Parameters Of Photovoltaic Models Research Articles

Parameter extraction of solar photovoltaic models using queuing search optimization and differential evolution

Given the photovoltaic (PV) model’s multi-model and nonlinear properties, extracting its parameters is a difficult problem to solve. Furthermore, because of the features of the problem, the algorithms that are used to solve it are subject to becoming stuck in local optima. Nonetheless, proper estimation of the parameters is essential due to the large impact they have on the performance of the PV system in terms of current and energy production. Moreover, the majority of the previously proposed algorithms have satisfactory results for determining PV model parameters. However, for precision and robustness, they generally use a lot of computational resources, such as the quantity of fitness assessments. For alleviating the previous problems, in this paper, an improved queuing search optimization (QSO) algorithm dependent on the differential evolution (DE) technique and bound-constraint amendment procedure, which is called IQSODE, has been presented to efficiently extract the PV parameter values for various PV models. The DE algorithm is applied to each solution generated by the QSO algorithm in order to increase population diversity. IQSODE is tested against other state-of-the-art algorithms. The practical and statistical findings show that IQSODE outperforms other methods in extracting parameters from PV models such as single diode, double diode, and photovoltaic module models. Also, the performance of the proposed algorithm is assessed utilizing two practical manufacturer’s datasheets (TFST40 and MCSM55). Statistically, the IQSODE outperforms other state-of-the-art algorithms in terms of convergence speed, reliability, and accuracy. Thus, the presented method is deemed to be a viable solution for PV model parameter extraction.

Parameter Estimation of Different Photovoltaic Models Using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm

The performance of a typical solar energy-based system can be improved by accurately modeling the current versus voltage characteristics of the involved solar cells. However, estimating the exact value of parameters related to solar cells is quite challenging. The optimization function, considering the current–voltage characteristics of solar cells, requires the solution of sophisticated non-linear and multi-modal optimization methods. So far, various optimization approaches have been reported. This paper proposes the application of a new hybrid algorithm, i.e., Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), which is a combination of two algorithms, i.e., Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) method. The hybrid PSOGSA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. Moreover, the developed hybrid algorithm is benchmarked using ten standard test functions to verify its efficiency. In this manuscript, monocrystalline and polycrystalline solar cells are considered. The parameter optimization results are obtained using PSOGSA and further compared with those obtained using other algorithms presented in the literature, such as PSO, GSA, MVO, HBO, PO and SCA. The complete error analysis is carried out for the modified single-diode model (MSDM), the modified double-diode model (MDDM), and the modified three-diode model (MTDM) of photovoltaic (PV) cells to prove the superiority of the PSOGSA. Moreover, statistical results are carried out based on Friedman’s ranking and Wilcoxon’s rank sum test. The comparison results show that the proposed PSOGSA is better than other algorithms in estimating the unknown PV model parameters.

A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction

Currently, solar energy is one of the leading renewable energy sources that help support energy transition into decarbonized energy systems for a safer future. This work provides a comprehensive review of mathematical modeling used to simulate the performance of photovoltaic (PV) modules. The meteorological parameters that influence the performance of PV modules are also presented. Various deterministic and probabilistic mathematical modeling methodologies have been investigated. Moreover, the metaheuristic methods used in the parameter extraction of diode models of the PV equivalent circuits are addressed in this article to encourage the adoption of algorithms that can predict the parameters with the highest precision possible. With the significant increase in the computational power of workstations and personal computers, soft computing algorithms are expected to attract more attention and dominate other algorithms. The different error expressions used in formulating objective functions that are employed in extracting the parameters of PV models are comprehensively expressed. Finally, this work aims to develop a comprehensive layout for the previous, current, and possible future areas of PV module modeling.

Boosted backtracking search optimization with information exchange for photovoltaic system evaluation

AbstractThe determination of photovoltaic (PV) parameters is of great importance for the reliability of solar system operation, continuity of the load power consumption, and control management of the energy source. Therefore, this study proposes an advanced backtracking search optimization algorithm (BSA) equipped with teaching and learning‐based optimization (TLBO), named TLBOBSA, to accurately simulate the PV model. During the evaluation of the proposed algorithm, the concept of teaching from TLBO is introduced into the BSA to guide optimal individuals, thus improving the convergence rate of the algorithm. The learning behavior among individuals in the student phase of TLBO facilitates interindividual learning and provides beneficial information for its evolution, which is introduced into the BSA to ensure the diversity of the population. The comprehensive test results of different PV module models in different environmental conditions show that the proposed algorithm is more advantageous for parameter extraction than other existing algorithms. This can be seen in the simulation experiments of two commercial PV models, where the simulated current is consistent with the measured current at each measured voltage. This demonstrates that the proposed TLBOBSA is an accurate and reliable tool for evaluating unknown parameters of PV models.

An enhanced Gradient-based Optimizer for parameter estimation of various solar photovoltaic models

The performance of a PhotoVoltaic (PV) system could be inferred from the features of its current–voltage relationships, but the PV model parameters are uncertain. Because of its multimodal, multivariable, and nonlinear properties, the PV model requires that its parameters be extracted with high accuracy and efficiency. Therefore, this paper proposes an enhanced version of the Gradient-Based Optimizer (GBO) to estimate the uncertain parameters of various PV models. The Criss-Cross (CC) algorithm and Nelder–Mead simplex (NMs) strategy are hybridized with the GBO to improve its performance. The CC algorithm maximizes the effectiveness of the population and avoids local optima trapping. The NMs strategy enhances the individual search capabilities during the local search and produces optimum convergence speed; therefore, the proposed algorithm is called a Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO). The primary objective of this study is to propose a simple and reliable optimization algorithm called CCNMGBO for the parameter estimation of PV models with five, seven, and nine unknown parameters. Firstly, the performance of CCNMGBO is validated on 10 benchmark numerical optimization problems, and secondly, applied to the parameter estimation of various PV models. The performance of the CCNMGBO is compared to several other state-of-the-art optimization algorithms. The results proved that the proposed algorithm is superior in handling the numerical optimization problem and obtaining the uncertain parameters of various PV models and performs better during different operating conditions. The convergence speed of the proposed CCNMGBO is also better than selected optimization algorithms with highly reliable output solutions. The average objective function value for case 1 is 9.83E−04, case 2 is 2.43E−04, and the average integral absolute error and relative error values are 1.05E−02 and 3.51E−03, respectively, for all case studies. With Friedman’s rank test values of 2.21 for numerical optimization and 1.66 for parameter estimation optimization, the CCNMGBO stood first among all selected algorithms.

Drunkard Adaptive Walking Chaos Wolf Pack Algorithm in Parameter Identification of Photovoltaic Module Model

The rapid and accurate identification of photovoltaic (PV) model parameters is of great significance in solving practical engineering problems such as PV power prediction, maximum power point tracking and battery failure model recognition. Aiming at the shortcomings of low accuracy and poor reliability and being easy to fall into local optimization when standard intelligent optimization algorithms identify PV model parameters, a novel drunken adaptive walking chaotic wolf swarm algorithm is proposed, which is named DCWPA for short. The DCWPA uses the chaotic map sequence to initialize the population, thus to improve the diversity of the initial population. It adopts the walking direction mechanism based on the drunk walking model and the adaptive walking step size to increase the randomness of walking, enhance the individual’s ability to explore and develop and improve the ability of algorithm optimization. It also designs the judgment conditions for half siege in order to accelerate the convergence of the algorithm and improve the speed of the algorithm. In the iterative process, according to the change of the optimal solution, the Hamming Distance is used to judge the similarity of individuals in the population, and the individuals in the population are constantly updated to avoid the algorithm from stopping evolution prematurely due to falling into local optimization. This paper firstly analyzes the time complexity of the algorithm, and then selects eight standard test functions (Benchmark) with different characteristics to verify the performance of the DCWPA algorithm for continuous optimization, and finally the improved algorithm is applied for parameter identification of PV models. The experiments show that the DCWPA has higher identification accuracy than other algorithms, and the results are more consistent with the measured data. Thus, the effectiveness and superiority of the improved algorithm in identifying solar cell parameters are verified, and the identification effect of the improved algorithm on solar cell parameters under different illumination is shown. This research provides a new idea and method for parameter identification of a PV module model.

Development of incremental average differential evolution algorithm for photovoltaic system identification

The determination of the behavior of photovoltaic (PV) systems is a current subject of research due to the increase in their share in electricity production. This identification problem is generally defined as the estimation of the unknown parameters of the equivalent circuit model. The parameters of the PV model are optimized by minimizing the error between the measured data from the actual PV cell and the results of the model. An efficient optimizer tool is required to obtain the best model’s parameter. This paper presents a novel metaheuristic named incremental average differential evolution algorithm (IncADE) for parameter estimation of PV models. The IncADE is a new variant of average differential evolution (ADE) that enhances the global search ability of ADE algorithm by the incremental population strategies. The performance of the developed IncADE is firstly evaluated on well-known benchmark functions, and the experimental results show that the proposed method improves the accuracy of the concluding solutions and the convergence performance of the basic ADE. Then, the IncADE is employed to estimate the optimal parameters of different PV models, which are single diode, double diode and PV module. Experimental results prove the superiority of IncADE on parameter estimation in terms of accuracy and computational efficiency by comparing with ADE and other metaheuristic algorithms.

An Enhanced Moth-Flame Optimization with Multiple Flame Guidance Mechanism for Parameter Extraction of Photovoltaic Models

How to accurately and efficiently extract photovoltaic (PV) model parameters is the primary problem of photovoltaic system optimization. To accurately and efficiently extract the parameters of PV models, an enhanced moth-flame optimization (EMFO) with multiple flame guidance mechanism is proposed in this study. In EMFO, an adaptive flame number updating mechanism is used to adaptively control the flame number, which enhances the local and global exploration capabilities of MFO. Meanwhile, a multiple flame guidance mechanism is designed for the full use of the position information of flames, which enhances the global diversity of the population. The EMFO is evaluated with other variants of the MFO on 25 benchmark functions of CEC2005, 28 functions of CEC2017, and 5 photovoltaic model parameter extraction problems. Experimental results show that the EMFO has obtained a better performance than other compared algorithms, which proves the effectiveness of the proposed EMFO. The method proposed in this study provides MFO researchers with ideas for adaptive research and making full use of flame population information.

Estimation of electrical parameters of photovoltaic panels using heap‐based algorithm

Parameter estimation for solar photovoltaic (PV) models is a challenging issue due to the complex nonlinear multivariable of the current-voltage and power-voltage characteristics. In this article, an improved heap-based algorithm (IHBA) is proposed to improve the performance of a recently published algorithm called heap-based algorithm (HBA). The HBA's performance is enhanced by applying an effective exploitation feature to boost the searching around the leader position with the goal of enhancing its global search capabilities and avoiding becoming trapped in a local optimum. The proposed IHBA and the standard HBA are developed considering the practical limits of the electrical parameters of PV models to minimize the root mean square error (RMSE) between the experimental and simulated results. The numerical analyses for the PVM-752GaAs PV module including single-diode model (SDM), double-diode model (DDM) and triple-diode model (TDM) are investigated to estimate five, seven, and nine electrical parameters of these models, respectively. Besides, different recent optimization techniques are simulated with fair comparisons, which are forensic-based investigation (FBI), equilibrium optimizer (EO), Jellyfish Search (JFS), HBA, Marine Predator Algorithm (MPA) and Enhanced MPA (EMPA), and are compared with the proposed IHBA. Several separate runs are illustrated for the proposed IHBA in comparison with others, whereas the whisker box plot and T-tests are activated to evaluate their effectiveness metrics. The simulation results derive higher superiority of the proposed IHBA with the minimum RMSE objective and standard deviation of (0.000228 and 3.7 × 106), (0.000184 and 1.93 × 105) and (0.000017 and 2.11 × 105) for SDM, DDM, and TDM, respectively, with respect to the standard HBA, other recent and reported optimization techniques. Additionally, the P-indicator is applied in this study to illustrate the evidence for the alternative hypothesis. Small values of the P-indicator for the proposed IHBA are obtained compared to the standard HBA and other recent optimization techniques, where IHBA achieves p-value of 1.0336 × 1017, 4.037 7× 1011 and 2.1490 × 1010 for SDM, DDM and TDM, respectively. The H-value is always one which elucidates that the hypothesis probability is not more than 5% for all algorithms for SDM, DDM and TDM. Moreover, the proposed IHBA demonstrates higher efficiency as it provides the first ranks in the confidence interval values compared with other algorithms. Furthermore, the proposed IHBA is significantly employed for the SQ_150 PV module considering the TDM with diverse solar irradiance and temperatures, where significant closeness between the emulated and experimental P–V curves is evidently demonstrated.

Parameter identification of solar photovoltaic cell and module models via supply demand optimizer

The extraction of photovoltaic (PV) module parameters is regarded as a critical topic for assessing the performance of PV energy systems. The Supply-Demand-Based Optimization Algorithm (SDOA) is employed in this work to extract the unknown parameters of PV models. The SDOA mimics the stability and instability modes between the supply and the demand one in order that the quantity and price of commodities converge to the equilibrium point after a specified number of repetitions. It is frequently used to handle complicated nonlinear problems because of its ease of implementation and powerful optimization capabilities. The Triple-Diode Model (TDM) is extensively adopted in PV module mathematical models. The optimal nine TDM parameters are determined for the PVM 752GaAs PV thin film cell, whereas other solar irradiation and temperature values are used for the SQ 150 and MSX 60 modules. When used on the TDM model, the SDOA was used to verify the fitness values and standard deviation errors. Furthermore, the obtained result achieved by SDOA are contrasted with new techniques established in 2020 which are Backtracking Search Algorithm (BSA), Grey Wolf Optimizer (GWO), Bernstein-Levy Search Differential Evolution Algorithm (BSDE), Crow search Optimizer (CSO), and Manta Ray Foraging Optimizer (MRFO). For accurate and consistent results, thirty runs of the algorithm are executed for the modules and the standard deviations of fitness values are less than 1 × 10–18 for the triple diode model. In addition to this, a practical PV power plant system that lie in the Guizhou Power Grid of China is used to validate the efficiency of SDOA compared to other recent algorithms. Thus, SDOA is considered as a competitive optimizer among other reported techniques in the literature or recent techniques for PV parameter extraction.

An enhanced Harris Hawk optimization algorithm for parameter estimation of single, double and triple diode photovoltaic models

Due to the rapid development of photovoltaic (PV) system and spreading of its application, the accuracy of modeling of solar cells, as the main and basic element of PV systems, is gaining relevance. In this paper, an Enhanced Harris Hawk Optimization Algorithm (EHHO) is proposed and applied for estimating the required parameters of different PV models in an effective and accurate way. Harris Hawk Algorithm (HHO) is based on Hawks ways in hunting and catching their preys. The HHO utilizes two phases including exploration and exploitation. The main purpose of proposed enhancement is to improve the second phase of HHO. This enhancement is performed on the exploration phase by fluctuating toward or outward the best optimal solution using sine and cosine functions. Both conventional and proposed algorithms are applied for single, double and triple diode PV models. In order to test the applicability and robustness of proposed algorithm, it is applied for estimating the parameters of different real PV systems and compared with other recent optimization algorithms. The results show that the proposed EHHO is more accurate than conventional HHO and other algorithms.

Heterogeneous differential evolution algorithm for parameter estimation of solar photovoltaic models

The behavior of a photovoltaic (PV) system can be derived from its current–voltage characteristics, depending on its unknown circuit model parameters. It is significant to accurately and efficiently extract the parameters of the PV model because of the nonlinear, multivariable, and multimodal characteristics. Many meta-heuristic algorithms have been proposed, in which differential evolution (DE) is known for simple structure, ease of use, and fast convergence. However, the performance of DEs still has room for further improvement because exploration and exploitation are highly coupled during the iterative process for most DE variants. To solve this problem, in this paper, a novel heterogeneous differential evolution algorithm (HDE) is presented to extract the parameters of PV. By proposing a concise heterogeneous mechanism, introducing two new mutation strategies, and establishing an implicit information exchange mechanism, exploration and exploitation processes of the HDE are simultaneously enhanced without one process crippling the other. The efficiency of the HDE has been demonstrated by considering six different PV modules models including the single diode model, the double diode model, the triple diode model, and three different PV modules. The statistical results indicate that the HDE is a representative DE variant in terms of robustness, reliability, solution accuracy, and execution efficiency compared with other 14 state-of-the-art algorithms for the estimation of PV model parameters.

Adaptive slime mould algorithm for optimal design of photovoltaic models

AbstractSolar energy is becoming more popular as it is a clean source of electricity. The design of photovoltaic (PV) cells has therefore captivated experts worldwide. The two key issues are the lack of an excellent model to define solar cells and the lack of data regarding PV cells. This scenario even impacts solar module performance (panels). The behavior of solar cells is described by the current versus voltage. Considering these values, the design challenge entails solving complicated nonlinear multimodal objectives. Different methods to figure out the parameters of the PV cells and panels have been suggested. They do not come up with the best solutions most of the time. Hence, a powerful and reliable optimizer is needed to derive the optimal parameters of these models. To this end, this study has developed an adaptive slime mould algorithm (ASMA) as a robust and precise optimization method. To implement the ASMA, four improvements are proposed: (1) a trigonometric‐based mutation and a double‐based best mutation are introduced to promote the global and local search; (2) a suitable mechanism to adaptively select the control parameters of the SMA; (3) a local escaping strategy; (4) an opposition‐based learning operator to improve the best solution. The ASMA is employed to derive optimal parameters of PV models and assessed utilizing a total number of eight well‐known optimization algorithms. The findings show that the ASMA is very competitive in terms of accuracy and convergence speed and that this is supported by a wealth of evidence. As a result, when assessing the parameters of the PV model, ASMA is a very efficient and robust optimizer. The source codes of the proposed ASMA will be uploaded for the public at http://imanahmadianfar.com and http://aliasgharheidari.com.

Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning

The performance of photovoltaic (PV) cell is affected by the model structure and corresponding parameters. However, these parameters are adjustable and variable, which play an available role in regarding to the efficiency and effectiveness of PV generation. Due to strong non-linear characteristics, existing PV model parameters identification methods cannot easily obtain accurate solutions. To tackle this, this paper proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOL), named DOLADE. In DOLADE, the opposite learning method expands the current elite population and the population of poor performance, improving the particles’ exploration capability. In the process of particles work, the searching area of particles is adjusting dynamically so that the particles’ exploitation capability is enhanced. The experimental data of different types of PV are tested, respectively. Three PV models are used to verify the new strategy’s accuracy and effectiveness. The proposed DOLADE is compared with several general advanced algorithms, and comprehensive experimental results are demonstrated. The results illustrate that DOLADE well extracts optimal parameters for each PV cell model and brought great competition in terms of accuracy, reliability, and computational efficiency in solving the problem.

Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm

Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the parameters of the solar PV models. However, it is still a challenging task to accurately, reliably, and quickly estimate the unknown parameters of PV models. This paper proposes a novel hybrid seagull optimization algorithm (HSOA) for estimating the unknown parameters of PV models effectively and accurately. In proposed HSOA, the personal historical best information is embedded into position search equation to improve the solution precision. A novel nonlinear escaping energy factor based on cosine function is presented for balancing global exploration and local exploitation. The differential mutation strategy is introduced to escape from the local optima. We firstly select twelve classical benchmark test functions to investigate the feasibility of HSOA, and experimental results show that HSOA is superior to most compared methods. Then, HSOA is used for solving parameters estimation problem of three benchmark solar PV models. The comparison results demonstrate that HSOA is superior to BOA, GWO, WOA, HHO, SOA, EEGWO, and ISCA on solution quality, convergence and reliability.

An Improved Levenberg–Marquardt Approach With a New Reduced Form for the Identification of Parameters of the One-Diode Photovoltaic Model

Abstract Building a highly accurate model for solar cells and photovoltaic (PV) modules based on experimental data is becoming increasingly important for the simulation, evaluation, control, and optimization of PV systems. Powerful, accurate, and more robust optimization algorithms are needed to solve this problem. In this study, a new optimization approach based on the Levenberg–Marquardt algorithm (ImLM) is proposed to estimate the parameters of PV cells and modules and simulate their electrical behavior under all environmental conditions efficiently and accurately. To avoid the premature convergence of the Levenberg–Marquardt algorithm and the long computation time caused by a bad choice of initial values, we propose a new approach. This is a new reduced form leading to a nonlinear relationship of the series resistance and thus allowing to calculate the optimal initial values of the model parameters. Comparisons with other published methods show that the proposed approach gives not only a more accurate final solution but also a fast convergence speed and a better stability. Furthermore, tests on three PV modules of different technologies (multi-crystalline, thin film, and monocrystalline) reveal that the proposed algorithm performs well at different irradiations and temperatures. These results confirm that the ImLM approach is a valuable tool and can be an effective and efficient alternative for extracting PV model parameters and simulating PV module behavior under different conditions.

Modified Salp Swarm Optimization for Parameter Estimation of Solar PV Models

The identification of parameters in solar cell models is still a major challenge in photovoltaic (PV) system simulation and design. Because of its more basic ideas, efficiency, adaptability, swarm and evolutionary optimization algorithms, as well as simple procedural frameworks, have been generally used in industry with real-world problems. However, due to the nonlinearity and complication of the PV parameter identification, the obtained solutions from swarm and evolutionary optimizers were immature. An efficient metaheuristic approach for identifying PV model parameters based on the salp swarm algorithm (SSA) is presented in this paper. In the suggested modified salp swarm optimization (MSSA), the leaders and followers will be updated based on the new formulas. The algorithm’s exploration potential is increased by this modification while also preventing it from converge prematurely. The behavior of the suggested technique is verified using benchmark functions, and the outcomes are contrasted with those of SSA and other successful optimization approaches. The suggested MSSA detects numerous characteristics in the PV model include single diode, double diode, and PV modules, in the most efficient way possible. According to the simulation results, MSSA outperforms the competition and may produce better optimal solutions. The findings demonstrate that the best value of RMSE obtained by MSSA is up to 69 percent lower than other methods and is nearly 5.6 percent lower than that assessed by SSA.

Parameter extraction of photovoltaic models using a memory-based improved gorilla troops optimizer

The parameter extraction of the PV model is a challenging issue owing to its multi-model and nonlinear characteristics. Moreover, these characteristics of the problem render the algorithms tackling it susceptible to being stuck in local optima. Nevertheless, it is imperative to accurately estimate the parameters due to their significant impact on the efficiency of the Photovoltaic (PV) system in terms of current and power generation. Hence, this paper proposes a new metaheuristic algorithm called the Memory-based Improved Gorilla Troops Optimizer (MIGTO). Furthermore, the Explorative Gorilla with an Adaptive Mutation Mechanism (EGAMM) and the Gorilla memory-saving technique are two major improvements that support the MIGTO superiority. EGAMM primarily comprises two main operators (explorative gorilla operator, adaptive mutation operator). The explorative gorilla operator enhances the search capability to explore new gorillas’ positions in the search space to escape from local optima with a small probability of 0.1. Otherwise, MIGTO updates the new gorilla position using the adaptive mutation operator by selecting two random gorillas. Additionally, the GMS technique promotes the quality of the gorillas by keeping a history of the previous positions to be compared to the current ones. Several experiments are conducted between MIGTO and other well-known metaheuristic algorithms. The experimental results and statistical analyses prove the outperformance of MIGTO in effectively extracting the parameters of different PV models, including the single diode model, the double diode model, the triple diode model, and the photovoltaic module model. Additionally, the performance of the algorithms is tested using the manufacturer’s datasheet.

Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation

Extracting the optimum parameters of solar photovoltaic (PV) model using the experimental data of current–voltage is very critical in simulating, controlling, and optimizing the PV systems. One of the important problems encountered in modeling and simulating is to find a model that can extract parameters from PV models quickly, accurately, and reliably. Based on this motivation, the goal of this study is to suggest an improved algorithm, namely genetic algorithm based on non-uniform mutation (GAMNU), in order to approximate efficiently the parameters of solar cells and PV modules. In GAMNU, non-uniform mutation operator is used to maintain diversity in the explored solutions and the crossover operator follows an adaptive search strategy, which consists of searching the entire space at the beginning while maintaining a focused search when the population tends to converge in a certain region of the search space. The performance of the method is comprehensively evaluated on different solar cell models, including single and double diode, and single diode PV modules, of a R.T.C France silicon solar cell, ESP-160 PPW PV, STP6-120/36 and Photowatt-PWP201 module. The results obtained from single and double diode models for R.T.C France are respectively 9.8618×10-4 and 9.8683×10-4, and for Photowatt-PWP201, STP6-120/36 and ESP-160 PPW are 2.3824×10-3 2.382420230900×10-3, 1.6735×10-21.6735786505085×10-2 and 8.2942×10-2 8.2942×10-2. The statistical obtained results show that the proposed method has very competitive performance in terms of accuracy and reliability when compared to other advanced algorithms. Therefore, the proposed algorithm is highly useful to extract the parameters of solar PV models.

A novel levy flight trajectory-based salp swarm algorithm for photovoltaic parameters estimation

Simulation and optimization of photovoltaic (PV) systems are recently urging as a hotspot in the field of renewable energy. For this, different models have been introduced to simulate the behavior of real PV cells and modules. An accurate estimation of unknown parameters of PV models is necessary to achieve a good performance of photovoltaic systems. This paper presents a new metaheuristic algorithm called Levy flight trajectory-based salp swarm algorithm (LSSA), which estimated single diode (SD), double diode (DD) and PV module models parameters. Compared with the original salp swarm algorithm (SSA), LSSA benefits from enhanced population diversity due to Levy flight trajectory characteristic of long jump steps. In order to validate its efficiency, the proposed technique was compared with other well-known meta-heuristics for estimating PV parameters in the SD, DD and PV modules. The experimental and comparative results demonstrated that LSSA was able to find extremely accurate solutions with high reliability and offers very competitive results for the problem of estimating PV parameters.