Double Diode Research Articles

Extremal Nelder–Mead colony predation algorithm for parameter estimation of solar photovoltaic models

AbstractMeasurement data based on current and voltage of photovoltaic (PV) systems and the establishment of more accurate and stable solar system models are of typical significance for the design, control, evaluation and optimization of PV systems. Accurate and stable parameter evaluation for PV systems needs to be based on more efficient optimization techniques to achieve efficient energy conversion from solar energy. Therefore, this paper proposes a novel and efficient optimization technique enhanced colony predation algorithm to solve the complex PV parameter identification problem named ECPA. By fusing extremal optimization strategy and Nelder–Mead simplex method enables ECPA to further develop in the neighborhood of potential optimal solutions while improving the position of inferior agent candidates, and finally has the ability to search globally beyond the local optimum. To verify the optimization efficiency of ECPA, the first part verifies the efficiency of ECPA in solving complex high‐dimensional and multimodal problems by conducting competitive comparison experiments at the IEEE CEC 2020 benchmark case. In the second part, ECPA is compared with nine similar published state‐of‐the‐art algorithms, and competitive tests for PV parameter identification under single diode model, double diode model, triple diode model and PV module model (PV) are conducted. Finally, we focused on three different commercial PV models (thin film ST40, monocrystalline SM55, and multicrystalline KC200GT) to test the accuracy of ECPA in evaluating PV parameters. The test results show that ECPA is able to maintain a high level of accuracy and stability when dealing with commercial PV models in complex environments. The experimental results demonstrate that ECPA outperforms other algorithms in terms of data fitting, stability, convergence speed and convergence accuracy. All the competitive experimental results show that ECPA can be a novel technique with the best performance for identifying the parameters to be determined in solar PV systems.

Peafowl optimization algorithm based PV cell models parameter identification

Photovoltaic (PV) power generation can considerably reduce the consumption of traditional fossil energy and improve environmental problems. Reliable photovoltaic (PV) cell modelling owns great significance to the following output characteristics analysis and optimal operation of the whole PV system, while there are several unknown physical parameters within different PV cell models. Thus, the identification of the internal parameters of the PV cell model is the first and foremost step for PV cell modelling, nevertheless, the intrinsic highly complex and non-linear and multi-modal features make traditional approaches, such as analytical methods hard to achieve satisfactory performance in solving this problem. Hence, this work aims to employ a powerful tool to effectively and efficiently overcome this thorny problem based on the most advanced optimization method. A recently developed meta-heuristic algorithm called peafowl optimization algorithm (POA) is employed in this work for PV cell modelling parameter identification. For comprehensive validation, two different PV cell models, i.e., double diode model (DDM) and triple diode model (TDM) are utilized. Simulation results demonstrate that POA can more accurately identify the unknown parameters of PV cell models in a higher convergence speed compared against other algorithms.

Simpson’s rule based accurate mathematical modelling of photovoltaic cell using multi-objective genetic algorithm for hardware verification

The behaviour and effective performance of solar cell is represented by Triple Diode Solar Cell Module (TDSCM) circuit with five parameters for different environmental conditions. The equations described the solar modules behaviour are usually implicit in nature and the parameter extraction was very complexity. From the Photovoltaic (PV) module data sheet, one can identify the four equations applying to single, double, and triple diode parameters. For getting fifth equation researchers have gone with several approximations, which concludes the computation complexity, convergence problem, and low accuracy issues. In the proposed work the fifth equation are framed under the area characteristics curve (V-I & P-V) concept using Simpson’s approximation. To find which PV module is less accuracy and non-linearity consideration for the performance level. Therefore, to overcome these issues the multi-objective Genetic Algorithm (GA) optimization method are prescribed to frame the fifth equation of the Simpson’s rules. This works improved non-linearity performance and gives the high accuracy modelling compare to other single, double diode methods.

A twofold hunting trip African vultures algorithm for the optimal extraction of photovoltaic generator model parameters

ABSTRACT The development of reliable simulators that finely imitate the behavior of PV devices is vitally important for the design and optimization of efficient and stable photovoltaic systems. In this work, an improved variant of the African Vultures Optimization Algorithm named IAVOA is designed to serve as a powerful tool for extracting the unknown parameters of photovoltaic models. The introduced scheme incorporates a twofold strategy in such a way that allows a portion of the search agents to conduct a global search while the remaining portion performs a local search. The embedded mechanism is based on two equations added to the standard version, and by which the exploration and exploitation capabilities of the algorithm have significantly been fostered. To testify the performance of the IAVOA, a comparative study based on the Root Mean Square Error (RMSE), was conducted on six distinct benchmark PV models, and the obtained results were, in most cases, remarkably superior to the ones achieved by its competitors. The algorithm was able to produce values for the ideality factors that have not been previously found by any existing work to the best of our knowledge. In turn, the Double Diode and Triple Diode models’ accuracies were notably improved with RMSE scores of 6.9096 × 10 − 4 and 7.4011 × 10 − 4 respectively for the RTC France cell, and 1.4251 × 10 − 2 for the STP6-120/36 module, outperforming the existing techniques. In light of that, it can be reliably presumed that the IAVOA is indeed a promising algorithm for the electrical characterization of PV devices.

Improved honey badger algorithms for parameter extraction in photovoltaic models

Precise estimation of the parameter values of solar models is very essential for optimization of solar systems. Many studies that use metaheuristic algorithms have recently been proposed for parameter estimation and optimization in photovoltaic models. In this study, it is aimed to enhance convergence performance in photovoltaic systems by scanning the search space using two improved versions of the honey badger algorithm. First, the Gauss/Mouse map-based chaotic honey badger algorithm has been considered motivating by the fact that chaotic maps are successful in controlling critical random values in exploration and exploitation phases. The other algorithm is based on hybridization of opposition based learning with honey badger algorithm. Opposition based learning has efficient convergence capability as it scans the search space using opposites of candidate solutions. The performances of these improved methods and recent metaheuristic optimization algorithms are firstly evaluated for CEC2017 and CEC2019 datasets. After obtaining the successful results for these datasets, proposed algorithms are compared for single-diode, double diode and photovoltaic module models which are given as poly-crystalline Photowatt-PWP201, mono-crystalline STM6-40/36, and poly-crystalline STP6-120/36. For each model, optimum parameter values are found minimizing the root-mean-square-error. In addition, three commercial PV panels; Mono-crystaline SM55, Thin-film ST40, and Multi-crystalline KC200GT are also considered for performance evaluation. According to simulation results, proposed algorithm exhibits high performance in terms of minimization of root mean square error. The compliance of estimated and actual values of the parameters are visualized with current–voltage (I–V) and power–voltage (P–V) characteristics. The results show that the proposed methods are effective alternatives for solution of photovoltaic parameter estimation problem and contribute to the parameter optimization of photovoltaic models.

Investigation of a social group assisted differential evolution for the optimal PV parameter extraction of standard and modified diode models

Parameter estimation of PV cells is essential to develop accurate simulation techniques, optimal control mechanisms, converter design optimization, maximize the efficiency of PV harvesting systems, improve the accuracy of monitoring systems etc. Advanced meta-heuristic algorithms for the optimal parameter estimation through the minimization of the disparity between the measured and estimated I-V data pairs have emerged as a powerful tool in this regard. Despite the efficacy of the meta-heuristics for the standard diode models, the optimization of modified diode models including the higher-order complex modified double and triple diode modules remains largely unexplored. Hence, an advanced hybrid meta-heuristic technique benefitting from the synergy of social group optimization and differential evolution known as social group assisted differential evolution (SGDE) is developed to achieve an enhanced balance of exploration and exploitation for the complex multi-modal landscapes of the modified diode models. Three experiments involving the minimization of the root mean square error for three commercial PV modules (KC200GT, Shell SQ85 and Shell ST40) based on the simulated I-V data followed by four experimental I-V curves of two solar cells (RTC France solar cell and PVM 752 GaAs thin-film cell) and two PV modules (Photowatt-PWP201 and STM6-40/36 PV modules) and the parameter optimization for two commercial modules (JAM72S01-380/PR and SUNCECO SEP300W PV modules) at varying levels of irradiance and temperature are investigated and extensively compared with fourteen other state-of-the-art meta-heuristic techniques. SGDE outperforms the competitor algorithms in terms of optimality, consistency and demonstrated greater robustness to local entrapment for the standard and modified diode models.

EXTRACTION OF DOUBLE-DIODE PHOTOVOLTAIC MODULE MODEL’S PARAMETERS USING HYBRID OPTIMIZATION ALGORITHM

This paper presents seven parameters of double diode model of the photovoltaic module under different weather conditions are extracted using differential development with an integrated mutation per iteration (DEIM) algorithm. The algorithm is produced by integrating of two other algorithms namely, electromagnetism like (EML) and differential evolution (DE) algorithms. DEIM enhances the mutation step of the original DE by using the attraction-repulsion principle found in the EML algorithm. Meanwhile, a novel strategy based on adjusting mutation and crossover rate factors for each iteration is adopted in this paper. The implemented scheme's success is confirmed by comparing its results with those obtained by techniques cited in the literature. Along with the results, the DEIM suggests high closeness with the experimental I–V characteristic. For the proposed algorithm an average Root Mean Square Error (RMSE), absolute error (AE), mean bias Error (MBE), and execution time values were 0.0608, 0.044, 0.0053 and 23.333, respectively. The comparisons and evaluations results proved that the DEIM is better in terms of precision and rapid convergence. Furthermore, fewer control parameters are needed in comparison to EML and DE algorithms.

Recent Development on Photovoltaic Parameters Estimation: Total Least Squares Approach and Metaheuristic Algorithms

ABSTRACT Photovoltaic parameter extraction has been the focus of many studies, each one presenting a new optimization method to obtain them. To further enhance the optimization process, this work presents a new cost function based on Total Least Squares and compares its performance with the usual function which is based on the Ordinary Least Squares approach. In this paper, eleven different metaheuristic methods are used to compare the performance of the two functions for both single and double diode photovoltaic cell models. The results are presented in terms of mean values of the estimated parameters. Additionally, the two cost function values are also evaluated according to their convergence properties for the different optimization methods considered. The results showed that the Total Least Squares method performs better parameter estimation than the Ordinary Least Squares approach, when used with all the eleven methods, where the best values are obtained when it is coupled with Teaching Learning Based Optimization algorithm for double diode model, resulting in a mean error value of 5.0375e-04 compared to 7.6423e-04 when using Ordinary Least Squares. The big difference between the convergence of the two cost functions was with the Dragonfly method. With the single diode model, this method results in a 0.0016 difference in the mean value of RMSE, and with the double diode model, this difference decreased to 0.0014.

Photovoltaic parameter estimation using honey badger algorithm and African vulture optimization algorithm

The accurate arithmetical models of solar photovoltaic (PV) modules plays a vital role in the performance, operation, and control of the PV systems as a part of renewable generation. Therefore, the proposed paper develops a novel robust Honey Badger Algorithm (HBA) and African Vulture Optimization algorithm (AVOA) that have been utilized for deducing the unknown parameters of various solar PV units accurately and efficiently. The HBA and AVOA techniques are developed to identify the approximate undefined parameters of different PV system models, namely, single diode model (SDM), double diode model (DDM), and three diode model (TDM). The attained results from the application of HBA and AVOA are to optimize the objective function of the current errors between the measure and the evaluation of the PV modules. Moreover, statistical tests have been presented to demonstrate the effectiveness of HBA and AVOA. The numerical results of the developed HBA and AVOA prove them as competitor methods for the parameters’ extraction of solar PV systems.

Design and development of extract maximum power from single-double diode PV model for different environmental condition using BAT optimization algorithm

To minimize real-time errors in a Photovoltaic (PV) system performances must be forecasted through precise simulation design before continuing into a practical application. However, due to the scarcity of data in datasheets and the inherent transcendental connections are between PV current and PV voltage, to determining the Single Diode Model (SDM) parameters becomes a more challenging problems. This paper offers a simulated study of a SDM and Double Diode Model (DDM) solar PV system under various irradiation represents, and the performance was developed by incorporating an optimization-based Maximum Power Point (MPP) tracking techniques. According to the present simulation presented in this article, a mathematical model for a SDM/DDM as well as optimization methodologies has been estimated MATLAB platform. The present MPP circuit model designed and compared with BAT optimization algorithms. The nonlinear relationship between Voltage (V) - Current (I) and Voltage (V) –Power (W) acknowledged as characteristic curves for different temperature (∘c) and irradiance (W/m2) values are verified in numerical simulation results. MPP tracking power and efficiency are examined for maximum power (Pmax) to test the optimization based system. The simulation results show that the BAT optimization model was achieved the highest tracking efficiency better than other heuristic algorithms.

Single Diode Solar Cells-Improved Model and Exact Current-Voltage Analytical Solution Based on Lambert's W Function.

There are three standard equivalent circuit models of solar cells in the literature—single-diode, double-diode, and triple-diode models. In this paper, first, a modified version of the single diode model, called the Improved Single Diode Model (ISDM), is presented. This modification is realized by adding resistance in series with the diode to enable better power loss dissipation representation. Second, the mathematical expression for the current–voltage relation of this circuit is derived in terms of Lambert’s W function and solved by using the special trans function theory. Third, a novel hybrid algorithm for solar cell parameters estimation is proposed. The proposed algorithm, called SA-MRFO, is used for the parameter estimation of the standard single diode and improved single diode models. The proposed model’s accuracy and the proposed algorithm’s efficiency are tested on a standard RTC France solar cell and SOLAREX module MSX 60. Furthermore, the experimental verification of the proposed circuit and the proposed solar cell parameter estimation algorithm on a solar laboratory module is also realized. Based on all the results obtained, it is shown that the proposed circuit significantly improves current–voltage solar cell representation in comparison with the standard single diode model and many results in the literature on the double diode and triple diode models. Additionally, it is shown that the proposed algorithm is effective and outperforms many literature algorithms in terms of accuracy and convergence speed.

Parameter estimation of four diode solar photovoltaic cell using hybrid algorithm

ABSTRACT The effective and precise parameter estimation of the solar photovoltaic (PV) cell is extremely crucial for precise evaluation and control of PV systems. In the recent years, many meta-heuristic and conventional algorithms have been proposed for the parameter estimation of PV cells. However, it is still a huge challenge for researchers. In this study, a new hybrid algorithm Hybrid Particle Swarm Optimization and Dingo Optimizer (HPSODOX) based on two widely used meta-heuristic algorithms i.e., Particle Swarm Optimization (PSO) and Dingo Optimizer (DOX) is developed for parameter estimation of four diode model of solar PV cell under different operating conditions. Two main components of any hybrid algorithm are exploration and exploitation, and HPSODOX has the ability to make fine balance between these components. The hybrid algorithm is benchmarked on ten functions, out of which seven are unimodal, and three are multimodal functions. Further, results achieved with developed hybrid algorithm for parameter estimation of four-diode solar PV cell model are compared with those of three meta-heuristic algorithms i.e., PSO, GWO, DOX and two hybrid algorithms i.e., GWOCS, PSOGWO. The result of four diode model is compared with single, double and triple diode model. For the four diode model the computational time of the proposed hybrid algorithm (HPSODOX 2.09) is better than the rest of the compared algorithms (PSO 4.75, GWO 4.73, DOX 4.43, GWOCS 2.25, and PSOGWO 2.23). The comparative analysis show that HPSODOX performed significantly better than other algorithms with minimum error and best optimal solution. After the extraction of parameters, the non-parametric test is performed i.e., Friedman ranking test. The results so obtained clearly indicate that the proposed hybrid algorithm is better than the rest of the compared algorithms.

Combination of quadratic ranking selection real-coded genetic algorithm with the Hooke-Jeeves optimization method for solar photovoltaic parameter estimation

Estimating accurate parameters for Photovoltaic cell (PVC) models plays a very important role in evaluation, simulation, control and maximum power point tracking (MPPT) of solar PV systems. Indeed, parameter extraction for solar cells and modules from experimental data is a challenging problem. These parameters must be evaluated from a set of nonlinear equations that cannot be solved analytically. Therefore, an optimization approach is essential to estimate these parameters by minimizing the difference between the calculated current from the PV model and the measured current. In this paper, a real-coded genetic algorithm (GA) based on quadratic ranking selection combined with the Hooke-Jeeves (HJ) method is proposed for this parameter extraction. It is well known that GA has proven good capacities in global search while on the other hand, the HJ has high-performance capacities in local search. As a hybrid technique, the HGAHJ has the feature of combining the capabilities of global and local searching. The proposed algorithm is developed and then applied for the parameter estimation for the single and double diode solar cell models. In addition, a comparative study with the GA and particle swarm optimization (PSO) algorithms is presented to demonstrate the effectiveness of the proposed approach using measured data of two commercial solar panels, which are Kyocera KC200GT and RTC France. Results reveal the superior capabilities of the suggested HGAHJ algorithm as a global optimization technique.

Bypass diode and photovoltaic module failure analysis of 1.5kW solar PV array

ABSTRACT Solar photovoltaic (PV) is the widely preferred power generation source to meet the growing energy demands. Solar PV system must be made highly reliable and effectively utilized during the availability of the source. System reliability is the predominant factor to be considered because of the possibilities of various faults and various operating conditions to which PV system is subjected. The present work addresses three major faults that commonly occur in solar PV system, namely, failure of bypass diode, failure of PV module, and power generation mismatch due to panel replacement. This proposed work deals with the detection and clearance of these three faults. The failure of PV module results in a higher power reduction of 82% in the PV array. The switching matrix circuit is proposed to enhance the power obtained from PV system under the module failure fault. The faults are manually created in a typical 1.5 kW grid-connected PV system and rectified to enhance the power production in the PV array. An average power enhancement of 14% is obtained after the clearance of the power mismatch due to panel replacement. With the help of double bypass diode technique, output power is enhanced to 55.2% higher compared to before fault clearance at the same irradiance level. The experimental results obtained in 1.5 kW grid-connected solar PV system are studied and validated.

Solar photovoltaic model parameter identification using robust niching chimp optimization

Researchers are becoming increasingly interested in studying how to accurately estimate the parameters of solar PV models. In this regard, this paper proposes a newly proposed nature-inspired technique named chimp optimization algorithm (ChOA) to create accurate and dependable PV models, such as single diode, double diodes, three diodes, and PV module models. In the PV models’ parameters estimation using optimization algorithms, two significant concerns need to be addressed: classifying various local/global optima and preserving these optimum values until the termination. Since ChOA is a general optimizer, it lacks an operator to address the two issues mentioned above. In order to address the mentioned problems, this paper embeds the niching technique in ChOA that includes the personal best qualities of PSO and a local search technique. In addition, a novel constraint handling approach is utilized to ensure that the algorithm is robust in tackling PV Models’ parameters estimation constraints. The outcome of RN-ChOA is evaluated using seven well-known optimization algorithms, including the whippy Harris hawks optimization algorithm (WHHOA), performance-guided JAYA (PGJAYA), enriched Harris hawks optimization algorithm (EHHOA), improved JAYA (IJAYA), birds mating optimizer (BMO), flexible particle swarm optimization algorithm (FPSO), chaotic biogeography-based optimizer (CBBO), and generalized oppositional teaching-learning algorithm (GOTLA), as well as dynamic Levy flight ChOA (DLF-ChOA) and weighted ChOA (WChOA) as the most recent modified version of ChOA. Furthermore, the performance of the RN-ChOA method has been assessed in a practical application for parameter evaluation of three widely-used commercial modules, namely, multi-crystalline (KC200GT), polycrystalline (SW255), and mono-crystalline (SM55), under a variety of temperature and irradiance conditions that cause changes in the photovoltaic model’s parameters. The findings demonstrate the robustness and excellent performance of the suggested approach.

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.

Improved gradient‐based optimizer for parameters extraction of photovoltaic models

An efficient operation of the solar photovoltaic (PV) system relies on accurate and reliable equivalent models and parameters. For different modular circuit models, the parameter estimation of PV solar cells is a key task that is usually translated into optimization problems solved by metaheuristic algorithms. Gradient-based optimizer (GBO), which was proposed in 2020, is a method with swarm characteristics that was developed from Newton's method. Gradient search rule (GSR) and local escape operator (LEO) is main components of the GBO algorithm. This paper proposes an improved GBO (IGBO) optimization algorithm based on the original algorithm. In IGBO, two strategies, adaptive weights and chaotic behaviour, are introduced to adjust the adaptive parameters. The purpose of adaptive weights is to effectively approach the optimal solution and avoid falling into local optimum in different search phases of the algorithm. Chaotic behaviour aims to replace the randomness of the metaheuristic algorithm and to improve the convergence speed and accuracy of the algorithm. To validate the performance of IGBO, it is applied to the parameter extraction of different PV models. The PV models used in this paper include single diode model (SDM), double diode model (DDM) and PV module model. Finally, comparing with the original algorithm and six well-established algorithms, the experimental data show that the IGBO algorithm is highly competitive, as reflected by the best results obtained in terms of root mean square error values. It can be concluded that IGBO is more competitive in robustness, convergence and accuracy.

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.

An improved Rao-1 algorithm for parameter estimation of photovoltaic models

The efficient operation of the solar photovoltaic (PV) system is influenced by the accurate determination of its model parameters. The appropriate selection of these parameters is a challenging task due to the multi-modality and non-linearity of the problem. Given less sensitivity to initial solutions and the powerful search capability, many metaheuristic algorithms have been successfully applied to estimate the unknown parameters of PV cell/module models. However, most used techniques are time-consuming, control parameters tuning, etc. This paper proposes an improved Rao-1 (IRao-1) algorithm for efficiently and accurately extracting the PV system parameters. IRao-1 is a new variant of the Rao-1 algorithm, which improves the global search ability of the Rao-1 algorithm by implementing three new search equations. The modification introduced in Rao-1 does not affect its structural simplicity. The effectiveness of the proposed algorithm is evaluated using several PV models, including PV cells (single diode and double diode) and three PV modules. Experimental results demonstrate the superiority of IRao-1 in study cases in terms of efficiency and accuracy by comparing it with recent works.

Static models for implementing photovoltaic panels characteristics under various environmental conditions using improved gradient-based optimizer

An accurate definition of the photovoltaic (PV) models is an essential task to emulate and understand the physical behavior of the PV cell/panels. The highly used PV models are the static equivalent circuits, including single and double diode models. However, the accurate definition of the static models is mainly based on their estimated parameters. Proposing a reliable Optimization-based approached is a challenging aim. So, this paper proposes a novel and efficient optimizer to identify PV single and double diode models’ parameters for several PV modules using different sets of experimentally measured data. The developed method depends on improving the gradient-based optimization algorithm (GBO) using a new crossover operator to enhances agents’ diversity. Furthermore, a modified local escaping operator is applied to improve exploitation of GBO. The performance of the improvement GBO (IGBO) is evaluated using different experimental datasets for numerous PV modules under several operating conditions of temperature and radiation. The efficiency of IGBO is validated through a massive comparison with a set of recent state-of-the-art techniques. Reported results, fitting curves, and convergence curves provide proof for the efficiency of IGBO in providing high qualifies results with remarkable convergence speed.