Parameters Of Photovoltaic Module Research Articles

Optimal design of the modelling parameters of photovoltaic modules and array through metaheuristic with Secant method

Nowadays, the performance of photovoltaic (PV) panels is a priority research topic. Obtaining the best performance of these panels requires an adequate and accurate model. This paper employs a new approach based on a modified social network search algorithm combined with the Secant method (MSNS-SEC) to produce the best parameters of a photovoltaic cell, module, and array. To improve the performance of the parameters to be estimated, a control parameter via a Gaussian and Cauchy distribution is randomly added to the search space to allow the agents to converge to the optimal solution. Then the Secant method is inserted into the objective function to calculate the best-estimated currents. The application of the proposed model on three different systems, namely a PV array, cell, and module, and the subsequent comparison with existing methods exhibit the high accuracy of the proposed method, with the best root mean square error of 6.6851 × 10−4 for the RTC cell, 1.5411 × 10−3 for the Photowatt PWP module and 0.0134 for the experimental 18 PV array.

Mono-crystalline and Poly-crystalline Photovoltaic Modules Performance under Ambient Condition of Dodoma, Tanzania

Photovoltaic (PV) modules are rated based on the Standard Test condition (STC). However, real-time performance under ambient conditions differs from performance reported by manufacturers. In this paper, the electrical performance of mono-crystalline(m-Si) and poly-crystalline(p-Si) installed 25 m at the rooftop building at University of Dodoma in Tanzania are presented. Results show that electrical parameters for each PV module strongly depend on the ambient parameters under consideration. At lower solar irradiance (below 400W/m2) and lower module temperature (below 25°C), all PV module parameters were found to increase with increasing solar irradiance and module temperature. However, at higher solar irradiance and elevated module temperature, PV module parameters except the current showed a decreasing trend with increasing solar irradiance, module temperature, and relative humidity. In comparison, m-Si with a normalized power output of 78% under ambient conditions is slightly better than p-Si which exhibits 73%.

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

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

Temperature-Irradiance Matrix and Determination of Temperature Coefficients of a Monocrystalline PV Module

Photovoltaic (PV) modules performance testing and energy rating as described in IEC 61853-1 standard depend on electrical performance parameters (short-circuit current, open-circuit voltage, maximum-power) of PV modules as a function of temperature and irradiance. In this work, in order to precisely determine the effects of temperature on the electrical parameters of a monocrystalline PV module, the temperature controlled, xenon light based solar simulator system with irradiance attenuating masks was used. This solar simulator, according to the IEC 60904-9 standard in terms of spectral match, spatial non-uniformity and temporal instability has A+A+A+ classes which are two times better than the standard requirements for a solar simulator to be used in PV module measurements. Moreover, the thermal chamber used in this work is a closed type chamber with fast opening door for not allowing the distortion of temperature uniformity over the surface of PV modules under test. Within about 2 m × 2 m area within 15°C to 75°C temperature interval, the temperature uniformity obtained for this system is less than 1.0°C which is almost two times better than the IEC 60891 standard requirements (±2.0°C). The temperature and irradiance dependent measurements of the electrical performance parameters of a mono-crystalline PV module at various irradiance levels and the evaluation of its temperature coefficients [α (% °C-1), β (% °C-1) and δ (% °C-1)] were done by implementing the interpolation method described in IEC 60891 standard.

A hybrid wind driven-based fruit fly optimization algorithm for identifying the parameters of a double-diode photovoltaic cell model considering degradation effects

The identification of unknown parameters of photovoltaic modules is the keystone to model their performance accurately. This paper introduces a novel hybrid wind driven-based fruit fly optimization algorithm to determine a double-diode photovoltaic cell model’s seven unknown parameters. Due to the limitations of reaching a matured convergence of the classical wind driven optimization for complex multi-modal optimization problems, this paper presents a hybrid algorithm by integrating the wind driven optimization algorithm’s exploitation and fruit fly optimization algorithm’s exploration capacities. The effectiveness of the proposed model is validated using real data from three photovoltaic technologies: mono-crystalline, poly-crystalline, and thin-film. Besides, its computational efficiency and precision are compared with those of various models: deterministic- and metaheuristic-based models. The average values of the standard deviation, normalized-root-mean-square error, mean absolute percentage error, coefficient of determination, and convergence speed of the proposed model were 8.1101 × 10-9, 0.0911%, 2.5661%, 99.0115%, and 10.0112 s. for mono-crystalline PV module, 7.1129 × 10-9, 0.1029%, 2.6334%, 98.9331%, and 8.1201 s. for poly-crystalline PV module, and 6.2212 × 10-9, 0.0871%, 2.3129%, 99.1256% and 9.3211 s. for thin-film PV module. Findings indicate that the proposed model outperforms the aforementioned models in accuracy, convergence speed and feasibility. In addition, it can work blindly with any current-voltage characteristic curve on a 15-min. basis under any weather condition without the need for any initial guess or previous information about any parameter.

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

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

Improved Hybrid Parameters Extraction of a PV Module Using a Moth Flame Algorithm

The identification of actual photovoltaic (PV) model parameters under real operating condition is a crucial step for PV engineering. An accurate and trusted model depends mainly on the accuracy of the model parameters. In this paper, an accurate and enhanced methodology is intended for PV module parameters extraction in outdoor conditions. The proposed methodology combines numerical methods and analytical formulations of the one diode model to derive the five unknown parameters in any operating condition of irradiance and temperature. First, the measured I-V curves at a random weather condition are translated to standard test conditions (i.e., G = 1000 W/m2, T = 25 °C), using translation equations. The second step consists of using an optimization algorithm namely the moth flame algorithm (MFO) to find out the five parameters at standard test conditions. Analytical formulations, at a random irradiance and temperature, are then used to express the unknown parameters at any irradiance and temperature. The proposed approach is validated under outdoor conditions against measured I-V curves at different irradiances and temperatures. The validation has also been performed under dynamic operation where the measured maximum power point coordinates (MPP) are compared to the predicted maximum power points. The obtained results from the adopted hybrid methodology confirm the accuracy of the parameter extraction procedure.

A novel two step method to extract the parameters of the single diode model of Photovoltaic module using experimental Power–Voltage data

Accurate modeling of the Photovoltaic (PV) module is required to find the location of the fault in the PV array. It also helps in designing a precise MPPT algorithm and avoiding uncertainties in the Micro-grids. This paper uses a Two-Step Linear Least Square Error (LLSE) method to extract the parameters of the PV module. The proposed method utilizes the experimental data of the Power–Voltage (P–V) curve to estimate the PV module parameters. The proposed method does not require any datasheet information and avoids initial guesses of parameters. To validate the proposed method, three different PV modules, Mono-crystalline STM6-40/36, Poly-crystalline SMP6-120/36, PWP-201, and one Real-Time Clock France solar cell have been considered. The performance of the proposed method was studied using the Root Mean Square Error, Mean Absolute Error (MAE), Normalized Root Mean Square Error (NRMSE), and Normalized Mean Absolute Error (NMAE). The proposed LLSE method exhibited the low Root-Mean-Square-Error (RMSE) for all three PV modules and the R.T.C France solar cell.

Parameter extraction of single‐diode photovoltaic module using experimental current–voltage data

SummaryAccurate modeling of photovoltaic (PV) module allows proper load scheduling and avoids the uncertainties in grid. This paper uses forward‐backward linear least square error (FB‐LLSE) curve fitting method to estimate the parameters of PV module. The proposed method does not require any data sheet information and initial values of parameters. The proposed method utilizes the experimental data on I–V curve to extract the parameters of PV module. To validate the proposed method, three different PV modules, namely, Mono‐crystalline STM6‐40/36, Poly‐crystalline SMP6‐120/36, PWP‐201, and one Real Time Clock (R.T.C.) France solar cell has been considered. The obtained results are compared with three existing methods in the literature. The performance of the proposed FB‐LLSE method is studied with root mean square error (RMSE), mean absolute error (MAE), normalized root mean square error (NRMSE), and normalized mean absolute error (NMAE). The calculated with proposed FB‐LLSEM is reduced by 10%, 7%, 2.26%, and 1.89%, respectively, for PWP‐201, STMP‐120/36, SMP6‐40/36, and R.T.C France solar cell when compared to the LLSEM method available in the literature. The proposed FB‐LLSE method is found to exhibit low value of RMSE for all three PV modules and one PV cell.

Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules

How to effectively realize the simulation, evaluation, and control of the photovoltaic (PV) system established on the actual measured voltage and current PV cells and module data has attracted widespread attention. The original SE possesses the disadvantages of slow convergence and poor accuracy in the parameter identification of PV cells and modules. This paper proposes an enhanced spherical evolution algorithm (SE) based on a novel dynamic sine-cosine mechanism (DSCSE). The introduction of the dynamic sine-cosine mechanism significantly promotes the information communication of disparate individuals and increases the diversity of diverse populations. To assess the performance of DSCSE, it is compared with ten comparative algorithms to estimate unknown parameters of PV cell and module at fixed and varying temperature and light conditions, including single diode model (SDM), double diode model (DDM), three diode model (TDM) and PV module. The experimental results indicate that the root mean square of the error (RMSE) gained by DSCSE outperforms most competing algorithms. The results of RMSE by DSCSE for SDM, DDM, and TDM of commercial solar cells R.T.C. France and PV module of Photowat-PWP201 is the percentage of improvement of 48.45%, 6.85%, 11.81%, and 4.73% compared to SE, respectively. Furthermore, for three manufacturers, including Mono-crystalline (SM55), Thin-film (ST40), and Multi-crystalline (KC200GT), the results of RMSE by DSCSE harvest the maximum and minimum increase of 96.1% and 31.36%. Therefore, DSCSE is expected to become a novel promising technology to estimate the parameter of PV cells and modules.

An online optimization method for extracting parameters of multi-parameter PV module model based on adaptive Levenberg-Marquardt algorithm

This paper proposes a novel method to estimate the optimal parameters of a single-diode photovoltaic (PV) module model as well as the active power of PV generation. First, a multi-parameter PV module model is established considering the changes of temperature and solar irradiance; thus, the model is adaptive to different environmental conditions. Second, the model parameter estimation is designed as an optimization problem. An online optimization scheme that only requires the normal operation information is proposed, which avoids the need for an offline experiment to determine the current–voltage (I-V) curve as in conventional PV modeling methods. Third, the adaptive Levenberg-Marquardt (ALM) method with an adaptative damping factor selection strategy is developed to solve the parameter optimization problem, which overcomes the singular issue of traditional LM methods. Moreover, the global sensitivity analysis (GSA) is leveraged to identify critical parameters affecting the PV generation output power, which provides information on model simplification. Finally, the proposed method is tested on data from real-world PV systems in Bend, Oregon, USA, under diverse conditions and scenarios. It is revealed that compared with commonly used methods, the proposed method obtains more accurate PV module parameters and offers PV generation outputs closer to the true outputs.

Parameters identification of photovoltaic cells using improved version of the chaotic grey wolf optimizer

Precise estimation of critical parameters of the photovoltaic models is a highly demanding task in modeling and simulation of the photovoltaic systems. In this paper, a new enhanced optimization method is proposed to estimate the unknown parameters of photovoltaic modules. The proposed adaptive chaotic grey wolf optimization (ACGWO) algorithm is employed to estimate the parameters of solar cells models for single-diode, double-diode, three-diode. The suggested optimization method is obtained by combining the adaptive grey wolf optimization (AGWO) and chaotic grey wolf optimization (CGWO) algorithms. Minimization of Root Mean Squared Error (RMSE) as employed as the common objective function. Besides, the results are compared with some well-known algorithms. The results of RMSE are compared with commonly used objective functions such as the sum of squared error (SSE) and Maximal Absolute Error (MAE). The efficiency of the ACGWO method is analyzed, and the results of the simulation report the lowest values for RMSE and confirm accuracy, robustness, and high convergence speed in comparison with some well-known optimization methods. According to the results, the proposed ACGWO could outperform four other methods competitive based on RMSE values, and the best values are reported for the ACGWO method.

Diagnostics of PID Early Stage in PV Systems

Potential induced degradation (PID) is a serious threat for the photovoltaic (PV) industry. The risk of PID may increase with increasing operating voltage of PV systems. Although PID tests are currently standard tests, the expansion of floating PV power plants and installation in humid climates show that PID-free modules are still sensitive to this type of degradation. Therefore, a method that can detect PID in the initial phase before standard tests reveal it, is necessary to increase the reliability of PV systems and maintain their lifetime. One possible tool for revealing early-stage PID manifestations is impedance spectroscopy and I-V dark curves measurements. Both IS and dark current measurement methods are sensitive to cell shunt resistance (RSH), which is strongly influenced by PID before significant power loss and can act as an early stage PID detection mechanism. The paper describes the differences of the common P-type PV module parameters both during the degradation process and also during the regeneration process when diagnosed by conventional and IS and dark current measurement methods.

A Self-adaptive Algorithm with Newton Raphson Method for Parameters Identification of Photovoltaic Modules and Array

The word’s demand for renewable energy has be rinsing incrementally. One of the solutions for the energy crisis is photovoltaic. However, the design and development of better performing photovoltaic cells and modules requires accurate extraction of their intrinsic parameters. Metaheuristic algorithms have been reported to be the best methods for obtaining accurate values of these intrinsic parameters. However, local convergence goes against the recently devised heuristic methods and inhibits them from producing optimal result. This paper proposes a hybrid method that is based on the Newton Raphson method and a self-adaptive algorithm called the Drone Squadron Optimisation. The latter is an artifact technique inspired by the simulation of a drone squadron from a command centre. It is proposed that this hybrid method can help extract the best intrinsic parameters of photovoltaic cell and module. This study also provides insights and clarification on the reported approaches that have been recently proposed to formulate the objective function. Further, this study also computes and compares the ten best recently published heuristics algorithms in the domain of photovoltaic estimation. The study’s results obtain point to the difference between the two formulations and the accuracy of the best formulation. The results obtained from the six case studies covered in this study present the combined performance of the Newton Raphson method and Drone Squadron Optimisation to extract the accurate parameters of a photovoltaic module.

Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models

In photovoltaic (PV) model, it is an urgent problem to control and optimize the accurate parameters. Hence, many algorithms have been proposed for parameter extraction of different PV models. However, the ability of many optimization algorithms is greatly affected by parameters, and the same parameter is not suitable for different model problems. In recent years, reinforcement learning has achieved competitive results in solving the problem of maximizing returns through learning strategies in the process of interaction with the environment. Therefore, in this paper we propose a new algorithm which combines differential evolution algorithm with reinforcement learning. Specifically, in the iterative process, the fitness function value is evaluated to determine the action reward for adjustment of parameter value, and the parameter value is adjusted through reinforcement learning to obtain the most suitable algorithm parameters for the environment model. The performance of the proposed approach has been verified by extracting single diode model, double diode model and PV module parameters, The simulation results (root mean square error) of single diode model (9.8602E−04), double diode model (9.8248E−04) and 2.4251E−03 for the Photowatt-PWP201, 1.7298E−03 for STM6-40/36 and 1.6601E−02 for the STP6-120/36 comprehensively show that the algorithm has better accuracy and robustness when compared with other advanced algorithms.

Differential evolution with dynamic control factors for parameter estimation of photovoltaic models

Parameter estimation of photovoltaic (PV) models is an essential component in the design of a PV system with enhanced performance. Although many reliable solutions have been proposed by various authors in recent years, the estimation of PV model parameters is still an emerging area and remains an important focus. The estimation problem is formulated as a single objective function to be minimized based on the difference between the experimental and estimated current. Although different variants of differential evolution (DE) have been proposed in the past, in the current work, a DE with dynamic control factors (DEDCF) is proposed. DEDCF considers the experimental I–V datasets to estimate the parameters of single- and double-diode-based PV cell models and PV modules. The control factors include mutation and crossover factors, both of which need to undergo dynamic adjustment to arrive at better solutions. The results show the efficiency of DEDCF in comparison with other methods proposed in recent works for estimating the parameters of actual PV cells and modules.

The investigation of PV systems quantum efficiency in various irradiation condition

We confirm the importance of the angle of incidence of direct solar radiation on external quantum efficiency of solar mini-modules consisting of the following closed structure: silicon solar cells with antireflective coating (Si + ARC1)/EVA (ethylene–vinyl acetate) foil /glass with or without antireflective coating (ARC2). The results are consistent with a proposed model of an optical module with a specific architecture.A study of the electrical parameters of photovoltaic modules as a function of the angle of incidence of radiation was carried out using a solar class AAAA simulator. The changes in short circuit current Isc were calculated relative to Isc for the module fully illuminated by light perpendicular to the surface. A decrease in Isc was recorded for modules containing textured glass or Flat glass. The use of antireflective coating ARC2 on glass in module structure increases external quantum efficiency and photogeneration current under AM1.5 global spectrum at 1000 W/m2 light intensity at temperature 25 °C, mainly for the angle of incidence of 35°.

Locomotion-based Hybrid Salp Swarm Algorithm for Parameter Estimation of Fuzzy Representation-based Photovoltaic Modules

Identifying the parameters of photovoltaic (PV) modules is significant for their design and simulation. Because of the instabilities in the weather action and land surface of the earth, which cause errors in measuring, a novel fuzzy represen-tation-based PV module is formulated and developed. In this paper, a novel locomotion-based hybrid salp swarm algorithm (LHSSA) is presented to identify the parameters of PV modules accurately and reliably. In the LHSSA, better leader salps based on particle swarm optimization (PSO) are incorporated to the traditional salp swarm algorithm (SSA) in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA. By this integration, the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region. The proposed LHSSA is investigated on different PV models, i. e., single-diode (SD), double-diode (DD), and PV module in crisp and fuzzy aspects. By comparing with different algorithms, the comprehensive results affirm that the LHSSA can achieve a highly competitive performance, especially on quality and reliability.

Gradient-Based Optimizer for Parameter Extraction in Photovoltaic Models

Solar radiation is increasingly used as a clean energy source, and photovoltaic (PV) panels that contain solar cells (SCs) transform solar energy into electricity. The current-voltage characteristics for PV models is nonlinear. Due to a lack of data on the manufacturer's datasheet for PV models, there are several unknown parameters. It is necessary to accurately design the PV systems by defining the intrinsic parameters of the SCs. Various methods have been proposed to estimate the unknown parameters of PV cells. However, their results are often inaccurate. In this article, a gradient-based optimizer (GBO) was applied as an efficient and accurate methodology to estimate the parameters of SCs and PV modules. Three common SC models, namely, single-diode models (SDMs), double-diode models (DDMs), and three-diode models (TDMs) were used to demonstrate the capacity of the GBO to estimate the parameters of SCs. The proposed GBO algorithm for estimating the optimal values of the parameters for various SCs models are applied on the real data of a 55 mm diameter commercial R.T.C-France SC. Comparison between the GBO and other algorithms are performed for the same data set. The smallest value of the error between the experimental and the simulated data is achieved by the proposed GBO. Also, high closeness between the simulated P-V and I-V curves is achieved by the proposed GBO compared with the experimental.

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I) – voltage (V) and power (P) – voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer’s datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1.8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite .