Search Results (109)

Meta-heuristic algorithms are the dominant techniques for parameter estimating for solar photovoltaic (PV) models. Current algorithms are primarily designed with a focus on search performance and convergence speed, but they fail to account for the significant difference in the lengths of the feasible regions for each decision variable in the solar parameter estimation problem. The consideration of variable length difference in algorithm design may be beneficial to the efficiency for solving this problem. A gray predictive evolutionary algorithm with adaptive threshold adjustment strategy (GPEat) is proposed in this paper to estimate the parameters of several solar photovoltaic models. Unlike original GPEs and their existing variants with fixed thresholds, GPEat designs an adaptive threshold adjustment strategy (ATS), which adaptively adjusts the threshold parameter of GPE to be proportional to the length of each dimensional variable of the PV problem. The adaptive change of the threshold helps GPEat to select suitable operators for different dimensions of the PV problem. Several sets of experiments are conducted based on single-, double-, and triple-diode models and PV panel models. The experimental results indicate the highly competitive in parameter estimation for solar PV models of the proposed algorithm. Full article

Photovoltaic (PV) systems are the core technology for implementing net-zero carbon emissions by 2050. The performance of PV systems is strongly influenced by environmental factors, including irradiance, temperature, and shading, which makes it difficult to characterize the nonlinear and multi-coupling behavior of the systems. Accurate modeling is essential for reliable performance prediction and lifespan estimation. To address this challenge, a novel metaheuristic algorithm called shuffled puma optimizer (SPO) is deployed to perform parameter extraction and optimal configuration identification across four PV models. The robustness and stability of SPO are comprehensively evaluated through comparisons with advanced algorithms based on best fitness, mean fitness, and standard deviation. The root mean square error (RMSE) obtained by SPO for parameter extraction are 8.8180 × 10⁻⁴, 8.5513 × 10⁻⁴, 8.4900 × 10⁻⁴, and 2.3941 × 10⁻³ for the single diode model (SDM), double diode model (DDM), triple diode model (TDM), and photovoltaic module model (PMM), respectively. A one-factor-at-a-time (OFAT) sensitivity analysis is employed to assess the relative importance of undetermined parameters within each PV model. The SPO-based modeling framework enables high-accuracy PV performance prediction, and its application to sensitivity analysis can accurately identify key factors that lead to reduced computational cost and improved adaptability for integration with energy management systems and intelligent electric grids. Full article

Accurate photovoltaic (PV) panel characterization is critical for optimizing renewable energy systems, but it is often hindered by the high cost of commercial tracers or the slow, error-prone nature of manual methods. This paper presents a low-cost, Arduino-based I–V curve tracer that overcomes these limitations through fully automated resistive load switching. By integrating a relay-controlled resistor bank managed by a single microcontroller, the system eliminates the need for manual intervention, enabling rapid and repeatable measurements in just 45 s. This rapid acquisition is a key advantage over manual systems, as it minimizes the impact of fluctuating environmental conditions and ensures the resulting I–V curve represents a stable operating point. Compared to commercial alternatives, our open-source solution offers significant benefits in cost, portability, and flexibility, making it ideal for field deployment. The system’s use of fixed, stable resistive loads for each measurement point also ensures high repeatability and straightforward comparison with theoretical models. Experimental validation demonstrated high agreement with a single-diode PV model, achieving a mean absolute percentage error (MAPE) of 4.40% against the manufacturer’s data. Furthermore, re-optimizing the model with field-acquired data reduces the MAPE from 18.23% to 7.06% under variable irradiance. This work provides an accessible, robust, and efficient tool for PV characterization, democratizing access for research, education, and field diagnostics. Full article

In the world of solar technology, precisely extracting photovoltaic cell and panel parameters is key to efficient energy production. This paper presents a new metaheuristic algorithm for extracting parameters from photovoltaic cells using the functionality of the PID-based search algorithm (PSA). The research includes single-diode (SDM) and double-diode (DDM) models applied to RTC France, amorphous silicon (aSi), monocrystalline silicon (mSi), PVM 752 GaAs, and STM6-40 panels. Datasets from multijunction solar cells at three temperatures (41.5 °C, 51.3 °C, and 61.6 °C) were used. PSA performance was assessed using root mean square error (RMSE), mean bias error (MBE), and absolute error (AE). A strategy was introduced by refining PID parameters and relocating error calculations outside the main loop to enhance exploration and exploitation. A Lévy flight-based zero-output mechanism was integrated, enabling shorter extraction times and requiring a smaller population, while enhancing search diversity and mitigating local optima entrapment. PSA was compared against 26 top-performing algorithms. RTC France showed RMSE improvements of 0.67–2.10% in 3.35 s, while for the mSi model, PSA achieved up to 40.9% improvement in 5.57 s and 22.18% for PVM 752 in 8.52 s. PSA’s accuracy and efficiency make it a valuable tool for advancing renewable energy technologies. Full article

The accurate modeling of photovoltaic (PV) systems is crucial in optimizing energy efficiency and operational reliability. To address challenges in parameter estimation under dynamic conditions, a hybrid deep learning (DL)-based optimization scheme is proposed. It is hypothesized that combining the global search capabilities of the Whale Optimization Algorithm (WOA) with local refinement of Fishier Mantis Optimization (FMO), supported by long short-term memory (LSTM)-based predictions, enhances accuracy and robustness. The method was validated through simulations on single-, double-, and triple-diode models (SDM, DDM, and TDM) using MATLAB 2021a version. The hybrid model achieved the lowest root mean square error (RMSE) of 6.96 × 10⁻⁴ across all models, outperforming standard metaheuristics and showing strong stability over multiple runs. These findings confirm the method’s superior accuracy and efficiency for PV parameter extraction. Full article

Photovoltaic systems are affected by light intensity, temperature, and radiation angle, which influence their efficiency. Accurate estimation of PV module parameters is essential for improving performance. This paper presents an improved optimization technique based on the White Shark Optimizer (WSO) algorithm to optimize key characteristics of the PV module, including current, voltage, series resistance, shunt resistance, and ideality factor. The proposed method incorporates opposition-based learning (OBL) and chaos theory to improve search efficiency. A critical aspect of PV module modeling is inherent symmetry in electrical and thermal characteristics, where balanced parameter estimation ensures uniform energy conversion efficiency. With the application of symmetrical search techniques during the process of optimization, the proposed method enhances convergence robustness and stability, ensuring consistent and precise results across different PV models. Experimental evaluations conducted on three PV models—Single Diode Model (SDM), Double Diode Model (DDM), and general photovoltaic modules—demonstrate that the proposed method outperforms existing metaheuristic techniques such as Jumping Spider Optimization (JSO), Harris Hawks Optimization (HHO), WOA, Gray Wolf Optimizer (GWO), and basic WSO. Key results show improvements in the Friedman rating by 8.1%, 10.79%, and 9.6% for the SDM, DDM, and PV modules, respectively. Additionally, the proposed method achieves superior parameter estimation accuracy, as evidenced by reduced RMSE values compared to the competing algorithms. This work highlights the importance of advanced optimization techniques in maximizing PV output power while maintaining symmetry in parameter estimation. By ensuring a balanced and systematic optimization approach, this study assists in the development of robust and efficient solutions for PV system modeling. Full article

Renewable energy sources are increasingly crucial for sustainable development. Photovoltaic (PV) systems, which convert solar energy into electricity, offer an environmentally friendly solution. Enhancing energy efficiency and minimizing environmental impacts in these systems heavily rely on parameter optimization. In this study, the Frilled Lizard Optimization (FLO) algorithm is proposed as a novel approach, integrating the newton-raphson method into the root mean square error (RMSE) objective function process to address nonlinear equations. Extensive analyses conducted on RTC France, STM6-40/36, and Photowatt PWP201 modules demonstrate the superior performance of the FLO algorithm using MATLAB R2022a software with Intel(R) Core(TM) i7-7500U CPU@ 2.70GHz 2.90 GHz 8 GB RAM. The RMSE values were calculated as 0.0030375 and 0.011538 for SDM and DDM in the RTC France dataset, 0.012036 for the STM6-40/36 dataset and 0.0097545 for the Photowatt-PWP201 dataset, respectively, indicating significantly lower error margins compared to other optimisation methods. Additionally, comprehensive evaluations were carried out using error metrics such as individual absolute error (IAE), relative error (RE) and mean absolute error (MAE), supported by detailed graphical representations of measured and predicted parameters. Current-voltage (I-V) and power-voltage (P-V) characteristic curves, as well as convergence behaviors, were systematically analyzed. This study introduces an innovative and robust solution for parameter optimization in PV systems, contributing to both theoretical and industrial applications. Full article

The utilization of solar photovoltaics (PV) in electricity generation is progressively increasing due to its environmental benefits, such as reducing power transmission costs and mitigating global warming. This research aims to enhance the effectiveness of the extracted PV parameters. To estimate the parameters of the PV model, a recent optimization algorithm called the Chaos Game Optimization algorithm (CGO) is employed to precisely choose PV parameters. In this work, PV cells are modeled using two different models, including the single-diode model (SDM) and the double-diode model (DDM). The CGO algorithm outperformed nine well-known optimization algorithms based on the root–mean squares of error (RMSE) with a percentage of up to 97% for the single-diode model (SDM) and up to 92.92% for the double-diode model (DDM). Full article

Accurately modeling photovoltaic (PV) cells is crucial for optimizing PV systems. Researchers have proposed numerous mathematical models of PV cells to facilitate the design and simulation of PV systems. Usually, a PV cell is modeled by equivalent electrical circuit models with specific parameters, which are often unknown; this leads to formulating an optimization problem that is addressed through metaheuristic algorithms to identify the PV cell/module parameters accurately. This paper introduces the flood algorithm (FLA), a novel and efficient optimization approach, to extract parameters for various PV models, including single-diode, double-diode, and three-diode models and PV module configurations. The FLA’s performance is systematically evaluated against nine recently developed optimization algorithms through comprehensive comparative and statistical analyses. The results highlight the FLA’s superior convergence speed, global search capability, and robustness. This study explores two distinct objective functions to enhance accuracy: one based on experimental current–voltage data and another integrating the Newton–Raphson method. Applying metaheuristic algorithms with the Newton–Raphson-based objective function reduced the root-mean-square error (RMSE) more effectively than traditional methods. These findings establish the FLA as a computationally efficient and reliable approach to PV parameter extraction, with promising implications for advancing PV system design and simulation. Full article

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as its accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits is challenging due to limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) for determining the ideal parameters for the circuit model. The presented EBOA incorporates several modifications aimed at improving its searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support the BOA settings to be adjusted in an adaptive manner. Additionally, it integrates key mechanisms from the Hippopotamus Optimization (HO) to strengthen the algorithm’s exploitation potential by leveraging surrounding knowledge for more effective position updates while also improving the balance between global and local search processes. The EBOA was subjected to extensive mathematical validation through the application of benchmark functions to rigorously assess its performance. Also, PV parameter estimation was achieved by combining the EBOA with a Newton–Raphson approach. Numerous module and cell varieties, including RTC France, STP6-120/36, and Photowatt-PWP201, were assessed using double-diode and single-diode PV models. The higher performance of the EBOA was shown by a statistical comparison with many well-known metaheuristic techniques. To illustrate this, the root mean-squared error values achieved by our scheme using (SDM, DDM) for RTC France, STP6-120/36, and PWP201 are as follows: (8.183847 × 10⁻⁴, 7.478488 × 10⁻⁴), (1.430320 × 10⁻², 1.427010 × 10⁻²), and (2.220075 × 10⁻³, 2.061273 × 10⁻³), respectively. The experimental results show that the EBOA works better than alternative techniques in terms of accuracy, consistency, and convergence. Full article

The new PV technologies, such as bifacial modules, bring the challenge of analyzing the response of numerical models and their fit to actual measurements. Thus, this study explores various models available in the literature for simulating the IV curve behavior of bifacial photovoltaic modules. The analysis contains traditional models, such as single and double-diode models, and empirical or analytical methodologies. Therefore, this paper proposes and implements a model performance assessment framework. This framework aims to establish a common basis for comparison and verify the applicability of each model by contrasting it with experimental data under controlled conditions of irradiance and temperature. The study utilizes bifacial modules of PERC+, HJT, and n-PERT technologies, tracing IV curves using a high-precision A+A+A+ solar simulator and conducting two sets of laboratory illumination measurements: single-sided and double-sided. In the first case, each face of the module is illuminated separately, while in the latter, the incident frontal illuminating light is reflected on a reflective surface. Experimental data obtained from these measurements are used to evaluate three different approximations for bifacial IV curve models in the case of double-sided illumination. The employed model for single-sided illumination is a single-diode model. The evaluation of various models revealed that shadowing from frames and junction boxes contributes to an increase in the error of modeled IV curves. However, among the three evaluated bifacial electrical models, one exhibited superior performance, with current errors approaching approximately $20\%$. To mitigate this discrepancy, a proposed methodology highlighted the significance of accurately estimating $I_{\mathrm{o}}$, suggesting its potential to reduce errors. This research provides a foundation for comparing electrical models to identify their strengths and limitations, paving the way for the development of more accurate modeling approaches tailored to bifacial modules. The insights gained from this study are crucial for enhancing the precision of IV curve predictions under various illumination conditions, which is essential for optimizing bifacial module performance in real-world applications. Full article

Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots. Full article

A metaheuristic algorithm named the Crystal Structure Algorithm (CrSA), which is inspired by the symmetric arrangement of atoms, molecules, or ions in crystalline minerals, has been used for the accurate modeling of Mono Passivated Emitter and Rear Cell (PERC) WSMD-545 and CS7L-590 MS solar photovoltaic (PV) modules. The suggested algorithm is a concise and parameter-free approach that does not need the identification of any intrinsic parameter during the optimization stage. It is based on crystal structure generation by combining the basis and lattice point. The proposed algorithm is adopted to minimize the sum of the squares of the errors at the maximum power point, as well as the short circuit and open circuit points. Several runs are carried out to examine the V-I characteristics of the PV panels under consideration and the nature of the derived parameters. The parameters generated by the proposed technique offer the lowest error over several executions, indicating that it should be implemented in the present scenario. To validate the performance of the proposed approach, convergence curves of Mono PERC WSMD-545 and CS7L-590 MS PV modules obtained using the CrSA are compared with the convergence curves obtained using the recent optimization algorithms (OAs) in the literature. It has been observed that the proposed approach exhibited the fastest rate of convergence on each of the PV panels. Full article

Accurate estimation of photovoltaic (PV) power generation can ensure the stability of regional voltage control, provide a smooth PV output voltage and reduce the impact on power systems with many PV units. The internal parameters of solar cells that affect their PV power output may change over a period of operation and must be re-estimated to produce a power output close to the actual value. To accurately estimate the power output for PV modules, a three-diode model is used to simulate the PV power generation. The three-diode model is more accurate but more complex than single-diode and two-diode models. Different from the traditional methods, the 9 parameters of the three-diode model are transformed into 16 parameters to further provide more refined estimates. To accurately estimate the 16 parameters in the model, an optimization tool that combines enhanced swarm intelligence (ESI) algorithms and the dynamic crowing distance (DCD) index is used based on actual historical PV power data and the associated weather information. When the 16 parameters for a three-diode model are accurately estimated, the I–V (current-voltage) curves for different solar irradiances are plotted, and the possible failures of PV modules can be predicted at an early stage. The proposed method is verified using a 200 kWp PV power generation system. Three different diode models that are optimized using different ESI algorithms are compared for different weather conditions. The results affirm the reliability of the proposed ESI algorithms and the value of creating more refined estimation models with more parameters. Preliminary fault diagnosis results based on the differences between the actual and estimated I–V curves are provided to operators for early maintenance reference. Full article

In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W method and Newton–Raphson method), respectively. It was found that the parameter-extraction accuracies of the Lambert W (LW) method and the Newton–Raphson (NR) method are always approximately equal and higher than that of the approximation method. The best RMSEs (root mean square error) obtained by using the LW or the NR method on the solar cell and the PV module are 7.72986 × 10⁻⁴ and 2.05296 × 10⁻³ for SDM parameter extraction and 6.93709 × 10⁻⁴ and 1.99051 × 10⁻³ for DDM parameter extraction, respectively. The latter may be the highest parameter-extraction accuracy reported on the solar cell and the PV module so far, which is due to the adoption of more reasonable DDM parameter boundaries. Furthermore, the convergence curves of the LW and the NR method basically coincide, with a convergence speed faster than that of the approximation method. The robustness of a parameter-extraction method is mainly determined by the metaheuristic algorithm, but it is also affected by the simulation current calculation method and the parameter-extraction object. In a word, the approximation method is not suitable for application in PV-model parameter extraction because of incorrect estimation of the simulation current and the RMSE, while the LW and NR methods are suitable for the application for accurately calculating the simulation current and RMSE. In terms of saving computation resources and time, the NR method is superior to the LW method. Full article