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New hybrid metaheuristic algorithm for scintillator gamma ray spectrum analysis
摘要： The Sodium Iodide detector (NaI(Tl)) is one of the most widely used nuclear devices in gamma-ray spectrometry due to its high efficiency and low price. However, this detector has low energy resolution and spectra measured by this detector are associated with Gaussian broadening. Therefore, the detector cannot resolve the photopeaks with very close energies. To overcome this problem, spectral deconvolution methods such as boosted ML-EM and boosted Gold algorithms have been proposed, that somewhat resolve the complex spectrum. But these methods cannot obtain a spectrum consisting of narrow photopeaks. Therefore, due to the importance of spectral deconvolution and its applications, there is always a need for a more efficient and precise method. In this study, a new multi-step method based on metaheuristic algorithms is introduced for deconvolution of NaI(Tl) detector spectrum. The new method is used for deconvolution of measured and simulated complex spectra and results are compared with the results of previous methods. The results show that the new multi-step spectral deconvolution method has a very high accuracy and efficiency in deconvolution of the complex spectra.
关键词： Metaheuristic algorithms,Multi-step method,Spectral deconvolution,NaI(T1) detector
更新于2025-09-23 15:21:01
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A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and Metaheuristic Global Optimization Algorithms
摘要： The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior of a PV generator. However, the implicit model does not analyze different solution methods to reduce computation time. This paper formulates the solution of the implicit model of SP arrays as an optimization problem with restrictions for all the variables, i.e., submodules voltages, blocking diode voltage, and strings currents. Such an optimization problem is solved by using two deterministic (Trust-Region Dogleg and Levenberg Marquard) and two metaheuristics (Weighted Differential Evolution and Symbiotic Organism Search) optimization algorithms to reproduce the current–voltage (I–V) curves of small, medium, and large generators operating under homogeneous and non-homogeneous conditions. The performance of all optimization algorithms is evaluated with simulations and experiments. Simulation results indicate that both deterministic optimization algorithms correctly reproduce I–V curves in all the cases; nevertheless, the two metaheuristic optimization methods only reproduce the I–V curves for small generators, but not for medium and large generators. Finally, experimental results confirm the simulation results for small arrays and validate the reference model used in the simulations.
关键词： partial shading,global optimization,series–parallel,deterministic optimization algorithm,implicit model solution,metaheuristic optimization algorithm,photovoltaic array
更新于2025-09-23 15:19:57
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Backtracking search algorithm with L??vy flight for estimating parameters of photovoltaic models
摘要： An accurate mathematical model plays an important role for simulation, evaluation and optimization of photovoltaic (PV) models. The characteristic current equations describing the PV models are implicit, nonlinear and transcendental. Given the features of the characteristic current equations, traditional optimization algorithms are usually easy to converge to local optimal solutions. Thus using metaheuristic methods called modern optimization algorithms to estimate parameters of PV models has been a research hotspot in recent years. Although many metaheuristic methods have been employed to solve this problem, it is still necessary for researchers to propose new optimization algorithms to obtain more accuracy and reliability solutions. This paper presents a new metaheuristic algorithm called backtracking search algorithm with Lévy flight (LFBSA) to estimate the parameters of PV models. Compared with the basic backtracking search algorithm (BSA), LFBSA has the following two remarkable features. Firstly, an information sharing mechanism with Lévy flight is built to enhance population diversity. Secondly, mutation operator based on the hunting mechanism of grey wolves is introduced to increase the chance of LFBSA to escape from local minima. LFBSA is used to estimate parameters of three different PV models. Experimental results show the proposed LFBSA is superior to BSA and the other compared algorithms in terms of accuracy and reliability.
关键词： Photovoltaic modeling,Backtracking search algorithm,Lévy flight,Metaheuristic method
更新于2025-09-23 15:19:57
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Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach
摘要： The integration of photovoltaic systems (PVSs) in future power systems grows into a more attractive choice. Thus, the studies related to PVSs operation have gained immense interest. Particularly, research in identifying PV cell model parameters remains an agile field because of the non-linearity of PV cell characteristics and its wide dependency on meteorological conditions of irradiation level and temperature. This paper proposes an Opposition-based Learning Modified Salp Swarm Algorithm (OLMSSA) for accurate identification of the two-diode model parameters of the electrical equivalent circuit of the PV cell/module. Six metaheuristic algorithms, including the recently released basic algorithm SSA, used with the benchmark test PV model of the double diode, and a practical PV module, are employed to assess the performance of OLMSSA. The experimental results and the in-depth comparative study clearly demonstrate that OLMSSA is highly competitive and even significantly better than the reported results of the majority of recently-developed parameter identification methods.
关键词： Metaheuristic Optimizer,Two-diode model,I-V characteristics,Parameters extraction,Photovoltaic panels,Salp Swarm Algorithm
更新于2025-09-19 17:13:59
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Plasmonic effect of metal nanoparticles on enhancing performance of transparent electrodes: a computational investigation
摘要： In this paper, metal nanoparticles are used as a new concept to enhance the optical and conductive properties of transparent electrode films. Finite difference time domain (3D-FDTD) numerical analysis is carried out to study the influence of engineered nanoparticles on the electrode transparency and resistivity performances. Our investigation demonstrates that metal nanoparticles are responsible for inducing plasmonic and light trapping effects, where their spatial arrangement, geometry and position in transparent conductive oxide (TCO) play a crucial role in modulating the electrode optical and electrical properties. Besides, an enhanced average transmittance and reduced sheet resistance over the conventional electrodes are recorded. Subsequently, a new hybrid modeling approach based on 3D-FDTD supported by genetic algorithm global optimization is proposed to identify the metal of nanoparticles and their spatial distribution, allowing an excellent trade-off between transparency and resistivity characteristics. Interestingly, the investigated electrode structure with optimized nanoparticles patterning showcases promising pathways for boosting the TCO performances, where it provides a high average figure of merit of 38 × 10?3 ??1. Therefore, this systematic investigation can provide more insights concerning the benefit of plasmonic effects for designing high-performance transparent electrodes suitable for optoelectronic and photovoltaic applications.
关键词： Nanoparticles,Transmittance,TCO,Photovoltaic,Plasmonic,Metaheuristic
更新于2025-09-19 17:13:59
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Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information
摘要： Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.
关键词： prediction,photovoltaic power,meteorological input,artificial neural networks,metaheuristic optimization
更新于2025-09-16 10:30:52
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[IEEE 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) - Gramado, Brazil (2019.9.15-2019.9.18)] 2019 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America) - Parameter Extraction of One-Diode Photovoltaic Model using Lévy Flight Directional Bat Algorithm
摘要： In this paper, a modi?ed version of the directional bat algorithm (DBA) is proposed to fast and accurately extract the ?ve parameters of the one-diode photovoltaic model from experimental data. In order to improve the exploit feature of the DBA, a random ?ight step based on the L′evy distribution is introduced. Additionally a dynamic procedure to correct any solution found which violates the established parameters bounds, during the solution process, is proposed. Tests were carried out on two commercial photovoltaic devices and the results demonstrated that for these two cases at least, the proposed algorithm named L′evy ?ight directional bat algorithm (LDBA), is more ef?cient and robust than the DBA and other well-established metaheuristic algorithms presented in the literature.
关键词： metaheuristic algorithm,Photovoltaic modeling,experimetal data,parameter estimation
更新于2025-09-12 10:27:22
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Application of Supply-Demand-Based Optimization for Parameter Extraction of Solar Photovoltaic Models
摘要： Modeling solar photovoltaic (PV) systems accurately is based on optimal values of unknown model parameters of PV cells and modules. In recent years, the use of metaheuristics for parameter extraction of PV models gains more and more attentions thanks to their efficacy in solving highly nonlinear multimodal optimization problems. This work addresses a novel application of supply-demand-based optimization (SDO) to extract accurate and reliable parameters for PV models. SDO is a very young and efficient metaheuristic inspired by the supply and demand mechanism in economics. Its exploration and exploitation are balanced well by incorporating different dynamic modes of the cobweb model organically. To validate the feasibility and effectiveness of SDO, four PV models with diverse characteristics including RTC France silicon solar cell, PVM 752 GaAs thin film cell, STM6-40/36 monocrystalline module, and STP6-120/36 polycrystalline module are employed. The experimental results comparing with ten state-of-the-art algorithms demonstrate that SDO performs better or highly competitively in terms of accuracy, robustness, and convergence. In addition, the sensitivity of SDO to variation of population size is empirically investigated. The results indicate that SDO with a relatively small population size can extract accurate and reliable parameters for PV models.
关键词： parameter extraction,cobweb model,solar photovoltaic models,supply-demand-based optimization,metaheuristic
更新于2025-09-11 14:15:04
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Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models
摘要： Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their e?cacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modi?ed search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by di?erential evolution are designed to balance the exploration and exploitation; (ii) a crossover operator based on the above modi?ed search strategies is introduced to meet the search-oriented requirements of di?erent dimensions; and (iii) a selection operator instead of the “generate-and-go” operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to ?ve PV types. Both single diode and double diode models are used to model these ?ve PV types. The good performance of MCSWOA is veri?ed by various algorithms.
关键词： metaheuristic,solar photovoltaic,whale optimization algorithm,parameter extraction
更新于2025-09-11 14:15:04
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[IEEE 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) - Ankara, Turkey (2018.10.19-2018.10.21)] 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) - Optimal Design of a Rooftop Wind-PV Hybrid System to Meet Energy Demand for a Typical Residential Home in 20-Year Lifetime Projection
摘要： The need for electrical power gradually increases every year due to population growth and economic development in particular in developing countries. The primary sources of electricity are usually fossil fuels such as coal, natural gas, oil etc. and major part of them is imported by those countries. This is unfortunately a typical indication of the energy dependence and play strategic role in economic development of the developing countries since some cannot switch to renewable energy use despite they have abundant renewable energy potential. The fact that the use of renewable energy is not widespread in those countries and it can be seen as not making enough investment by the government or private sector. This potential can be utilized by using on-grid/off-grid renewable systems in particular wind-PV systems ranging from power ratings of 1 to 10 kW in remote areas. To make such systems economic, power balance between generation and consumption should be maintained at hourly time slots in the day. One way to do that is to solve a discrete optimization problem and the solution can be achieved by a mathematical model satisfying the given constraints in a certain location. Unit sizing of a low power off-grid renewable system to meet power demand for a typical residential home in a location is achieved and the outcomes are meaningful and encouraging for widening renewable energy applications worldwide.
关键词： electric generation,metaheuristic techniques,optimal design,rooftop wind-PV hybrid system,renewable energy
更新于2025-09-04 15:30:14