Diode Model Research Articles

CGH-GTO method for model parameter identification based on improved grey wolf optimizer, honey badger algorithm, and gorilla troops optimizer

The current–voltage (I–V) characteristics are significant for applied research in photovoltaics (PV). The I–V characteristics calculated by double diode model (DDM) are accurate. DDM contains seven parameters, which are hard to solve. The combination method (CGH-GTO) based on improved grey wolf optimizer (IGWO), honey badger algorithm (HBA), and gorilla troops optimizer (GTO) for model parameter identification is proposed. First, the random initialization of GWO is replaced by Circle chaotic mapping (CCM). IGWO is employed to provide good initial values of parameters. Then, the bird-guided method of HBA is applied to optimize GTO for precise identification. Finally, the effectiveness, stability, and practicality of CGH-GTO are verified through experiments. The optimal RMSE (Root Mean Square Error) and R2 (Coefficient of determination) of CGH-GTO are 0.00241A and 0.99999 for TSM-240. Compared to the reference method GTO, the improvement ratio of RMSE for CGH-GTO can reach 7.3%. For Photowatt-PWP 201, the average RMSE of CGH-GTO is 0.00184A in the stability experiment. The RMSE of CGH-GTO is stabilized at [0.00182A, 0.00350A]. For three PV cell models, the results of evaluation metrics for DDM present the optimum. CGH-GTO achieves the highest accuracy in model parameter identification for DDM with RMSE and R2 of 0.00182A and 0.99999.

A modified particle swarm optimization rat search algorithm and its engineering application.

Solar energy generation requires photovoltaic (PV) systems to be optimised, regulated, and simulated with efficiency. The performance of PV systems is greatly impacted by the fluctuation and occasionally restricted accessibility of model parameters, which makes it difficult to identify these characteristics over time. To extract the features of solar modules and build highly accurate models for PV system modelling, control, and optimisation, current-voltage data collecting is essential. To overcome these difficulties, the modified particle swarm optimization rat search algorithm is presented in this manuscript. The modified rat search algorithm is incorporated to increase the PSO algorithm's accuracy and efficiency, which leads to better outcomes. The RSA mechanism increases both the population's diversity and the quality of exploration. For triple diode model of both monocrystalline and polycrystalline, PSORSA has showed exceptional performance in comparison to other algorithm i.e. RMSE for monocrystalline is 3.21E-11 and for polycrystalline is 1.86E-11. Similar performance can be observed from the PSORSA for four diode model i.e. RMSE for monocrystalline is 4.14E-09 and for polycrystalline is 4.72E-09. The findings show that PSORSA outperforms the most advanced techniques in terms of output, accuracy, and dependability. As a result, PSORSA proves to be a trustworthy instrument for assessing solar cell and PV module data.

A novel method for reference parameters identification and electrical property estimation of PV modules under varying operating conditions

The parameters identification of the diode model under reference condition are crucial and have significant impact on output characteristics estimation of photovoltaic (PV) module under varying operating conditions. The traditional methods of extracting model parameters have always focused on solving the parameter optimization problem only under some certain condition, which neglects their effect of performance estimation under other operating condition. In this paper, a novel method is proposed to identify the reference physical parameters and enhance the accuracy of electrical property estimation for PV modules under varying operating conditions. New objective function with penalty function is constructed by considering the estimated performance not only under reference condition but also under multiple real operating conditions. The maximum permissible error (MPE) is proposed to constrain the accuracy for key electrical output indicators and added in penalty function to ensure the accuracy under reference condition. Taking into account the inequality constraints, the guaranteed convergence particle swarm optimization with penalty function (P-GCPSO) is introduced for parameter identification. A set of comparisons between the measured and calculated results of PV modules indicate that the proposed method substantially more accurate than other documented methods for both single and double diode model. For the six different materials tested, the root mean square error (RMSE) was compared. The RMSE of the single-diode model is reduced by at least 57.98% compared to the compared method, with a maximum reduction of 86.57%. Similarly, the RMSE of the double -diode model was reduced by at least 47.24% with a maximum reduction of 88.71% compared to the compared methods. Additionally, the tests with six different types of PV modules at varying environmental conditions for over one year prove that the proposed method is reliable and practical for real-world applications. The relative error of power generation reached a minimum of 2.20%. This proves the reliability and practicability of the proposed method in practical applications. The proposed method is envisaged to be valuable for both offline analysis and online monitoring applications where an accurate, fast and consistent performance estimation tool is required.

Broadband parasitic modeling of diodes in the millimeter-wave band

This paper presents the extraction of an equivalent circuit model for PIN diodes and varactors in the millimeter waves. This circuit model is handy for new communication applications involving, for example, electronic beam reconfigurability, where diodes are commonly used. For parameter extraction, the proposed model includes the effects of the device, the pads, and the gap underneath the device. From the measurement at various bias points (varactor) or states (PIN diode), it is possible to extract an equivalent circuit to properly model the behavior of these devices when using them to design reconfigurable elements. The results are validated experimentally, obtaining an excellent agreement between the measurements and the equivalent circuit models in a large frequency band up to 67 GHz.

Analysis of the behavior of TCAD and SPICE diode models in ESD protection circuits

To protect the interfaces of high-speed ICs from ESD, it is necessary to develop and optimize specialized protection circuit designs to minimize parasitic capacitance. The classic widely used designs based on ggMOS and SCR have too large parasitic capacitance, which degrades the parameters of the interface pins of the ICs. It is necessary to develop and optimize other ESD protection designs. One of the possible promising options is optimized diode protection. In this paper, an analysis of the effect of the design of a diode protection on its electrical characteristics is carried out. An analysis of the possibility of modeling protection using SPICE models was also performed and an assessment of the need to use TCAD simulation was carried out. Based on a comparison of the results of SPICE and TCAD simulations, it is shown that widely used SPICE diode models not optimal for designing and optimizing diode ESD protection. For this task, it is necessary to use TCAD simulation, taking into account the self-heating of the structure and the dynamics of the propagation of the thermal front. The results of this work are used for the development of ESD protection for interfaces of high–speed microcontrollers, DACs/ADCs, and transceivers.

Modeling of Schottky diode and optimal matching circuit design for low power RF energy harvesting

This work designs and implements a single-stage rectifier-based RF energy harvesting device. This device integrates a receiving antenna and a rectifying circuit to convert ambient electromagnetic energy into useful DC power efficiently. The rectenna is carefully engineered with an optimal matching circuit, achieving high efficiency with a return loss of less than −10 dB. The design uses a practical model of the Schottky diode, where both RF and DC characteristics are derived through extensive experimental measurements. The results from both experiments and simulations confirm the effectiveness of the design, showing its proficiency in efficient RF energy harvesting under low-power conditions. The antenna produced operates in the wifi band with a gain close to 4 dBi and a bandwidth of 100 MHz. With a load resistance of 1600 Ω, the proposed device achieves an impressive RF-to-DC conversion efficiency of approximately 52% at a low incident power of −5 dBm. These findings highlight the potential and reliability of rectenna systems for practical and efficient RF energy harvesting applications. The study significantly contributes to our understanding of rectenna-based energy harvesting, providing valuable insights for future design considerations and applications in low-power RF systems.

Current Model for Organic Diodes at Different Temperatures: Comparison With Universal Curve Equation

Current Model for Organic Diodes at Different Temperatures: Comparison With Universal Curve Equation

Parameter extraction of solar cell models using improved war strategy algorithm

A study proposed an improved war strategy algorithm called the chaos genetic manifestation war strategy algorithm (CGM-WSO) to extract parameters of solar cell model. The CGM-WSO algorithm incorporates three improvement measures. First, it introduces the logistic–tent chaos map to enable random perturbation during initialization. Second, it utilizes the crossover and mutation operators of the genetic algorithm to enhance the algorithm’s ability to escape local optima. Finally, the Lambert W function is employed to obtain explicit analytic expressions of single and double diode models. The performance of the CGM-WSO algorithm was validated using several cell’s models, and the results demonstrated significantly improved accuracy, reliability, and convergence speed compared to other algorithms.

Improved reference condition independent method for output performance estimation of PV modules under varying operating conditions

Traditional methods for estimating output property of the photovoltaic (PV) modules are strongly influenced by the selection of reference condition and transforming equations, which determine the calculated physical parameters under real operating conditions. The differences in the carrier transport properties of PV cells under varying operating conditions, such as the number and velocity of minority carriers at the junction edge and their recombination speed, lead to large deviations in the estimation of the output characteristics, especially under low irradiance conditions. To enhance the accuracy of performance estimation, we propose an improved method that is independent of reference condition. This method eliminates the impact of reference conditions and improves the transformation equations under all irradiance levels. Transformation equations of single diode model are established in different irradiance intervals based on the dependence of physical parameter on irradiance and temperature. Especially in the low irradiance range, all effects of irradiance and temperature are considered for each physical parameter in improved transformation equations. To optimize the unknown parameters in the transformation equations, the artificial hummingbird algorithm is used to fit experimental I–V data. The experimental results of six different types PV modules under a wide range of operating conditions are used to verify the effectiveness of the proposed method. The proposed method offers immediate benefits, including independence from reference condition and a more precise relationship between physical parameters and environmental factors in the estimation of PV output properties. Comparing the results to the traditional method by Laudani, the proposed method demonstrates superior capability in estimating I–V characteristics and accurately identifies the maximum power point under various operating conditions, which is of significant value for engineering applications.

Super‐evolutionary mechanism and Nelder‐Mead simplex enhanced salp swarm algorithm for photovoltaic model parameter estimation

AbstractIn the pursuit of enhancing the efficiency of solar cells, accurate estimation of unspecified parameters in the solar photovoltaic (PV) cell model is imperative. An advanced salp swarm algorithm called the Super‐Evolutionary Nelder‐Mead Salp Swarm Algorithm (SENMSSA) is proposed to achieve this objective. The proposed SENMSSA addresses the limitations of SSA by incorporating a super‐evolutionary mechanism based on a Gaussian‐Cauchy mutation and a vertical and horizontal crossover mechanism. This mechanism enhances both the global optimization capabilities and the local search performance and convergence speed of the algorithm. It enables a secondary refinement of the global optimum, unlocking untapped potential in the solution space near the global optimum and elevating the algorithm's precision and exploitation capabilities to higher levels. The Nelder‐Mead simplex method is further introduced to enhance local search capabilities and convergence accuracy. The Nelder‐Mead simplex method is a versatile optimization algorithm that improves local search by iteratively adjusting a geometric shape (simplex) of points. It operates without needing derivatives, making it suitable for non‐smooth or complex objective functions. To assess the efficacy of SENMSSA, a comparative analysis is conducted against other available algorithms, namely SSA, IWOA, SCADE, LWOA, CBA, and RCBA, using the CEC2014 benchmark function set. Subsequently, the algorithm was employed to determine the unknown PV parameters under fixed conditions for three different diode models. Additionally, SENMSSA is utilized to estimate PV parameters for three commercially available PV models (ST40, SM55, KC200GT) under varying conditions. The experimental results indicate that the SENMSSA proposed in this study displays a remarkably competitive performance in all test cases compared to other algorithms. As such, we consider that the SENMSSA algorithm constitutes a reliable and efficient solution for the challenge of PV parameter estimation.

Comparative analysis of the hybrid gazelle‐Nelder–Mead algorithm for parameter extraction and optimization of solar photovoltaic systems

AbstractThe pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems.

Radiation effects on photo diode modeling in space environment for laser inter-satellite links

In this paper the effect of space radiation environment on a silicon photo diode is studied. This detector is considered as a part of the optical receiver chain of the satellite and exposed to the radiation and vacuum environment of the space. The hardness of this detector during its confrontation to space environment is an important index for link performance evaluation. This effect is analyzed and simulated with Geant4, and then the simulation results are verified by irradiating protons to the PIN diode detectors.

An efficient, fast, and robust algorithm for single diode model parameters estimation of photovoltaic solar cells

AbstractParameter estimation of photovoltaic (PV) solar cells and module models pays attention to researchers owing to their importance in practical considerations. The single diode model (SDM) circuit with five unknown parameters is widely used to model PV solar cells and modules. In this paper, a novel approach called alternate optimization (AO) algorithm based on a discrete search is proposed to estimate the SDM parameters. The proposed algorithm provides efficient and robust performance, considering a limited set of discrete values and increasing the convergence speed. Two practical case studies with actual measurements are considered to assess the proposed AO algorithm: the RTC France solar cell and monocrystalline PV modules with different irradiations and temperatures. The numerical findings underscore the superior performance of the proposed AO algorithm across various metrics. Notably, it achieves an exceptional Root Mean Square Error (RMSE) of 7.7426 × 10−04 for the RTC France PV cell and approximately 1 × 10−03 RMSE for monocrystalline PV modules. Additionally, the algorithm exhibits unparalleled speed, showcasing the fastest convergence with an elapsed time of 1.66 × 10−05—markedly 4.45 times quicker than the fastest method documented in the literature for SDM parameter estimation. Furthermore, the proposed AO algorithm stands out for its efficiency, requiring a maximum of five iterations for parameter estimation, a substantial improvement compared to the more than 10 iterations typically needed by algorithms in the existing literature. Its robustness is also commendable, as evidenced by the stability of final RMSE values across a variety of experiments, distinguishing it from less robust algorithms found in the literature.

Data quality processing for photovoltaic system measurements

The operation and maintenance activities in photovoltaic systems use meteorological and electrical measurements that must be reliable to check system performance. The International Electrotechnical Commission (IEC) standards have established general criteria to filter erroneous information; however, there is no standardized process for the evaluation of measurements. In the present work we developed 3 procedures to detect and correct measurements of a photovoltaic system based on the single diode model. The performance evaluation of each criterion was tested with 6 groups of experimental measurements from a 3 kWp installation. Based on the error of the 3 procedures performed, the most unfavorable case has been prioritized. Then, the reduction of errors between the estimated and measured value has been achieved, reducing the number of measurements to be corrected. For the clear sky categories, the coefficient of determination is 0.9975 and 0.9961 for the high irradiance profile. Although an increase of 2.5% for coefficient of determination has been achieved, the overcast sky categories should be analyzed in more detail. Finally, the different causes of measurement error should be analyzed, associated with calibration errors and sensor quality.

Performance analysis and effective modeling of a solar photovoltaic module based on field tests

Abstract In recent years, the demand for photovoltaic (PV) energy has increased parallel to scientific research on PV cells, including electrical modeling, characterization, and extraction of unknown parameters. Moreover, our main contribution in this paper focuses on experimental investigation of the effect of solar radiation and temperature on the performance of a small photovoltaic plant years after its installation in the Ouargla region. As in many parts of the world, this studied area has experienced rising temperatures due to climate change, affecting system outputs. Therefore, in the first phase, we offer to characterize PV modules in various conditions and analyze the electrical parameters’ performance. The results indicate solar radiation and temperature influence PV modules’ electrical parameters. Moreover, the temperature influences the open-circuit voltage, while solar radiation positively impacts the short-circuit current. On the other side, we determine the optimal parameters of these modules and develop an accurate PV model using the bald eagle search algorithm (BES), gradian-based optimizer algorithm (GBO), and whale optimization Algorithm (WOA) based on a single diode model. The achievements show that the BES and GBO algorithms give good results for the optimum estimation of the PV model compared to WOA, where the best fitness was recorded at 0.015608 with the lowest deviation of 0.012565 and 0.039588, respectively. However, the BES has generated the minimum error values and with minimum iteration number, which indicates that this technique is more stable and robust for PV module parameter extraction.

Extraction of single diode PV cell/module model parameters using a hybrid BMO approach with Lambert's W function

The accurate estimation of photovoltaic (PV) system parameters is crucial for effective simulation. Evaluation, modelling, and control of solar energy systems. The performance of photovoltaic installations is significantly influenced by the variability of model parameters and the difficulties in accessing them. As a result, the continuous objective is to consistently strive to search for and identify these parameters. This study proposes a hybrid algorithm that combines Bird Mating Optimiser algorithm with Lambert W-function (LBMO) with Wang's analytical method called (WLBMO) to optimise the parameters of the single diode model (SDM). The algorithm utilises a fast and precise iterative method for extracting I0 and Iph , and a Bird Mating Optimiser with Lambert W-function for extracting α, Rsh and Rs . The effectiveness of the proposed method is evaluated on the RTC solar cell and three commercial photovoltaic models. The specific results demonstrate the superior performance of our proposed WLBMO algorithm compared to known alternatives. For the R.T.C. France solar cell's single diode model, WLBMO achieves the smallest RMSE of 9.8630E-04, and similarly for the PWP201 model, yielding a low RMSE of 2.3845E-03. Comparative analysis, including Wang and LBMO, further validates our approach. Additionally, WLBMO excels among other methods for the STM6-120/36 commercial PV module, with a lowest RMSE of 1.7599E-03 and an exceptional minimum power error of 3.2270E-05. Similarly, for the KC200GT model, our method produces the smallest RMSE of 4.7569E-03. These consistent findings affirm the accuracy and efficacy of our WLBMO algorithm for PV model parameter estimation, emphasising its capacity to achieve significant error rate reductions of 92.856%, 1.147%, 49.732%, and 89.221% for the R.T.C France solar cell, Photowatt-PWP201, STM6-40/36, and KC200GT modules, respectively, and highlighting its pivotal role in optimising solutions.

Multiple-carrier-lifetime model for carrier dynamics in InGaN/GaN LEDs with a non-uniform carrier distribution

We introduce a multiple-carrier-lifetime model (MCLM) for light-emitting diodes (LEDs) with non-uniform carrier distribution, such as in multiple-quantum-well (MQW) structures. By employing the MCLM, we successfully explain the modulation response of V-pit engineered MQW LEDs, which exhibit an S21 roll-off slower than −20 dB/decade. Using the proposed model and employing a gradient descent method, we extract effective recombination and escape lifetimes by averaging the carrier behavior across the quantum wells. Our results reveal slower effective carrier recombination and escape in MQW LEDs compared with LEDs emitting from a single QW, indicating the advantages of lower carrier density achieved through V-pit engineering. Notably, the effective carrier recombination time is more than one order of magnitude lower than the effective escape lifetime, suggesting that most carriers in the quantum wells recombine, while the escape process remains weak. To ensure the reliability and robustness of the MCLM, we subject it to a comprehensive threefold validation process. This work confirms the positive impact of spreading carriers into several quantum wells through V-pit engineering. In addition, the MCLM is applicable to other LEDs with a non-uniform carrier distribution, such as micro-LEDs with significant surface recombination and non-uniform lateral carrier profiles.

Archimedes optimization algorithm based parameter extraction of photovoltaic models on a decent basis for novel accurate RMSE calculation

Solar photovoltaic (PV) technology stands as a promising alternative to conventional fossil fuel-based power generation, offering pollution-free and low-maintenance energy production. To harness its potential effectively, understanding the power generation process and accurately modeling solar PV systems are essential. Unfortunately, manufacturers often do not provide the necessary parameters for modeling solar cells, making it challenging for researchers. This research employs the Archimedes Optimization Algorithm (AOA), an optimization technique, to determine unknown parameters for the PVM752 GaAs thin film solar cell and the RTC France solar cell. The modeling of these solar cells utilizes both a Single Diode Model (SDM) and a Double Diode Model (DDM). Performance evaluations are conducted using the sum of individual absolute errors (SIAE) and a novel root mean square error (RMSE) method. Comparing the effectiveness of the AOA with other optimization methods, The RMSEs for the AOA applied to the SDM and DDM of RTC France solar cell were 3.7415 × 10–3 and 1.0033 × 10–3. Similarly, for PVM752 GaAs thin film solar cell were 1.6564 × 10–3, and 0.00106365, respectively. The SIAE values for both solar diode models of RTC France cells were 0.071845 and 0.021268, respectively. For the PVM752 GaAs thin film, the corresponding SIAE values were 0.031488 and 0.040224. The results highlight the efficiency of the AOA-based approach, showcasing consistent convergence and a high level of accuracy in obtained solutions. The suggested approach produces superior results with a lower RMSE compared to other algorithms, demonstrating its efficacy in determining solar PV parameters for modeling purposes.

Using Recycled Tetrapak and Doped Titanyl/Vanadyl Phthalocyanine to Make Solid-State Devices.

In this work we studied the semiconductor behavior of titanyl phthalocyanine (TiOPc) and vanadyl phthalocyanine (VOPc), doped with anthraflavic acid and deposited on Tetrapak/graphite as flexible electrodes. The molecular structure was approached using the density functional theory and astonishingly, it was found that the structure and electronic behavior can change depending on the metal in the phthalocyanine. Experimentally, the Root Mean Square was found to be 124 and 151 nm for the VOPc-Anthraflavine and TiOPc-Anthraflavine films, respectively, and the maximum stress was 8.58 MPa for the film with VOPc. The TiOPc-Anthraflavine film presents the smallest fundamental gap of 1.81 eV and 1.98 eV for indirect and direct transitions, respectively. Finally, the solid-state devices were fabricated, and the electrical properties were examined. The tests showed that the current-voltage curves of the devices on Tetrapak and VOPc-Anthraflavine on a rigid substrate exhibit the same current saturation behavior at 10 mA, which is achieved for different voltage values. Since the current-voltage curves of the TiOPc-Anthraflavine on a rigid substrate presents a defined diode model behavior, it was approximated by nonlinear least squares, and it has been determined that the threshold voltage of the sample for the different lighting conditions is between 0.6 and 0.8 volts.

Hybrid adaptive dwarf mongoose optimization with whale optimization algorithm for extracting photovoltaic parameters

<abstract> <p>This article proposed adaptive hybrid dwarf mongoose optimization (DMO) with whale optimization algorithm (DMOWOA) to extract solar cell model parameters. In DMOWOA, the whale optimization algorithm (WOA) is used to enhance the capability of DMO in escaping local optima, while introducing inertial weights to achieve a balance between exploration and exploitation. The DMOWOA performances are tested through the solving of the single diode model, double diode model, and photovoltaic (PV) modules. Finally, the DMOWOA is compared with six well-known algorithms and other optimization methods. The experimental results demonstrate that the proposed DMOWOA exhibits remarkable competitiveness in convergence speed, robustness, and accuracy.</p> </abstract>