheuristic algorithms.
关键词： Genetic algorithm,Foreground-background information,Optimization,Visual tracking,Firefly algorithm,Object localization
更新于2025-09-19 17:15:36