Solar Photovoltaic Models Research Articles

A novel hybrid algorithm based on improved marine predators algorithm and equilibrium optimizer for parameter extraction of solar photovoltaic models

In the field of solar photovoltaic (PV) systems, the accurate and reliable extraction of parameters from PV models is crucial for effective simulation, evaluation, and control. Although various optimization algorithms have been widely used for parameter extraction in solar PV systems, the accuracy and reliability of the parameters extracted by these methods usually fall short of the expected standards. To address these shortcomings, a novel hybrid algorithm that combines the improved marine predators algorithm (MPA) with the equilibrium optimizer (EO), named IMPAEO, is proposed. In the IMPAEO, an elite opposition-based learning strategy is introduced to expand the exploration range of the algorithm to enhance population diversity; an adaptive weight coefficient is employed to improve the updating rule of the MPA, facilitating a balance between global exploration and local exploitation; the EO operator is integrated to enhance the performance of the MPA in the local exploitation phase to improve the solution quality and convergence speed; and the linear population size reduction (LPSR) technique is introduced to dynamically reduce the population size and improve the search performance. The effectiveness of the proposed IMPAEO is validated using the CEC2017 benchmark test suite, which is also applied to extract parameters of the single diode model, the double diode model, and three different PV modules. Comparative analysis with other algorithms demonstrates that the IMPAEO exhibits significant advantages in terms of solution quality, convergence speed, and algorithm stability. Consequently, the proposed IMPAEO not only proves to be efficient and reliable in parameter extraction for solar PV models but also offers a promising alternative in this field.

A tree seed algorithm with multi-strategy for parameter estimation of solar photovoltaic models

Tree seed algorithm, which is one of the metaheuristics algorithms recently proposed for the solution of continuous optimization problems, has an effective algorithmic structure inspired by the relation between trees and seeds. At the same time, the use of two different solution generation mechanisms by depending on the control parameter in TSA aims to balance the exploration and exploitation capabilities of the algorithm. However, when the structure of the algorithm is examined in detail, it is seen that there are some disadvantages such as loss of population diversity and getting stuck in local minimums. To overcome these disadvantages in the basic algorithm, three different approaches (self-adaptive weighting mechanism, chaotic elite learning approach and experience-based learning method) were proposed to TSA under the name of multi-strategies in this study. The algorithm improved with these approaches is named as the multi-strategy-based tree seed algorithm (MS-TSA). MS-TSA was first tested on CEC2017 functions. Then MS-TSA was applied to the problems in the CEC2020 competition and compared with the results of the best performing algorithms in this competition. As a result of the comparisons, MS-TSA was found to be a competitive method on solving benchmark functions. Then, parameter estimation of single diode, double diode and photovoltaic module models using the input data of various solar panels was carried out by the MS-TSA. The results obtained with MS-TSA were compared with both the results of the basic TSA and the results of well-known algorithms in the literature. The results obtained are 9.8642E-04, 9.8356E-04, 2.4251E-03, 1.7534E-03 respectively. As a result of the comparative analysis, the lowest RMSE value was obtained by MS-TSA. In addition, comprehensive performance analyzes of the algorithms were made with the convergence curve, boxplots, current (I) - voltage (V) and power (P) - voltage (V) characteristic curves obtained according to the experimental results. As a result of the experiments and analyses, MS-TSA was found to be a more successful method than the compared algorithms in parameter estimation of PV models.

Parameters estimation of complex solar photovoltaic models using bi-parameter coordinated updating L-SHADE with parameter decomposition method

Accurate estimation of unknown parameters of complex photovoltaic models is crucial to whether photovoltaic generators can efficiently convert energy. When a photovoltaic model has multiple diode branches, its complexity increases geometrically. To address the problems of high complexity and difficulty in estimation, this paper proposes an effective improved algorithm based on success-history adaptation differential evolution with linear population size reduction (L-SHADE)—Bi-parameter coordinated updating L-SHADE with parameter decomposition method (CSpL-SHADED). First in CSpL-SHADED, dynamic crossover rate ranking technology is developed to bridge the relationship between individuals and crossover rates, thereby improving effective mutation capabilities. In addition, a dynamic sub-population mechanism is also proposed to divide the entire population into multiple sub-populations so that they are evenly distributed in the search space, so the search ability of individuals in local areas is improved. Secondly, the unknown parameters of solar photovoltaic models of different complexity are effectively decomposed into linear and nonlinear parameters by the decomposition method. The nonlinear parameters are accurately estimated by CSpL-SHADED, and the linear parameters are calculated based on the constructed matrix equation. Through experiments on four solar photovoltaic models of different complexity, CSpL-SHADED showed strong competitiveness to varying degrees compared to the comparative algorithms.

A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality.

Parameters optimization of solar PV cell using genetic-iterative algorithm

Accurately determining optimal parameters from current-voltage (IV) data in solar photovoltaic (PV) models is crucial for effective system simulation and control. In this study, we propose a novel approach that combines genetic algorithm and iterative techniques maximizing their strengths, and exploiting the influence of each parameter on the IV curve to categorize them into groups. This adaptable method can adjust the interval of each parameter to different scenarios during optimization. We evaluated the method across various solar cell models including both the ‘SDM’ and ‘SDM-based PMM’, achieving notable accuracy and reliability compared to other advanced meta-heuristic algorithms. The results indicate a value of 7.3870e-5 for the SDM and 9.3365e-4 for the PMM (STM6-40/36). The proposed algorithm demonstrates notable accuracy and reliability, highlighting its usefulness in accurately determining parameters in solar PV models.

Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation

AbstractIn the pursuit of enhancing the efficiency of solar cells, accurate estimation of unspecified parameters in the solar photovoltaic (PV) cell model is imperative. An advanced salp swarm algorithm called the Super‐Evolutionary Nelder‐Mead Salp Swarm Algorithm (SENMSSA) is proposed to achieve this objective. The proposed SENMSSA addresses the limitations of SSA by incorporating a super‐evolutionary mechanism based on a Gaussian‐Cauchy mutation and a vertical and horizontal crossover mechanism. This mechanism enhances both the global optimization capabilities and the local search performance and convergence speed of the algorithm. It enables a secondary refinement of the global optimum, unlocking untapped potential in the solution space near the global optimum and elevating the algorithm's precision and exploitation capabilities to higher levels. The Nelder‐Mead simplex method is further introduced to enhance local search capabilities and convergence accuracy. The Nelder‐Mead simplex method is a versatile optimization algorithm that improves local search by iteratively adjusting a geometric shape (simplex) of points. It operates without needing derivatives, making it suitable for non‐smooth or complex objective functions. To assess the efficacy of SENMSSA, a comparative analysis is conducted against other available algorithms, namely SSA, IWOA, SCADE, LWOA, CBA, and RCBA, using the CEC2014 benchmark function set. Subsequently, the algorithm was employed to determine the unknown PV parameters under fixed conditions for three different diode models. Additionally, SENMSSA is utilized to estimate PV parameters for three commercially available PV models (ST40, SM55, KC200GT) under varying conditions. The experimental results indicate that the SENMSSA proposed in this study displays a remarkably competitive performance in all test cases compared to other algorithms. As such, we consider that the SENMSSA algorithm constitutes a reliable and efficient solution for the challenge of PV parameter estimation.

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

AbstractThe pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems.

Parameter identification of solar photovoltaic models by multi strategy sine–cosine algorithm

AbstractAccurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine–cosine algorithm (SCA), named enhanced sine–cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single‐diode model, the double‐diode model, the three‐diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV‐PWP201, STM6‐40/36, and STP6‐120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells.

Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm

Given the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by the propensity of conventional algorithms to get trapped in local optima due to the complex nature of the problem. Accurate parameter estimation, nonetheless, is crucial due to its significant impact on the PV system’s performance, influencing both current and energy production. While traditional methods have provided reasonable results for PV model variables, they often require extensive computational resources, which impacts precision and robustness and results in many fitness evaluations. To address this problem, this paper presents an improved algorithm for PV parameter extraction, leveraging the opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with opposition-based learning, provides an enhanced exploration capability and efficient exploitation of the search space, helping to mitigate the risk of entrapment in local optima. The proposed OBEDO algorithm is rigorously verified against state-of-the-art algorithms across various PV models, including single-diode, double-diode, three-diode, and photovoltaic module models. Practical and statistical results reveal that the OBEDO performs better than other algorithms in estimating parameters, demonstrating superior convergence speed, reliability, and accuracy. Moreover, the performance of the proposed algorithm is assessed using several case studies, further reinforcing its effectiveness. Therefore, the OBEDO, with its advantages in terms of computational efficiency and robustness, emerges as a promising solution for photovoltaic model parameter identification, making a significant contribution to enhancing the performance of PV systems.

Enhancing Photovoltaic Cell Parameters Extraction through Grey Wolf Optimizer

Efficient and precise parameter extraction from solar Photovoltaic (PV) models is paramount for the comprehensive simulation, assessment, and management of PV systems. Despite the proliferation of analytical, numerical, and metaheuristic algorithms aimed at this task in recent years, the extraction of parameters remains a formidable obstacle. This study employs the Grey Wolf Optimizer (GWO) to extract the five key parameters of the RTC France solar cell. The GWO’s performance is systematically compared with metaheuristic algorithms such as Enhanced Chaotic JAYA (CJAYA) and Performance-Guided JAYA (PGJAYA). The study showcases the prowess of GWO in optimizing PV parameters, marking a significant stride forward in the realm of optimization techniques for PV cell modeling. Through meticulous analysis using MATLAB-SIMULINK, the research unveils the profound effectiveness of GWO in navigating the intricate landscape of parameter extraction within PV systems.

Solar photovoltaic cell model optimal parameter identification by using an improved chimp optimization algorithm

Identifying the parameters of solar photovoltaic (PV) cell models accurately and reliably is crucial for simulating, evaluating, and controlling PV systems. For this reason, we present an improved chimp optimization algorithm (IChOA) for the generation of precise and reliable solar PV cell models. As a new and improved version of the standard chimp optimization algorithm (ChOA), IChOA embeds two mutation rules in ChOA that include the elite opposition-based learning and visual search mechanism. The first rule is applied to strengthen global exploration capacity of ChOA, and the second one is utilized to enhance ChOA’s local exploitation ability (convergence accuracy). Based on the six benchmark test functions with different characteristics, the effectiveness of IChOA is evaluated by comparing to other five well-known optimization algorithms. The results suggest that IChOA offers superior performance over other competing algorithms. Finally, IChOA’s performance is confirmed through optimizing parameters for three widely employed mathematical models, specifically the single diode model, the double diode model, and the multi-cell PV modules. The findings prove the excellent performance of the suggested approach.

Performance evaluation of logarithmic spiral search and selective mechanism based arithmetic optimizer for parameter extraction of different photovoltaic cell models.

The imperative shift towards renewable energy sources, driven by environmental concerns and climate change, has cast a spotlight on solar energy as a clean, abundant, and cost-effective solution. To harness its potential, accurate modeling of photovoltaic (PV) systems is crucial. However, this relies on estimating elusive parameters concealed within PV models. This study addresses these challenges through innovative parameter estimation by introducing the logarithmic spiral search and selective mechanism-based arithmetic optimization algorithm (Ls-AOA). Ls-AOA is an improved version of the arithmetic optimization algorithm (AOA). It combines logarithmic search behavior and a selective mechanism to improve exploration capabilities. This makes it easier to obtain accurate parameter extraction. The RTC France solar cell is employed as a benchmark case study in order to ensure consistency and impartiality. A standardized experimental framework integrates Ls-AOA into the parameter tuning process for three PV models: single-diode, double-diode, and three-diode models. The choice of RTC France solar cell underscores its significance in the field, providing a robust evaluation platform for Ls-AOA. Statistical and convergence analyses enable rigorous assessment. Ls-AOA consistently attains low RMSE values, indicating accurate current-voltage characteristic estimation. Smooth convergence behavior reinforces its efficacy. Comparing Ls-AOA to other methods strengthens its superiority in optimizing solar PV model parameters, showing that it has the potential to improve the use of solar energy.

Chaos inspired invasive weed optimization algorithm for parameter estimation of solar PV models

High performance solar photovoltaic models require precise knowledge of solar PV cell parameters. Numerous methods based on both deterministic and meta-heuristics have been developed for identifying solar cell parameters. However, the presented methods in the literature have a heavy computational load and limited ability to extract crucial parameters due to nonlinear dynamics of solar PV systems. In addition, because they rely on approximations to determine the objective function, the preceding state-of-the-art parameter estimation techniques do not provide accurate results. Thus, a novel chaos-inspired invasive weed optimization (CIIWO) has been developed for accurate solar PV system parameter estimation. Adding a chaotic map to IWO improves the performance of suggested method by expanding the search space globally. Moreover, to cope with the inadequacy in state-of-art objective functions, Newton Raphson approach has been combined with proposed CIIWO algorithm. The suggested approach for solar cell parametric identification has been tested on one-diode, two-diode, and three-diode models. By contrasting the outcomes with nine contemporary optimization strategies for parameter estimation, the superiority of the suggested algorithm has been demonstrated. Commercial PV cell RTC France has been used for the experimental validation. Comprehensive study of experimental data validates the efficacy and stability of the suggested algorithm.

Assessment of hybrid MPPT for SPV based high gain cubic boost converter interfaced with reduced switch multilevel inverter for power quality enhancement

A high-gain cubic boost converter (HG-CBC) with hybrid-based maximum power point tracking (MPPT) through a neural network (NN) aided by the P&O technique (HNN-PO MPPT) has been suggested to acquire optimum power from a solar photovoltaic (SPV) model under varying climatic conditions. The SPV’s output is enhanced using the suggested HG-CBC as per the requirement. A detailed comparison of different conventional boost converters (BC) with the suggested HG-CBC is presented, mainly highlighting part count and boost factor (B). Using the MATLAB tool, the functionality of the developed HNN-PO MPPT technique has been examined for constant and different irradiation (G) levels. The hybrid-based MPPT helps quickly attain maximum power point (MPP) with minimum oscillations at the output. The convergence period is very short with high precision in comparison with P&O and NN MPPT. The results are examined between the suggested and traditional MPPT methods in relation to the percentage of oscillations and rise time. The Reduced Switch Multilevel Inverter (RSMLI) is proposed to integrate the SPV with the RL load. The RSMLI is compared with the conventional standard five-level MLI in relation to the quantity of DC sources, diodes, switches, capacitors, and other parts utilized. The suggested MLI involves a reduced switch count, which mitigates the overall losses during switching and hence improves the efficiency of an inverter. MLI switches are controlled using a sine Pulse Width Modulation (PWM) technique. The THD of the output current of a five-level RSMLI is 4.47%, and it falls within the IEEE 519 norm. Hence, output power quality is enhanced.

A Solar PV Model Parameter Estimation Based on the Enhanced Self-Organization Maps

Solar photovoltaic (PV) is a commonly utilized renewable energy source, with PV cells often being modeled as electric circuits. The identification of suitable circuit model parameters for PV cells is vital for performance evaluation, efficiency calculations, and the implementation of maximum power point tracking in solar PV systems. However, modeling the solar PV system is a nonlinear problem that requires an efficient algorithm. In this paper, we employ the enhanced self-organization maps (EASOM) to efficiently reduce the search space for parameter estimation in solar PV models. Our algorithm trains the SOM network on a subset of solutions, identifies the top solution's neural unit, generates a population of potential solutions, and selects the best candidate using a cost function, which represents the best PV model parameters obtained. The performance of EASOM is verified by extracting the parameters of the single diode (SDM) and double diode (DDM) models for the STM6-40/36 PV module. EASOM outperformed state-of-the-art algorithms with the lowest RMSE and MSE values of 8.3 mA and 6.87e-05 and achieved the lowest maximum error values of 27.37 mA and 20.52 mA, as well as low power error of 66.04 mW and 62.8 mW for SDM and DDM models.

The Impact of Roof Coating and Solar PV System in the Tropical Region of Ghana

Adding PV module to roof has impacts on building`s electricity energy consumption. The aim of this paper is to assess the energy consumption performance of buildings by integrating solar Photovoltaic (PV) system into buildings with roof coating. An experiment was conducted to verify the efficient outcome of PV module using a building from the Anaji area of Takoradi in the Western region of Ghana. A framework energy model was proposed to analyse the integrated contribution of coating and PV performance using PVSOL. The temperature of the coated roof surfaces underneath the PV panels were significantly lower than that of the exposed roof in the daytime. The system integrated energy efficiency for flat and tilted overhead PV roofs are 63.35 % and 62.73 %, respectively. Using the mean absolute percentage error (MAPE) performance criterion, the monthly energy savings for coated roofs with solar PV is 28.86 kW or GH¢ 340.21; while the monthly energy savings for coated roofs without PV is 25.91 kW or GH¢ 303.00. Overall, the proposed integrated coated roof with PV outperforms the coated roof without PV. Validating the model, the mean relative errors (MRE) were all below 10%, while the accuracy of Power-Added Efficiency (PAE) were all beyond 95%. Thus, the proposed integrated roof coating and solar PV model for optimizing energy consumption is reliable.

A solar PV model parameters estimation based on an improved manta foraging algorithm with dynamic fitness distance balance

Accurately simulating and operating photovoltaic (PV) modules or solar cells requires determining specific model parameters based on experimental data. Extracting these parameters is crucial for analyzing system performance under various conditions such as temperature and sunlight variations. However, modeling solar photovoltaic systems is inherently nonlinear, which calls for an efficient algorithm. In this study, we employ the MRFO-dFDB (Manta Ray Foraging Optimization with dynamic Fitness Distance Balance) algorithm, which utilizes fitness distance balance to balance the exploration and exploitation of the search area when assessing parameters in solar PV models. By applying MRFO-dFDB to extract parameters from the STP6-120/36 and Photowatt-PWP201 solar modules, we observe exceptional predictive performance for both single diode (SDM) and double diode (DDM) models. MRFO-dFDB exhibits superior performance compared to state-of-the-art methods. It achieves lower Root-Mean-Square Error (RMSE) values, specifically < 15.3 mA for the STP6-120/36 module and <2.4 mA for the Photowatt-PWP201 module. Additionally, it demonstrates lower maximum errors of 39.02 mA and 5.33 mA, as well as lower power errors of 155.42 mW and 14.122 mW, for the STP6-120/36 and Photowatt-PWP201 solar modules, respectively. Furthermore, it exhibits excellent performance with faster computation speed (< 30.1 seconds in all tests), further emphasizing its superiority.

Design of Grid-Connected rooftop Photovoltaic system for leakage current reduction using optimization algorithms

Rooftop solar power systems refer to the organization of photovoltaic (PV) panels on the rooftop of a building. They are a feasible substitute for land-based solar arrays, and they are being used in different Asian countries. Thus regulations, grid connectivity, off-take, and land acquisition are the main obstacles that renewable energy plant owners face. At high penetration of rooftop PV, this voltage variability reduces the stability of the grid due to transient imbalance in load and generation and causes voltage and frequency to exceed set limits if not countered by power controls. To reduce this power loss in the transmission cable, a Manta Ray Foraging Optimization (MRFO) process is presented in this research. This method also enhances the PV system's performance. A Non-isolated Buck-Boost Converter is connected between the PV and the grid. For renewable energy applications, a new non-isolated Buck-Boost converter with high voltage gain and positive output voltage has been developed. This converter receives the voltage of input from the PV and gives the output as boosting or lowering the voltage. To improve the current tracking with high power conversion efficiency, a Finite Control Set Model Predictive Controller (FCS-MPC) is introduced for this converter. The proposed optimization algorithm is utilized to tune the controller and improves the dynamic process of the power plant system connected to the grid. This algorithm is simulated using the Matlab Simulink software. The voltage and power generated by the proposed rooftop PV are 560 V and 495.25 kW/sec. The voltage and electricity produced by the ground-mounted are very low compared to the proposed method, therefore, utilizing this rooftop solar PV model, that study generates more power than the ground-mounted PV. This study contributes to the field by improving the energy production of roof-top solar PV systems based on roof design.

Performance analysis and validation of intelligent tool based on Brownian random walk‐based sand cat swarm optimization algorithm for parameter identification of various solar photovoltaic mathematical models

AbstractThe photovoltaic (PV) system stands out as a viable energy source due to its environmental friendliness and cleanliness. The conversion rate at which solar power generation is still relatively low due to limitations imposed by advances in PV technology. For PV systems, an appropriate model with precise internal parameters is considerably more crucial to increase conversion efficiency further. Different PV mathematical models, such as single‐diode, two‐diode, and three‐diode, are available to model the PV system. Investigators are interested in assessing the accurate PV model parameters through the experimental voltage–current (I–V) samples or using the manufacturer's specifications. At the same time, the difficulty is in accurately assessing and developing a more trustworthy PV model with well‐optimized parameters. To address the parameter estimation of various solar PV models, in this article, a new bio‐inspired algorithm called Brownian random walk‐based Sand Cat Swarm Optimization Algorithm (SCSOA) named Boosted SCSOA (BSCSOA) is proposed and developed. Along with the Brownian random strategy, chaotic tent drift is also used to enhance the exploration and exploitation of SCSOA, and the proposed BSCSOA is applied to different models to estimate their parameters accurately. The effectiveness of the suggested BSCSOA is compared with other well‐known algorithms, including the basic SCSOA, in terms of statistical measures and fitness values. The obtained results demonstrated the superiority of the BSCSOA over the other algorithms for all PV models of the cell and module.

A reliable optimization framework for parameter identification of single‐diode solar photovoltaic model using weighted velocity‐guided grey wolf optimization algorithm and Lambert‐W function

AbstractIn estimating the parameters of the five unknown parameters Single‐Diode Model (SDM) of the solar photovoltaic (PV) model, a non‐linear equation for the PV cell current is typically utilized. Then, the error between the estimated current and measured current is computed using the objective function called Root‐Mean‐Square‐Error (RMSE). In order to compute the PV cell current in SDM, an iterative method built on the Lambert‐W function is presented in this study. Along with the Lamber‐W function, an optimization algorithm called Weighted Velocity‐Guided Grey Wolf Optimizer (WVGGWO) is used to identify the unknown lumped parameters of SDM of the cell and the module. The proposed WVGGWO is an updated version of the original Grey Wolf Optimizer (GWO). The position update of the GWO has been modified, and the weightage has been provided for the wolf hierarchy. Additionally, by emphasizing the lengthening of each leading wolf's steps towards the others in the earlier search while emphasizing the shortening of the steps while reaching the later iterations, WVGGWO improves both the exploration and exploitation of the original GWO. Four case studies are considered for testing the validity of the proposed algorithm along with the Lambert‐W function. The performance of the proposed approach is compared with seven other well‐known algorithms. The results demonstrate that the suggested approach produces better outcomes than many optimization algorithms.