Parameters Of Single-diode Model Research Articles

Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network

In this paper, an artificial neural network (ANN) is used for isolating faults and degradation phenomena occurring in photovoltaic (PV) panels. In the literature, it is well known that the values of the single diode model (SDM) associated to the PV source are strictly related to degradation phenomena and their variation is an indicator of panel degradation. On the other hand, the values of parameters that allow to identify the degraded conditions are not known a priori because they can be different from panel to panel and are strongly dependent on environmental conditions, PV technology and the manufacturing process. For these reasons, to correctly detect the presence of degradation, the effect of environmental conditions and fabrication processes must be properly filtered out. The approach proposed in this paper exploits the intrinsic capability of ANN to map in its architecture two effects: (1) the non-linear relations existing among the SDM parameters and the environmental conditions, and (2) the effect of the degradation phenomena on the I-V curves and, consequently, on the SDM parameters. The ANN architecture is composed of two stages that are trained separately: one for predicting the SDM parameters under the hypothesis of healthy operation and the other one for degraded condition. The variation of each parameter, calculated as the difference of the output of the two ANN stages, will give a direct identification of the type of degradation that is occurring on the PV panel. The method was initially tested by using the experimental I-V curves provided by the NREL database, where the degradation was introduced artificially, later tested by using some degraded experimental I-V curves.

A Noniterative Parameter-Extraction Method for Single-Diode Lumped Parameter Model of Solar Cells

As a key to simulate electrostatic characteristics of solar cells and implement single-diode model into simulators, a noniterative parameter-extraction method is proposed to determine five parameters of single-diode model. It is straightforwardly derived from the transcend equation set of terminal I - V equations to overcome the imperfections of poor efficiency in numerical iteration methods, low accuracy in semiempirical approaches, and absence of physical meaning in optimization algorithms. As a result, such a parameter-extraction method actually serves as a useful tool to acquire model parameters, analyze preparing process' results, and expand practicability of lumped parameter model.

A Complete Numerical Investigation on Implicit and Explicit PV Single-Diode-Models Using I- and V-Approaches

The single-diode-model (SDM) is widely used for the identification of a photovoltaic (PV) module output curve. However, the implicit nature of the model imposes a few difficulties such as need for an iterative solution method, high computation load and large computation time. In the last years, several mathematical approximations have been adopted in order to convert the implicit SDM into explicit one. In turn, this simplicity has invited other complications: inaccuracy in the curve identification, wrong maximum power point computation, additional parameters over the (five) SDM parameters etc. This article considers the complete and comprehensive comparisons of all possible ways (numerical methods) to compute the output curve. This article provides clear guidelines to the researchers in PV modeling and helps to select an appropriate numerical method. The SDM computation is carried out by the I-approach and V-approach using all possible methods. All methods are tested on cells and modules in uniform irradiance conditions, four commonly used case studies in PV literature. Also, the investigation is extended for two more case studies that include a PV string and a PV array operating under partial shading conditions. In summary, this article supplies a useful PV modeling manual that provides freedom to select a suitable method based on the I - or V-approach equations, the convergence criteria and the computation execution burdens. Overall, the work proposes the series expansion of Lambert-W in both approaches is the most suitable method for PV curves computation.

Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification

The whale optimization algorithm (WOA) is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA (MWOA) in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model (SDM), double diode model (DDM), and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules.

Parameter Extraction of Single-Diode Model From Module Datasheet Information Using Temperature Coefficients

The single-diode model (SDM) is used widely for modeling of photovoltaic cells and modules because of its simplicity and accuracy. The main objective of this article is to propose a new method to estimate the five unknown parameters of the SDM under standard test conditions using the module datasheet information (MDI) alone. The proposed method specifically uses the temperature coefficient (TC) of the maximum power of the module given in the MDI in conjunction with three physical or empirical models of the TCs of the photo-induced current, the diode reverse saturation current, and the diode ideality factor. It is well known that four equations that limit these parameters can be deduced naturally from the MDI, and a new fifth equation was deduced in this research from the MDI and the three TC models above without no simplification or other assumptions. The nonlinear equations for the SDM's parameters were solved using a simple mesh search algorithm. Finally, the proposed method was extended to produce an adaptive model to enable estimation of the SDM parameters at any irradiance and temperature.

Performance Evaluation of Analytical Methods for Parameters Extraction of Photovoltaic Generators

This paper presents a succinct exploration of several analytical methods for extracting the parameters of the single-diode model (SDM) of a photovoltaic (PV) module under standard test conditions (STC). The paper investigates six methods and presents the detailed mathematical analysis leading to the development of each method. To evaluate the performance of these methods, MATLAB-based software has been devised and deployed to generate the results of each method when used to extract the SDM parameters of various PV test modules of different PV technologies. Similar software has also been developed to extract the same parameters using well-established numerical and iterative techniques. A comparison is subsequently made between the synthesized results and those obtained using numerical and iterative methods. The comparison indicates that although analytical methods may involve a significant amount of approximations, their accuracy can be comparable to that of their numerical and iterative counterparts, with the added advantage of a significant reduction in computational complexity, and without the initialization and convergence difficulties, which are normally associated with numerical methods.

In-depth analysis of single-diode model parameters from manufacturer’s datasheet

The objective of this paper is to determine all the possible combinations of the five parameters of the single-diode model (SDM) of a photovoltaic panel when only the following three important points (remarkable points) of a I–V curve, namely, short circuit, maximum power and open circuit points, are available, usually from manufacturer’s datasheet. In this work, four of the five parameters of the SDM are expressed as explicit functions of the remaining one. Taking advantage of this, the monotony of these functions has been studied and the intervals where the corresponding parameters belong have been determined, that is, the domain of the parameters, in terms exclusively of the remarkable points. Using these functions, a unique SDM solution can be also easily determined if an extra data or equation is available. A possible application of this study is to validate if an extra equation is compatible with the set of equations obtained from the remarkable points. The results presented in this paper have been tested with a database gathering information of 8835 datasheets included in the Energy Commission’s Solar Equipment Lists. Comparisons have also been made with other works which have tried to obtain the SDM parameters only with datasheet information.

Solar cell parameter identification using the three main points of the current–voltage characteristic

A new efficient approach for extracting solar cell parameters based on the three main points of the I-V characteristic of the five-parameter model is presented. The proposed method allows extracting the single-diode model parameters using only the information provided by the manufacturer's datasheet or those obtained under else conditions and able to accurately represent their electrical behavior. A transcendental equation is formulated where the search space is reduced from five to two dimension; this equation links exclusively the series resistance and the ideality factor. The graphical optimization technique is proposed to solve the found equation. The accuracy of the method presented is justified by four important reference studies in the literature and has been widely used by researchers and two other cases presented in a single study. Besides, the results of the proposed procedure are compared to the most recent techniques proposed in the literature. Moreover, the results show that the current versus voltage I-V characteristics of the solar panels are presented with satisfactory precision with regard to the different algorithms and methods to solve the kind problems in different conditions of temperature and irradiation; in particular, at the point of maximum power, the error is always well below 0.01%.

Adjusting the Single-Diode Model Parameters of a Photovoltaic Module with Irradiance and Temperature

This paper presents a concise discussion and an investigation of the most literature-reported methods for modifying the lumped-circuit parameters of the single-diode model (SDM) of a photovoltaic (PV) module, to suit the prevailing climatic conditions of irradiance and temperature. These parameters provide the designer of a PV system with an essential design and simulation tool to maximize the efficiency of the system. The parameter modification methods were tested using three commercially available PV modules of different PV technologies, namely monocrystalline, multicrystalline, and thin film types. The SDM parameters of the three test modules were extracted under standard test conditions (STC) using a well-established numerical technique. Using these STC parameters as reference values, the parameter adjustment methods were subsequently deployed to calculate the modified parameters of the SDM under various operating conditions of temperature and irradiance using MATLAB-based software. The accuracy and effectiveness of these methods were evaluated by a comparison between the calculated and measured values of the modified parameters.

A method for hybrid extraction of single-diode model parameters of photovoltaics

This paper presents a hybrid method to extract the five unknown parameters of a single-diode photovoltaic (PV) model. The proposed method is a combination of analytical and optimization algorithms such that only two parameters, namely, series (Rs) and shunt resistance (Rsh), are estimated by using metaheuristic algorithms. The information of three major key points in datasheets provided by manufacturers is used for optimization and the sum of the squared error formulated. The rest of the unknown parameters, diode ideality factor (D), photo-generated current (Iph), and dark saturation current (Io) are obtained analytically. In order to predict the behavior of real performance characteristics of solar PV modules under different environmental conditions, a set of translational formulas have been used. Finally, performance indices, such as PV characteristics, absolute error in current, normalized root mean square error (nRMSE), maximum power, and relative maximum power error are estimated for six different types of PV modules from different technology to reveal the effectiveness of the proposed method. Through comparison with experimental data available in modules datasheet and existing method, it was found that the proposed method is sufficiently accurate.

Virtual Sensing of Photovoltaic Module Operating Parameters

The single diode equivalent circuit allows describing photovoltaic panels behavior quite accurately. The values of the five parameters appearing in this model are usually identified in standard test conditions, at which irradiance and temperature values are given. Instead, when this identification has to be performed on field, suitable environmental sensors are required. In this article, a procedure for the on-site identification of the single diode model parameters as well as of the working irradiance and temperature conditions is presented. The procedure operates only on the measured panel current versus voltage curve, or on a part of it around the maximum power point, and does not need any other input data. A consolidated identification algorithm, effectively running on any embedded system for on-site application, is used. The proposed procedure is validated through a large number of experimental measurements acquired in two different geographic locations.

Brent’s algorithm based new computational approach for accurate determination of single-diode model parameters to simulate solar cells and modules

Simulated current-voltage (I-V) characteristics of photovoltaic (PV) cells and modules are significant for the performance assessment, design and quality control, which are decided by the accurate determination of the intrinsic parameters of the devices. Commonly, a single-diode model is utilized to extract these parameters such as the ideality factor (n), series resistance (Rs), shunt resistance (Rsh), saturation current (Io) and photo-generated current (Iλ). Driven by this idea, a new mathematical manipulation was performed on the single-diode equation that yielded a non-linear formula of Rs. Later, Brent’s algorithm was used to precisely estimate Rs at every fine-tuned point of n, thereby all other parameters were determined. The set of parameters that provided the lowest root mean square error (RMSE) between the experimental and simulated I-V data were chosen to be optimum. The proposed Brent’s algorithm (BA) was shown outperform several recently reported computational and heuristic algorithms that were exploited to mine the single-diode model parameters for solar cells and modules with varied device temperatures and solar irradiation conditions.

Combining Identification and Translation Methods of the Single-Diode Model to Compute the Average Temperature of Photovoltaic Modules From the Open-Circuit Voltage

A common method of measuring the temperature of a photovoltaic module is by attaching a sensor to its back surface. However, since this method of measurement is punctual, the temperature gradient along the module surface is not considered. In addition to that, the temperature of the sensor is usually considered equal to the temperature of the photovoltaic cells, thus ignoring the temperature drop along the materials in between. This paper focuses on the problem of computing the average cell temperature of photovoltaic modules, based on information available on their respective datasheets and on measurements of the open-circuit voltage and solar irradiance. For that, different methods regarding identification and translation of the single-diode model parameters are used in conjunction, aiming to establish a simple and accurate procedure for computing the average temperature. The best performing combinations are then compared with IEC-60904-5, which presents a procedure for calculating the equivalent cell temperature of a module. Such computing procedures for the average module temperature have been experimentally tested, presenting coherent results.

Extraction of the PV modules parameters with MPP estimation using the modified flower algorithm

Modeling of photovoltaic (PV) module remains a serious issue for a lot of applications such as monitoring system or fault detection system. Therefore, several equivalents models of the PV cell have been proposed, the famous proposed models are called the single diode model (SDM) and double diode model (DDM). Each model possesses unknown parameters values which must be defined. In the present paper, two electrical models equivalent to PV cell are proposed, these models have an unknowns parameters which must be identified. The modified flower algorithm (MFA) is an optimization algorithm inspired from the nature, this algorithm is used to extract the optimal parameters values for both models. The proposed algorithm mimics the pathways of pollen transfer to help produce plants in nature, in other words, there are a lot of ways that the pollen can be travel to reproduce the plants these ways can be developed to a powerful optimization algorithm. In order to assess the proposed algorithm, several experimental data are used, these data are acquired in outdoors conditions and contains various I-V curves, these I-V curves are taken from three kind of PV cell technologies which namely Monocrystalline, Polycrystalline and Amorphous. In addition, the simulation results are compared with experimental data for both models. Moreover, the identified SDM parameters are applied to predict the current, the voltage and the power at the maximum power point (MPP) which was then compared to the MPP obtained from real data of grid connected photovoltaic system (GCPVS).

Non-Iterative Methods for the Extraction of the Single-Diode Model Parameters of Photovoltaic Modules: A Review and Comparative Assessment

The extraction of the photovoltaic (PV) model parameters remains to this day a long-standing and popular research topic. Numerous methods are available in the literature, widely differing in accuracy, complexity, applicability, and their very nature. This paper focuses on the class of non-iterative parameter extraction methods and is limited to the single-diode PV model. These approaches consist of a few straightforward calculation steps that do not involve iterations; they are generally simple and easy to implement but exhibit moderate accuracy. Seventeen such methods are reviewed, implemented, and evaluated on a dataset of more than one million measured I-V curves of six different PV technologies provided by the National Renewable Energy Laboratories (NREL). A comprehensive comparative assessment takes place to evaluate these alternatives in terms of accuracy, robustness, calculation cost, and applicability to different PV technologies. For the first time, the irregularities found in the extracted parameters (negative or complex values) and the execution failures of these methods are recorded and are used as an assessment criterion. This comprehensive and up-to-date literature review will serve as a useful tool for researchers and engineers in selecting the appropriate parameter extraction method for their application.

A Numerical-Analytical Hybrid Approach for the Identification of SDM Solar Cell Unknown Parameters

Appropriate modeling and accurate parameter identification of solar cells are crucial in the optimization of photovoltaic (PV) systems. The single-diode model (SDM), consisting of an ideal current source, an ideal diode, a shunt resistor and a series resistor, is widely used to simulate the behavior of PV cells/panels. In this article, a hybrid approach for identification of solar cell SDM parameters is presented. This approach uses the inverse of the slope of the I-V curve under short-circuit and open-circuit conditions and combines numerical and analytical solutions. Indeed, knowing that numerical methods require appropriate initial values, the main idea of the proposed approach is to provide these solutions by analytical methods. The comparison of obtained results with experimental ones, based on manufacturer’s datasheet, proves that the algorithm thus obtained requires less information from the manufacturer and improves significantly the parameter identification accuracy.

New method for computing single diode model parameters of photovoltaic modules

Abstract The objective of this paper is to calculate the single diode model parameters (A, Rs, Rsh, IL, and I0) of photovoltaic modules in most straightforward way no using iterative techniques and an additional information of pre-measured/digitized current-voltage curves. For that purpose, a new method based on solely datasheet information is introduced. The novelty of this method is to use a dark saturation current as an initial parameter as calculating the single diode model parameters. The effectiveness of proposed was validated using current-voltage curves of five commercially available photovoltaic modules at standard test conditions. The current-voltage curves were simulated using the proposed method and compared with datasheet supplied current-voltage curves by means of a root means square error tool. It has been founded that the new method, which proposes a very simple calculation procedure to obtain the single diode model parameters, meets international standards as simulating current-voltage curves of photovoltaic modules.

Online Identification of Photovoltaic Source Parameters by Using a Genetic Algorithm

In this paper, an efficient method for the online identification of the photovoltaic single-diode model parameters is proposed. The combination of a genetic algorithm with explicit equations allows obtaining precise results without the direct measurement of short circuit current and open circuit voltage that is typically used in offline identification methods. Since the proposed method requires only voltage and current values close to the maximum power point, it can be easily integrated into any photovoltaic system, and it operates online without compromising the power production. The proposed approach has been implemented and tested on an embedded system, and it exhibits a good performance for monitoring/diagnosis applications.

Variations of PV module parameters with irradiance and temperature

This paper presents a comparison of common and well-documented methods for varying the single-diode model parameters extracted at standard test conditions (STC) of a PV module to suit varying operating conditions of irradiance and temperature. To perform such a comparison, accurate values of the single-diode parameters at STC are required. These were obtained using well-established numerical and iterative methods. The Newton-Raphson method was found to be most accurate for obtaining these parameters at STC. Consequently, these parameters were used to compare the methods of varying the single-diode model parameters with temperature and irradiance. MATLAB software has been developed to evaluate the performance of each method using the Shell SQ150 PV module. Results are compared with measured data and discussion of the accuracy of various methods is presented.

An improved single-diode model parameters extraction at different operating conditions with a view to modeling a photovoltaic generator: A comparative study

Abstract This paper proposes advanced analytical, numerical and genetic algorithm (GA) approaches for retrieving the parameters of photovoltaic (PV) panel. A comparative study for extracting the five parameters of a single diode PV model is presented. Based on the datasheet values, a numerical based Newton-Raphson algorithm is investigated for solving the current-voltage relation of a single diode solar PV model. To highlight the rigorous performance of our models, a second analytical model is proposed. For improving the accuracy of solar panel parameters, a technique based on GA is established. This approach is based on the problem of research and optimization of the extracted parameters as an objective function. To account for variation in solar radiation and temperature, these models are presented under the reference and real operating conditions. The performances of the proposed algorithms are compared by using MATLAB scripts programming, and the theoretical advantages of GA model were demonstrated. The different models are validated experimentally by various tests of temperature and solar irradiance variation. The experimental results indicate that the GA model has a very satisfactory performance compared with the two other models and it offers good compromise between accuracy and fastness.