Diode Model Research Articles

Extracting photovoltaic cells parameters for three diode model using HSDA algorithm

Developing the metaheuristic algorithms directly for extracting the photovoltaic cells and panels (PV) parameters or the implementation of algorithms for PV already used for other problems conduct to accurately obtain parameters and, generally, in a short time. The objective of the paper is to accurately extract the parameters of the photovoltaic cells and panels. To obtain this the hybrid successive discretization algorithm (HSDA) is applied to the triple diode model (TDM) for the extraction of photovoltaic cells and panels parameters. Using HSDA, nine parameters are extracted for two photovoltaic cells and four photovoltaic panels. The HSDA algorithm results for root mean square errors (RMSE) and mean absolute errors (MAE) are compared with other ones obtained with the best algorithms from research literature for prove the performance of the algorithm considered. The comparison proves that the HSDA algorithm has lower or equal RMSE. Therefore, the decrease in RMSE varies from 0.4 % to 38.5 %, in the case of the RTC France silicon photovoltaic cell (La Radiotechnique Compele) and for the Photowatt - PWP201 photovoltaic panel the variation is from 0.01 % to 813 %. The RMSE for STM6–40 is around 4 times lower. The root mean square error decreases 4 times for Kyocera KC200GT and for PVM 752 GaAs the decrease varies between 33 % and 6 times. The comparison between the models used for photovoltaic cells shows that the TDM has given lower values for RMSE and MAE proving it is the best solution to analyze them.

An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

The Impact of Soiling on Temperature and Sustainable Solar PV Power Generation: A detailed Analysis

Soiling accumulation and high temperatures have a detrimental impact on the performance of solar photovoltaic modules. However, the effect of soiling on module temperatures remains a relatively unexplored area despite offering intriguing research possibilities. This study investigates the impact of soiling on solar photovoltaic modules, focusing on the variation in module temperatures. The research uses experimental investigations and a hybrid diode model to account for soiling losses. Furthermore, the model is employed to quantify the power reduction due to the temperature rise caused by soiling. The experimental investigations revealed that soiling deposition results in both substantial energy reductions and higher module temperatures. The soiling-induced variation in temperature profiles and corresponding power reductions on certain days was also analyzed. The results showed that the daily average reduction in power due to increased temperature was 0.614%, 1.044% and 1.31%, while on the same days, the daily maximum reduction observed was 1.55%, 2.53% and 3.46%, respectively. Thus, higher temperatures lead to substantial power degradation and may also affect the health of PV modules in the long run. The outcomes of this study emphasize the importance of addressing soiling-induced temperature variations, offering valuable insights for improved design and maintenance practices of power plants.

An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation

Identification of photovoltaic (PV) module characteristics in solar systems is a vital task, nowadays, for optimal PV power estimation. In this paper, this challenge task has been studied using a novel advanced Kepler optimization algorithm (KOA). The standard version of KOA is adopted and assessed for getting the nine parameters of the PV triple diode model (3DM) considering three different practical PV modules. Kepler's principles of planetary motion are used by KOA to forecast the location and velocity of planets at any particular moment. However, the success rate of the KOA is not compatible, and its efficiency needs to be enhanced. As a result, an Improved KOA (IKOA) is created by incorporating an advanced mechanism of Local Escaping Operator (LEO), resulting in improved process of searching with evading local optima. This mechanism means that the exploitation approach will activate with around half of the solutions for every iteration starting at the initial phase of the iteration journey. The suggested IKOA besides the standard KOA are developed for predicting PV parameters for three distinct PV modules which are Photowatt PWP201, R.T.C France and STM6-40/36. The results corresponding to the latest algorithms are also compared with the proposed IKOA about different published works. The simulation findings reveal that the suggested IKOA exhibits notable average improvement rates for the three modules of 62.27 %, 55.1 %, and 32.12 %, respectively. Furthermore, the suggested IKOA asserts significant superiority and robustness over previously reported results.

Determination of I-V curves for photovoltaic generator by an analytical method; Akbaba model

In order to maximize the amount of electrical energy generated and ensure effective use, the conventional I-V or V-I characteristics derived from the diode model of Photovoltaic (PV) Solar Panels are important. Unfortunately, these features cannot be extracted using linear equations. It follows that the answer requires extremely drawn-out, time-consuming procedures. Many studies focus on calculating maximum power extraction. The analytical solutions to these equations are also nonexistent. Programming on computers can assist in solving the equations. The Akbaba Model was created specifically for the PVG to address these challenges. The Akbaba Model relies heavily on coefficients A, B, and C in this equation. The diode model serves as the true model in this case, and the coefficients A, B, and C are calculated. Another advantage of this model is that, because its qualities have partially deteriorated with time, the genuine I-V characteristics of the PV Panels can be recovered by measuring again.

Novel optimized models to enhance performance forecasting of grid-connected PERC PV string operating under semi-arid climate conditions

This study focuses on modeling and forecasting the performance of a grid-connected photovoltaic (PV) string, aiming to enhance the accuracy of output power prediction, which is crucial for the effective management and stability of the electrical grid. While previous research has extensively examined PV modules, there is a notable lack of studies specifically targeting PV string behaviors, especially in harsh climates prevailing in semi-arid regions. This study addresses this gap by investigating the modeling and performance prediction of a Passivated Emitter Rear Cell (PERC) PV string under semi-arid climate conditions using analytical and predictive approaches based on both implicit and explicit models as Single Diode Model (SDM), Das Model (DM), Boutana et al. Model (BM) and Power Law Model (PLM). New mathematical models of DM and BM shape parameters versus temperature and irradiance along with novel analytical formulas giving DM shape parameters as a function of PV metrics have been introduced. The proposed approaches are validated using real measurements of a PERC PV string incorporating 12 JKM405M-72H PV modules operating at Green Energy Park (GEP) research facility in Morocco. The reliability of these approaches is assessed by comparing I-V curves, P-V curves and peak power generated and predicted by SDM, DM, BM and PLM to actual measurements. The comparison reveals that generated and predicted outcomes align well with measured data, with an average value of Normalized Root Mean Square Error (NRMSE) not exceeding 4.16 % for all models throughout the day. The findings indicate that the proposed approaches effectively predict the performance of the PERC PV string, accounting for climatic influences and providing insights into optimizing PV system performance, thereby contributing to improved grid stability in semi-arid regions.

Facile assembly of SnO2 thin film-based Al/SnO2/Al device for sensing of 260 nm UVC and 365 nm UVA radiation

An Al/SnO2/Al metal-semiconductor-metal (MSM) device was formed by depositing a ~55nm thin film of SnO2 on Si substrate. Structural, surface morphology and optical properties of the SnO2 film were studied. The current-voltage (I-V) characteristics of the device were recorded under the dark condition followed by 260 nm-UVC and 365 nm-UVA illumination conditions. Under the dark condition, the device shows a resistivity of 8.05 Ωcm at V=1.5V bias voltage. The device produces nearly symmetric rectifying-type I-V characteristics under forward and reverse bias conditions. The electrical properties of the device were explained using back-to-back Schottky diode model. Under the dark condition, the ideality factor of the junction under forward and reverse bias were estimated to be 1.08 and 1.72 respectively. The external quantum efficiency of the device under UVC was found to be ~518.72% and specific detectivity reached as high as 1.03×109 Jones at 2.0V with a response time of ~1s. The device showed a high signal-to-noise ratio between 0.5 and 1.5V bias voltages. The values of the ideality factor being greater than 1 was attributed to the presence of point defects in the SnO2 layer as verified by photoluminescence study. This study reveals that the device could be used as a UVC and UVA sensor at a relatively low bias voltage of ~1.5V.

A novel hybrid algorithm based on improved marine predators algorithm and equilibrium optimizer for parameter extraction of solar photovoltaic models

In the field of solar photovoltaic (PV) systems, the accurate and reliable extraction of parameters from PV models is crucial for effective simulation, evaluation, and control. Although various optimization algorithms have been widely used for parameter extraction in solar PV systems, the accuracy and reliability of the parameters extracted by these methods usually fall short of the expected standards. To address these shortcomings, a novel hybrid algorithm that combines the improved marine predators algorithm (MPA) with the equilibrium optimizer (EO), named IMPAEO, is proposed. In the IMPAEO, an elite opposition-based learning strategy is introduced to expand the exploration range of the algorithm to enhance population diversity; an adaptive weight coefficient is employed to improve the updating rule of the MPA, facilitating a balance between global exploration and local exploitation; the EO operator is integrated to enhance the performance of the MPA in the local exploitation phase to improve the solution quality and convergence speed; and the linear population size reduction (LPSR) technique is introduced to dynamically reduce the population size and improve the search performance. The effectiveness of the proposed IMPAEO is validated using the CEC2017 benchmark test suite, which is also applied to extract parameters of the single diode model, the double diode model, and three different PV modules. Comparative analysis with other algorithms demonstrates that the IMPAEO exhibits significant advantages in terms of solution quality, convergence speed, and algorithm stability. Consequently, the proposed IMPAEO not only proves to be efficient and reliable in parameter extraction for solar PV models but also offers a promising alternative in this field.

A tree seed algorithm with multi-strategy for parameter estimation of solar photovoltaic models

Tree seed algorithm, which is one of the metaheuristics algorithms recently proposed for the solution of continuous optimization problems, has an effective algorithmic structure inspired by the relation between trees and seeds. At the same time, the use of two different solution generation mechanisms by depending on the control parameter in TSA aims to balance the exploration and exploitation capabilities of the algorithm. However, when the structure of the algorithm is examined in detail, it is seen that there are some disadvantages such as loss of population diversity and getting stuck in local minimums. To overcome these disadvantages in the basic algorithm, three different approaches (self-adaptive weighting mechanism, chaotic elite learning approach and experience-based learning method) were proposed to TSA under the name of multi-strategies in this study. The algorithm improved with these approaches is named as the multi-strategy-based tree seed algorithm (MS-TSA). MS-TSA was first tested on CEC2017 functions. Then MS-TSA was applied to the problems in the CEC2020 competition and compared with the results of the best performing algorithms in this competition. As a result of the comparisons, MS-TSA was found to be a competitive method on solving benchmark functions. Then, parameter estimation of single diode, double diode and photovoltaic module models using the input data of various solar panels was carried out by the MS-TSA. The results obtained with MS-TSA were compared with both the results of the basic TSA and the results of well-known algorithms in the literature. The results obtained are 9.8642E-04, 9.8356E-04, 2.4251E-03, 1.7534E-03 respectively. As a result of the comparative analysis, the lowest RMSE value was obtained by MS-TSA. In addition, comprehensive performance analyzes of the algorithms were made with the convergence curve, boxplots, current (I) - voltage (V) and power (P) - voltage (V) characteristic curves obtained according to the experimental results. As a result of the experiments and analyses, MS-TSA was found to be a more successful method than the compared algorithms in parameter estimation of PV models.

A Geometry-Scalable Physically-Based SPICE Compact Model for SiC MPS Diodes Including the Snapback Mechanism

In this paper, a simple compact model for the static behavior of SiC MPS diodes is developed in the form of a SPICE-compatible subcircuit. The model is suited to describe the undesired snapback mechanism likely to occur in unoptimized high-voltage MPS structures with narrow width of the PiN portion and/or very thick drift layer; in particular, the model accounts for the snapback mechanism both as the cell extension varies and as individual portions of Schottky and PiN vary. Sentaurus TCAD simulations of a 10-kV MPS diode are used as a reference for the calibration of the model parameters and accuracy verification.

ADJUSTABLE RESISTANCE FEEDBACK PIECEWISE LINEAR INTERPOLATION PHOTOVOLTAIC EMULATOR

Photovoltaic (PV) emulator (PVE) is a power converter that follows the voltage and current characteristic of a PV module. The direct referencing method (DRM) is the prevailing control strategy for the PVE because of its straightforward nature. Nonetheless, stability is an issue for this control strategy. The resistance feedback method (RFM) provides an alternative solution without encountering stability issues. The RFM requires a specialize current-resistance (I-R) PV model to operate. Currently, the adjustable piecewise linear interpolation (PLI) for this specialize PV model is unavailable for the RFM PVE. This paper proposed an adjustable I-R PLI (IRPLI) PV model for the PVE that uses the RFM control strategy. The performance of this PV model and its corresponding RFM PVE is compared with the current-voltage PLI (IVPLI) PV model together with the DRM PVE. The output of the IRPLI is similar to the IVPLI at various irradiance. The discrepancy in error for both PV models is a maximum of 6% when compared to the standard single diode model (SDM) PV model. The IRPLI is 3.2 times faster compared to the SDM and 1.07 times slower compared to the IVPLI. The IRPLI also able to properly integrated with the RFM PVM without any stability issues.

A back-to-back diode model applied to van der Waals Schottky diodes

The use of metal and semimetal van der Waals contacts for 2D semiconducting devices has led to remarkable device optimizations. In comparison with conventional thin-film metal deposition, a reduction in Fermi level pinning at the contact interface for van der Waals contacts results in, generally, lower contact resistances and higher mobilities. Van der Waals contacts also lead to Schottky barriers that follow the Schottky-Mott rule, allowing barrier estimates on material properties alone. In this study, we present a double Schottky barrier model and apply it to a barrier tunable all van der Waals transistor. In a molybdenum disulfide (MoS2) transistor with graphene and few-layer graphene contacts, we find that the model can be applied to extract Schottky barrier heights that agree with the Schottky-Mott rule from simple two-terminal current-voltage measurements at room temperature. Furthermore, we show tunability of the Schottky barrierin-situusing a regional contact gate. Our results highlight the utility of a basic back-to-back diode model in extracting device characteristics in all van der Waals transistors.

Analysis of Single Diode Model of Solar Cell Simulated with MATLAB for Maximum Electric Power Extraction

Analysis of Single Diode Model of Solar Cell Simulated with MATLAB for Maximum Electric Power Extraction

Design and modelling of SiC MPS diodes with superior surge current robustness

The investigation and modelling of the 4H–SiC merged PiN/Schottky (MPS) diode are presented for superior surge current capability design. A bipolar current transmission (BCT) model is proposed for investigating the current transport mechanism of MPS diodes under bipolar operation. The knee voltage (Vturn) defining the conversion from unipolar to bipolar operation is precisely modelled for controlling surge current. According to the proposed model, the method of designing a SiC MPS diode with high robustness against surge current is discussed and concluded primarily, which has great significance for improving the reliability of SiC MPS diodes. Meanwhile, we propose a new hybrid stripe cell design according model calculation and simulation for enhancing the surge current capability of the diode while maintaining high forward current capability. The proposed model and design method are verified by experimental results. And the proposed hybrid stripe cell design has superior surge current robustness while it almost avoids the forward current degradation in experiment.

Light induced degradation of CIGS solar cells

Considering warranted lifetimes of PV modules of typically 20-30 years, the performance stability of PV modules is one of the most crucial factors regarding power generation and yield. Long term outdoor performance studies depict realistic operation conditions best, but the correlated nature of e.g. irradiance and temperature impedes the physical interpretation. The possibility to control and tune the conditions in laboratory experiments enables to decompose effects, that are typically overlayed in outdoor experiments. This way, laboratory are crucial to interpret the results obtained in outdoor degradation studies. One driving force of degradation of CIGS modules is light exposure. In literature the focus of light induced degradation (LID) of CIGS modules and solar cells is on metastable changes analyzed on time scales ranging from minutes to hours. In this work we expose industrially produced encapsulated CIGS solar cells for more than 1000 h to light with varied intensity under varied temperature conditions. Such, we aim to study temperature and light intensity dependencies of the observed performance changes. Furthermore, we study the influence of applied bias by comparing LID at short and open circuit. We demonstrate that LID under short circuit conditions leads to VOC degradation, while being temperature assisted and not dependent on the irradiance intensity. CIGS solar cells kept at open circuit conditions appear to be stable under illumination. Exploiting the one diode model, we further connect the observed temperature assisted performance loss to enhanced recombination with lower ideality factor, in comparison to the dominant recombination process before degradation.

Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models

As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.

Explicit representation of S-shaped and standard V–I curve of illuminated solar cell

A generalized explicit model is proposed in this study to model the S-shaped and standard voltage-current characteristics (V–I) of illuminated solar cells. Even though the voltage-current characteristics of a typical solar cell follow a single exponential model (single diode model), there is evidence of S-shaped voltage-current characteristics that cannot be captured by the existing standard analytical models. The proposed explicit analytic representation is comparatively simple and produces satisfactory results in depicting the voltage-current curve for a wide variety of solar cells, including those with S-shaped characteristics. The methodology for determining the empirical parameters of the proposed model is also described, including an easy estimation of maximum power point voltage using model parameters.

Electrical and thermal performance of bifacial photovoltaics under varying albedo conditions at temperate climate (UK)

Bifacial photovoltaics (bPV) provide an advantage over their traditional monofacial counterparts as they can utilise solar radiation incident on both the front and rear side of the module, allowing for increased energy production. While this is an advantage on the side of bPV, the amount of energy produced by the rear side of the bPV panel can vary greatly depending on the inclination and azimuth of the panel as well as external climatic and ground albedo conditions. This paper aims to analyse the electrical and thermal performance of bPV under varying albedo conditions, using two different materials: grass, and a reflective material. It also contains an example of bifaciality testing under real-world conditions. Several experiments were conducted to evaluate the electrical performance and the temperature distribution of the bPV panel under different weather conditions at the UK location and then the results were compared with the single diode model of bPV. Furthermore, a bPV panel temperature model has been developed for the temperate climate condition of UK. The results show that the open circuit voltage of the system increases with irradiance up to around 800 W/m2, at which point it decreased due to the increased PV system temperature. The normalised efficiency for the PV system under different conditions were also evaluated, which showed that the bPV module was most efficient under diffuse irradiance conditions, encouraging the use of the bPV technology in the UK.

RLC resonator with diode nonlinearity: Bifurcation comparison of numerical predictions and circuit measurements

A nonlinear RLC resonator is investigated experimentally and numerically using bifurcation analysis. The nonlinearity is due to the parallel combination of a semiconductor rectifier diode and a fixed capacitor. The diode’s junction capacitance, diffusion capacitance, and DC current–voltage relation each contribute to the nonlinearity. The closely related RL-diode resonator has been of interest for many years since its demonstration of period-doubling cascades to chaos. In this study, a direct comparison is made of dynamical regime maps produced from simulations and circuit measurements. The maps show the variety of limit cycles, their bifurcations, and regions of chaos over the 2D parameter space of the source voltage’s frequency and amplitude. The similar structures of the simulated and experimental maps suggest that the diode models commonly used in circuit simulators (e.g., SPICE) work well in bifurcation analyses, successfully predicting complex and chaotic dynamics detected in the circuit. These results may be useful for applications of varactor-loaded split ring resonators.

Nonlinear behavioral analysis of diode model using Volterra series

Nonlinear behavioral analysis of diode model using Volterra series