Photovoltaic Cell Model Research Articles

Modelling and simulation in sim 3D software of a solar hybrid cell to using in PV power integration

A photovoltaic cell converts solar energy into electrical energy thanks to the photovoltaic effect. This technology is widely used in land and space applications. Many studies are being carried out to improve the models of these cells and for the proper functioning and to bring them to their maximum potency to be obtained. In this article, we present our photovoltaic cell model and for this we choose a GaAs-based pin cell, our studies carried out at the equilibrium of this cell. We used all our calculations on the SIM 3D simulator carried out in the “Component modelling and circuit design” team. This software is designed to study low geometry design components and to determine the volume of a structure, potential distributions and free carrier densities. The SIM 3D is under development by a group from the Mohamed Tahri University of Bechar. Algeria. We will present our photovoltaic cell model and our SIM 3D software development.

Parameter identification of PV solar cells and modules using bio dynamics grasshopper optimization algorithm

AbstractThe escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.

Circuit modeling and analysis of hysteresis effect of perovskite photovoltaic cells

Perovskite solar cells are an important development direction for future solar photovoltaic technology, with advantages such as low cost and high efficiency. However, they commonly suffer from hysteresis effects, which severely impacts the efficiency and lifespan of the cells. This paper analyzes the mechanism of hysteresis effects and the characteristics of the J-V curve of the cells. From a circuit perspective, this paper proposes a circuit modeling method for the J-V characteristics of hysteresis effects in perovskite photovoltaic cells. By utilizing the dynamic properties of nonlinear capacitors, the hysteresis model of perovskite photovoltaic cells is constructed, and the general expression of the model is derived. This model can simulate common hysteresis curves of different perovskite photovoltaic cells under various conditions. Simulation analysis of parameters' effects on hysteresis effects is conducted using the model. Experimental validation confirms that the circuit model accurately replicates the hysteresis effects observed in individual cells. By finely adjusting parameters, the model can efficiently generate a wide array of hysteresis effects, offering exceptional precision and versatility. This capability facilitates the precise simulation of hysteresis phenomena observed in perovskite photovoltaic cells.

Parameters Estimation of Photovoltaic Modules Using Differential Evolution Metaheuristic Method

This paper presents the analysis of the differential evolution (DE) metaheuristic method, which is used to estimate some parameters employed in the equivalent electrical circuit that represents the photovoltaic (PV) cell model. The DE algorithm is an evolutionary algorithm that can be designed to search global optimal points with fast convergence time and present accurate results, requiring few control variables to be set. The accuracy of reproducing the characteristics of the PV module depends on the knowledge of some PV cell model parameters, such as series and shunt resistances, diode ideality factor, and diode reverse saturation current. Once the referred parameters are not usually given in the manufacturers' datasheet, their determination can be achieved by the DE algorithm, employing data and the current-voltage (I-V) characteristic curve provided by the PV module manufacturers. The technique is designed to minimize the error between the reference I-V curve and the I-V curve reproduced by the DE algorithm. Through simulation results, the feasibility of the DE algorithm to estimate the desired parameters of a PV cell is validated, and the algorithm's performance to reach the best solution with fast convergence and a small iteration number is evaluated.

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

A maximum power point tracking (MPPT) control procedure constructed based on the Artificial intelligence optimization algorithm is proposed. A mathematical model of photovoltaic cells was established under varying light intensity and ambient temperature. Maximal power tracking control for iterative search using an artificial intelligence (AI) optimization technique Excellent, the artificial intelligence-based MPPT algorithm's principle is presented. Simulation results it shows that the artificial intelligence algorithm can faster and exactly track the maximum power point and remains stable, and compared to Perturb and Observe algorithm under dynamic shadow conditions and artificial intelligence MPPT control method, which has higher tracking accuracy and faster convergence speed. Faster and smaller oscillation amplitude, which is the best response to sunlight conditions for photovoltaic maximum power point tracking technology Flexibility. Using Matlab/Simulink software to build a simulation model of an independent photovoltaic system. It controls variables by keeping the temperature continuous and changes the light intensity to simulate different lighting environments. The identical comparison was conducted between all based MPPT methods such as the fuzzy control approach, demonstrating that the fuzzy control based technique exhibits higher results in terms of efficiency.

Stochastic approach for maximum power point determination for bifacial PV modules by implementing two different cell models

PurposeThis paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function.Design/methodology/approachIn this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation.FindingsFrom the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job.Practical implicationsThis approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location.Originality/valueAn optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement.

Optimal Evaluation of Photovoltaic Cells Parameters Using Euclidean Distance Calculations

The relationship between current and voltage describes the features and characteristics of the photovoltaic (PV) cells. This relationship mainly depends on the equivalent circuit parameters of the PV cell model. Accurate estimation of these parameters is crucial for analyzing the performance of PV systems. This paper proposes a simple and efficient method to estimate the equivalent circuit parameters of the PV cells/modules. The main concept of the proposed method is to optimize the PV series resistance value using Euclidean distance calculations in such a way to get the corresponding best maximum power conditions. Various assessments have been employed in this paper to confirm the validity of the presented approach. Those include analyzing different commercial PV modules, experimental data, irradiance and temperature variations, and comparison with other reported algorithms. When compared with experimental data at standard test conditions, the mean absolute current and power differences are 0.0329 A and 0.6339 W, respectively. Furthermore, the mean absolute differences at normal operating cell temperature are 0.0120 A and 0.1412 W. The results have shown that the proposed method has confirmed its effectiveness in predicting the PV cell equivalent circuit characteristics for any PV cells/modules using only data available from the manufacturer’s datasheet.

An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm

This article discusses the problem of accurate and efficient modeling of photovoltaic (PV) panels. It is a highly nonlinear problem. The following models were considered: a single diode model, a double diode model, a triple diode model, a four diode model, a module model (a poly-crystalline Photowatt-PWP201 module and a mono-crystalline STM6-40/36 module). The article presents a mathematical notation of these models, a detailed interpretation of their individual components, and a comparison of obtained results. To increase the effectiveness of modeling, a new population-based algorithm which can handle complex objective functions and a large number of decision variables was developed. This is important for the problem of identifying the parameters of PV cell models because each evaluation of the objective function requires calculating a set of points that determine the current–voltage characteristics. Moreover, in the considered problem a solution is searched with the use of the trial and error method. The proposed algorithm is called Micro Adaptive Fuzzy Cuckoo Search Optimization (μAFCSO). The μAFCSO algorithm uses several new mechanisms that were developed based on our experience with population-based algorithms. The use of these mechanisms has produced very good results in simulations. In the scope of simulation studies, the μAFCSO algorithm was used for parameter extraction in six PV cell models and was also applied to optimize fifteen typical test functions. The test functions were considered in order to demonstrate that our algorithm can be used to solve typical problems processed using population-based algorithms. The results obtained in this study were compared with the results obtained using well-established algorithms. The results obtained in this work are better or comparable to them.

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.

Parameter extraction of photovoltaic model based on butterfly optimization algorithm with chaos learning strategy

Accurate extraction of photovoltaic (PV) model parameter is significant for the evaluation of PV system. Most of existing methods for extracting the PV parameters suffer from low extraction accuracy and are prone to fall into local optima. To solve these shortcomings, a chaos learning butterfly optimization algorithm (CLBOA) is proposed. First, the introduced Cauchy mutation increases the perturbation to butterflies and helps the algorithm to jump out of local optima. Second, a chaos learning strategy is proposed to lead the butterflies within the population to learn from the optimal individual using the improved Tent chaos mapping, which strengthens the learning exchange of the population, enhances the ability of global exploration and local exploitation, and optimizes the convergence speed and accuracy. Third, the three worst individual positions were randomly initialized using a terminal elimination mechanism to increase population diversity. After evaluating CLBOA through the CEC2022 benchmark suite, it was used to extract parameters for a total of 12 PV cell and module models for 5 materials. Compared with 9 well-known algorithms, CLBOA performs excellent in terms of convergence performance, parameters extraction accuracy, running time and improvement index. Finally, CLBOA was applied on YL PV power station model of Guizhou Power Grid in China under different irradiations and temperatures. The I-V and P-V curves reconstructed by CLBOA are highly fit to the synthesized curves, and the individual absolute error (IAE) and relative error (RE) also confirm the satisfactory accuracy of parameters extraction. Comprehensive analysis shows that CLBOA can extract parameters better than the comparison algorithms, demonstrating its strong potential for PV parameters extraction.

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.

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.

Analytical modeling of novel equivalent circuits of double diode solar cell circuits using a special transcendental function approach.

Solar photovoltaic (PV) cell modeling is crucial to understanding and optimizing solar energy systems. While the single-diode model (PVSDM) is commonly used, the double-diode model (PVDDM) offers improved accuracy at a reasonable level of complexity. However, finding analytical closed-form solutions for the current-voltage (I-U) dependency in PVDDM circuits has remained a challenge. This work proposes two novel configurations of PVDDM equivalent circuits and derives their analytical closed-form solutions. The solutions are expressed in terms of the Lambert W function and solved using a special transcendental function approach called Special Trans Function Theory (STFT). The accuracy of the proposed equivalent circuits is demonstrated on two solar cells/modules, RTC-F and MSX-60, showing equal or better performance than the standard PVDDM equivalent circuit. Further testing on a commercial solar panel under different irradiance and temperature conditions confirms the applicability of the proposed models. To address the parameter estimation problem, a novel metaheuristic algorithm, the chaotic honey-badger algorithm, is developed and evaluated. The results obtained validate the accuracy and practicality of the proposed PVDDM equivalent circuit configurations.

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.

An evaluation of the use of air cooling to enhance photovoltaic performance

The rapid rise in global energy consumption and its consequences on climate change has made incorporating renewable energy sources like solar photovoltaics into the building envelope easier. However, in spite of extensive uses and significant technological advances, the lower solar panel efficiencies caused by high temperatures remain a significant barrier to the viability of deploying photovoltaic technology in regions with hot climates utilizing computational fluid dynamics (CFD). This research examines the cooling effectiveness of air-cooled photovoltaic (PV) under the climate of Nablus - Palestine. This study presents a numerical model designed to cool solar panels using various air-cooled channel configurations. Rectangular fins made of high thermal conductivity materials such as copper were used in this study. The parametric study was based on the changing baseplate thickness, fin spacing, height, and thickness through a stepwise optimization process to enhance the heat transfer mechanism. The results show that the optimum design of average volume temperatures for the PV cell models in air-cooled channel configurations with and without fins were 40.28 °C and 42.58 °C, respectively. The optimum design was obtained at 3, 110, 60, and 4 mm for baseplate thickness, fin spacing, height, and thickness, respectively. This optimum design was responsible for the average PV panel temperature drop by 1.6 %, 1.3 %, 5.9 %, and 6.2 % for baseplate thickness, fin spacing, height, and thickness, respectively. The optimum design of an air-cooled cooling channel for PV is an important insight provided by this work, and it may help in the future development of more effective and affordable cooling methods.

Parameter Extraction of Photovoltaic Models by Honey Badger algorithm and Wild Horse Optimizer

Analyzing the processes ranging from the determination of the installation configuration of the photovoltaic (PV) systems to the operation at the maximum power, from the technical and economic feasibility study to the positive contribution to the region where the production is planned are just possible with the accurate and efficient simulation models of the PV systems. PV parameter extraction, which is a topic frequently discussed recently, is crucial for the detailed modeling of PV cells and modules and simulating the behavior of these systems. For this reason, the current study examined PV parameter extraction and solved this optimization problem with the honey badger algorithm (HBA) and wild horse optimizer (WHO). PV cells and modules were modeled with the single diode model (SDM) and double diode model (DDM) and tested with actual measurement data. The root-mean-square error (RMSE) was chosen as the objective function, and the results were compared with the evaluation metrics for computational accuracy and time. Based on four PV model results, RMSE values were calculated between 9.9318E-04 to 1.7011E-03 for HBA and between 9.8602E-04 and 1.7298E-03 for WHO. As a result, even though both algorithms produce successful, stable, and fast results in PV parameter extraction, the WHO yielded better results.

A Comparative Study on Accurate Parameter Estimation of Solar Photovoltaic Models Using Metaheuristic Optimization Algorithms

Fossil fuel prices are increasing day by day due to growing demand and resource limitations. This situation shifts conventional electricity generation to renewable power generation. Among renewables, solar photovoltaic (PV) energy is very promising as the third fastest growing one. However, PV system modeling requires the estimation of unknown parameters of PV cells and modules. This problem remains a compelling task due to its multi-dimensional, multi-model and non-linear characteristics. In order to contribute to the solution of this optimization problem, this work makes a detailed comparison of artificial hummingbird algorithm, artificial rabbits optimization, enhanced Jaya algorithm, flow direction algorithm and artificial gorilla troops optimizer for determining the unknown parameters of PV models. Experimental results demonstrate that artificial hummingbird algorithm in single diode modeling of PV cell, flow direction algorithm in double diode modeling of PV cell and artificial rabbits optimization in single diode modeling of PV module are found to be capable of estimating accurate and efficient design coefficients for PV systems. In addition, the mentioned metaheuristic algorithms show reasonable convergence characteristics for the corresponding PV models.

Smart Drone with Renewable Smart System

In order to lessen its negative effects on the environment and to maintain its future operations in a clear, renewable, and sustainable manner, the aviation industry has begun developing designs that are dependent on alternative energy sources but also friendly to the environment and conventional energy. Solar energy has been suggested as a potential remedy. Aerial vehicles driven by solar energy are viewed as essential to limiting the consequences of global warming. In this study, a MATLAB/Simulink environment is used to simulate a mathematical model of a solar-powered BLDC motor of a UAV. under photovoltaic (PV) array systems, the phrase "maximum power point tracking" (MPPT) is crucial to ensuring that, under specific circumstances, the connected systems receive the greatest power output. This study simulates "fuzzy logic control," one of the preferred MPPT methods, using a solar-powered BLDC motor for an unmanned aerial vehicle (UAV) design. The PV cell, MPPT, buck-boost converter, and BLDC motor models in the cascade structure are simulated, tested, and the results are compared to the DC motor technical data. As a result, despite changes in irradiance, the results of mathematical model simulation overlap with motor technical reference values. A mathematical model of a solar-powered BLDC motor for a UAV is created and simulated using the MATLAB/Simulink environment, in contrast to prior solar-powered BLDC motor literature efforts. The fuzzy logic control MPPT technique is preferred for adjusting the maximum power output at the solar cell, and a buck-boost converter structure is connected between the MPPT and the BLDC motor mathematical model. It is recommended for usage in solar-powered UAV designs in the future.

A Modified Gradient Search Rule Based on the Quasi-Newton Method and a New Local Search Technique to Improve the Gradient-Based Algorithm: Solar Photovoltaic Parameter Extraction

Harnessing solar energy efficiently via photovoltaic (PV) technology is pivotal for future sustainable energy. Accurate modeling of PV cells entails an optimization problem due to the multimodal and nonlinear characteristics of the cells. This study introduces the Multi-strategy Gradient-Based Algorithm (MAGBO) for the precise parameter estimation of solar PV systems. MAGBO incorporates a modified gradient search rule (MGSR) inspired by the quasi-Newton approach, a novel refresh operator (NRO) for improved solution quality, and a crossover mechanism balancing exploration and exploitation. Validated through CEC2021 test functions, MAGBO excelled in global optimization. To further validate and underscore the reliability of MAGBO, we utilized data from the PVM 752 GaAs thin-film cell and the STP6-40/36 module. The simulation parameters were discerned using 44 I-V pairs from the PVM 752 cell and diverse data from the STP6-40/36 module tested under different conditions. Consistency between simulated and observed I-V and P-V curves for the STM6-40/36 and PVM 752 models validated MAGBO’s accuracy. In application, MAGBO attained an RMSE of 9.8 × 10−4 for double-diode and single-diode modules. For Photowatt-PWP, STM6-40/36, and PVM 752 models, RMSEs were 2.4 × 10−3, 1.7 × 10−3, and 1.7 × 10−3, respectively. Against prevalent methods, MAGBO exhibited unparalleled precision and reliability, advocating its superior utility for intricate PV data analysis.