Diode Model Research Articles

Nonlinear behavioral analysis of diode model using Volterra series

Nonlinear behavioral analysis of diode model using Volterra series

Application of KBaYSi2O7:Bi3+,Eu3+ Phosphor for White Light-Emitting Diodes with Excellent Color Quality

This paper examines the properties of two phosphor materials synthesized via utilizing the sol gel method: KBaYSi2O7:Bi3+ (KBYS:Bi) phosphor providing cyan/deep-blue emission and KBaYSi2O7:Bi3+,Eu3+ (KBYS:Bi,Eu) phosphor exhibiting tunable emission from near-UV to red. The optimal doping concentrations for Bi3+ and Eu3+ are 0.2% and 3.5%, respectively. It is found that the ability to give discrepant emission peaks under different excitation sources of the KBYS:Bi phosphor is attributed to the occupancy of Bi3+ in different cation hosts. Meanwhile, co doping the Eu3+ and Bi3+ into the KBYS host leads to red and cyan emission regions, enabling the emission tunability of the KBYS:Bi,Eu phosphor. KBYS:Bi,Eu phosphor was then used in combination with YAG:Ce3+ and blue chips to fabricate a white light emitting diode (LED) model. The particle sizes of KBYS:Bi,Eu phosphor are adjusted to examine its influences on the LED properties. With increasing particle sizes (≥12 µm), the KBYS:Bi,Eu phosphor can improve the scatter efficacy, transmission power, lumen output, and color performance (rendition and uniformity). Both KBYS:Bi and KBYS:Bi,Eu phosphors are promising luminescent phosphors that can be combined with other phosphor with different emission colors to obtain the full-spectrum or tunable white light for LEDs.

Parameter Estimation and Preliminary Fault Diagnosis for Photovoltaic Modules Using a Three-Diode Model

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

Enhanced Method for Estimating Unknown Parameters in Single Diode Model for Solar Photovoltaic Cells

Three models of equivalent circuit are frequently applied for modelling solar cells/modules: single, double and three diode models. Among these, the single diode (SD) model is currently the most widely applied for solar cell modelling. This model is characterized by five parameters, which can be estimated using various methods. In the present work, the unknown parameters of this model are determined using an improved hybrid method. Our research introduces an innovative approach for this purpose. Our method formulates and resolves the equations defining these critical parameters. Particularly, our main objective revolves around estimating the diode's saturation current (I0), series resistance, photocurrent (Iph) and parallel resistance (Rsh), ideality factor ( through these equations. We confirmed our method reliability by thoroughly simulating the I-V curves of six PV cells, showing the credibility and the precision of our approach. This research significantly advances solar cell modelling by offering an improved technique for estimating crucial parameters within the SD model, enhancing the accuracy and reliability of solar cell performance predictions.

Diode Equation for Sandwich-Type Thin-Film Photovoltaic Devices Limited by Bimolecular Recombination

Analytical diode models that relate the current to key material parameters are invaluable for understanding the device physics in photovoltaic (PV) devices. However, a diode model that accurately describes the current in thin-film PVs based on low-mobility semiconductors (including organic solar cells) limited by bimolecular recombination has been lacking. Previous models neglect effects of injected charge carriers, which are the predominant source for bimolecular recombination in thin-film PV systems with Ohmic contacts. In this work, we derive a unified diode equation for thin-film PVs based on undoped semiconductors limited by bimolecular recombination. Based on a regional approximation approach, we derive an analytical model for the current accounting for the interplay between charge-carrier extraction, injection, and bimolecular recombination. The analytical model is validated by numerical simulations and tested experimentally on organic solar cells. Our findings provide key insights into the mechanisms driving and limiting charge collection, and ultimately the power-conversion efficiency, in low-mobility PV devices. The presented framework is material agnostic and generally applicable to sandwich-type thin-film PV devices, including photodiodes and indoor light-harvesting cells. Published by the American Physical Society 2024

Predictive energy control for grid-connected industrial PV-battery systems using GEP-ANFIS

Rising energy costs in Uganda's industrial sector have significantly increased manufacturing expenses and led to the closure of potential enterprises, underscoring the urgent need for efficient energy management solutions. The study presents an optimization-based predictive energy control model that integrates Gene Expression Programming (GEP) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) for grid-connected solar PV-battery systems. The hybrid GEP-ANFIS model leverages GEP's strength in complex pattern recognition and ANFIS's adaptive learning capability to manage nonlinear data, resulting in more accurate and reliable energy predictions. The model utilizes historical load patterns, grid data, and solar PV/battery inputs to forecast load demand and solar power generation, optimizing energy usage based on Time of Use (TOU) pricing. The approach addresses the limitations of existing energy management techniques by enhancing predictive accuracy and ensuring seamless integration with industrial applications. The present work demonstrates that the proposed GEP-ANFIS double diode model consistently outperforms its counterparts, achieving a mean absolute percentage error (MAPE) of 7.25 %, a mean absolute deviation (MAD) of 2.95 %, and a root mean square error (RMSE) of 8.42 %. Moreover, the model exhibits superior accuracy in predicting short-term load demand, with a MAPE of 2.24 %, a MAD of 0.13 %, and an RMSE of 0.18 %. Additionally, the model results in an average energy cost reduction of 6.7 % for hybrid grid-connected solar PV/battery configurations, highlighting its economic benefits. The study's key contributions include the creation of a novel hybrid predictive model, significant cost savings, improved load prediction accuracy, and the integration of renewable energy sources into industrial energy management. The model's inferences can effectively address the challenges of rising energy costs, providing a robust framework for sustainable and efficient energy management in industrial settings.

An intelligent aortic valve model for complete cardiac cycle.

The aortic valve (AV) is crucial for cardiovascular (CV) hemodynamic, impacting cardiac output (CO) and left ventricular volumetric flow rate (LVQ). Its nonlinear behavior challenges standard LVQ prediction methods as well as CO one. This study presents a novel approach for modeling the AV in the CV system, offering an improved method for estimating crucial parameters like LVQ across various AV conditions, including aortic stenosis (AS). The model, based on AV channel length during the entire cardiac phase, introduces a time-varying AV resistance (TV-AVR) parameterized by the pressure ratio across the AV and LVQ, enabling the simulation of both healthy and AS-related conditions. To validate this model, in vitro measurements are compared using a hybrid mock circulatory loop device. An unconventional use of a convolutional neural network (CNN) corrects the model's estimates, eliminating the need for labeled datasets. This approach, incorporating real-time learning and transforming 1-D CV signals into 2-D tensors, significantly improves the accuracy of LVQ measurements, achieving an error rate of less than 3.41 ± 4.84% for CO in healthy conditions and 2.83 ± 1.35% in AS cases-a 33.13% enhancement over linear diode models. These results underscore the potential of this approach for enhancing the diagnosis, prediction, and treatment of AV diseases. The key contributions of the proposed method encompass nonlinear TV-AVR estimation, investigation of transient CV responses, prediction of instantaneous CO, development of a flexible framework for noninvasive measurements integration, and the introduction of an adjustable resistance model using an extended Kalman filter (EKF) and CNN combination, all without requiring labeled data.

A self-started predictor–corrector method for calculating the Lambert W function within the scope of the photovoltaic single diode model

The single diode model is one of the most employed to represent the behavior of photovoltaic generators. Nonetheless, the transcendental nature of its characteristic equation requires that some mathematical approach be specifically adopted to allow the determination of its solution. In recent years, the approach via the Lambert W function has gained more and more popularity; however, the calculation of this special function still faces performance issues. That said, the present paper proposes a self-started predictor–corrector method for calculating the Lambert W function in the interval of interest of the single diode model. This method was properly designed to return high-quality solutions at a low and fixed computational cost. In this sense, the moderate complexity of its four equations stands out, as well as its non-conditional and non-iterative nature. The results obtained for appropriate case studies ascertain its elevated level of accuracy. Also, they demonstrate that it is more effective and faster than other state-of-the-art alternatives. Therefore, the paper’s major contribution is this novel method, which proves to be reliable, easy to implement, and useful for making the employment of the single diode model in studies more efficient.

Observer-based security control for state-dependent fractional-order system with multiple attacks via polytope-type approach and application to tunnel diode model

This work examines a polytope-type approach for observer-based security control of a fractional-order system that is under multiple attacks via an event-triggered scheme. A novel multiple-attack model is first proposed for fractional-order systems such as denial-of-service attacks and reply attacks, which can destroy the system's security during the signal process. In contrast to previous studies, the design of the controller is developed using an observer-based event-triggered scheme in the presence of multiple attacks. An event-triggering strategy is proposed to reduce the communication burden of the output measurement signals. An observer-based control is designed to keep the fractional-order system stable in all scenarios where one or more simultaneous attacks may occur. These findings can ensure the absence of Zeno behaviour and considerably reduce data transmissions. By employing the suitable Lyapunov–Krasovskii functional, sufficient conditions are derived to ensure fractional-order system stability. Controller and observer-gain matrices are obtained by using the linear matrix inequality technique. Finally, two examples are provided to demonstrate the feasibility and usefulness of the suggested methodology.

The electrical transport property and gas detection behaviour of polyvinyl alcohol and γ-Fe2O3 nanocomposite film

This paper presents the electrical and gas detection behaviour of polyvinyl alcohol (PVA)/γ-Fe2O3 nanocomposite films. The physical characterization includes X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), and UV–Visible spectroscopy. The analysis of both the dc resistivity and ac conductivity using Correlated Barrier Hoping (CBH) conduction process shows that the conductivity enhances with the decreasing of activation energy in case of the PVA/γ-Fe2O3 nanocomposite film which is the indication of interfacial defect states. The current voltage study at various temperatures follows the thermoionic emission model of Schottky Barrier diode. Moreover, the dielectric constant enhances for PVA/γ-Fe2O3 nanocomposite film, which indicates homogeneous distribution of γ-Fe2O3 nanoparticles within PVA matrix and the aggregation of trapped charges found in interface develops an interfacial polarization. As a result, the gas adsorption capacity of the nanocomposite film increases which improves the signal-to-noise ratio, leading to higher gas detection sensitivity.

Adaptive Particle Swarm Optimization based improved modeling of Solar Photovoltaic module for parameter determination

Solar Photovoltaic (SPV) panel manufacturers provide a datasheet, which contains key electrical specifications of the SPV module for the user's reference. However, these data alone prove inadequate in anticipating the performance of PV systems under fluctuating atmospheric conditions. The approach put forth in this paper ascertains five parameters of a PV module using information supplied by the manufacturer. This approach considers the impact of solar radiation and cell temperature. The paper outlines the process for determining the parameters of the five-parameter model and establishes a comparison between projected current-voltage curves and manufacturer-provided data. To extract the unknown parameters of a single diode model Adaptive Particle Swarm Optimization (APSO) techniques with barrier constraints was developed. The study investigates both multi-crystalline and mono-crystalline PV module technologies to validate the efficacy of the proposed method. Results demonstrate that the proposed technique, besides being straightforward, proves adept at accurately simulating the dependable performances of PV modules. Comparative analysis reveals that the proposed methods extract parameters with minimal modeling errors and high precision, regardless of temperature fluctuations, outperforming conventional PSO methods.

A coarse-grained Poisson–Nernst–Planck model for polyelectrolyte-modified nanofluidic diodes

Abstract Polyelectrolyte (PE)-modified synthetic nanopores have gained substantial research attention because molecular modification promotes ion gating and rectification. However, theoretical research on PE-modified nanopores is relatively scarce because it is difficult to establish an elaborate model for PEs, and it accordingly causes a trade-off between the computational resources needed and the accuracy. Therefore, an appropriate simulation method for the PE-modified nanopore is in high demand and still an enormous challenge. Herein, we report the simulation result of ion transport through PE-modified nanopores through a coarse-grained Poisson–Nernst–Planck method. By modeling the stuffed PE molecules as PE particles in a well-established continuum model, adequate computational accuracy can be achieved with acceptable computational cost. Based on this model, we study the ion transport in PE-modified nanofluidic diodes and reveal the PE around ion selectivity, which can explain the previous experimental works. Intriguingly, we found that the ion enrichment state in the nanofluidic diode is sensitive to steric hindrance and charge distribution near the heterojunction region. This property is critical for the ion transport behavior in the PE-modified nanofluidic diodes. Based on this property, we predict a heterogeneous structure that can realize the single molecule response to charged analytes. These findings provide insights for understanding the ion transport in PE-modified nanofluidic systems and bring inspiration to the design and optimization of high-performance chemical sensors.

Electron beam response corrections for an ultra-high-dose-rate capable diode dosimeter.

Ultra-high-dose-rate (UHDR) electron beams have been commonly utilized in FLASH studies and the translation of FLASH Radiotherapy (RT) to the clinic. The EDGE diode detector has potential use for UHDR dosimetry albeit with a beam energy dependency observed. The purpose is to present the electron beam response for an EDGE detector in dependence on beam energy, to characterize the EDGE detector's response under UHDR conditions, and to validate correction factors derived from the first detailed Monte Carlo model of the EDGE diode against measurements, particularly under UHDR conditions. Percentage depth doses (PDDs) for the UHDR Mobetron were measured with both EDGE detectors and films. A detailed Monte Carlo (MC) model of the EDGE detector has been configured according to the blueprint provided by the manufacturer under an NDA agreement. Water/silicon dose ratios of EDGE detector for a series of mono-energetic electron beams have been calculated. The dependence of the water/silicon dose ratio on depth for a FLASH relevant electron beam was also studied. An analytical approach for the correction of PDD measured with EDGE detectors was established. Water/silicon dose ratio decreased with decreasing electron beam energy. For the Mobetron 9 MeV UHDR electron beam, the ratio decreased from 1.09 to 1.03 in the build-up region, maintained in range of 0.98-1.02 at the fall-off region and raised to a plateau in value of 1.08 at the tail. By applying the corrections, good agreement between the PDDs measured by the EDGE detector and those measured with film was achieved. Electron beam response of an UHDR capable EDGE detector was derived from first principles utilizing a sophisticated MC model. An analytical approach was validated for the PDDs of UHDR electron beams. The results demonstrated the capability of EDGE detector in measuring PDDs of UHDR electron beams.

Special Trans Function based exact expressions for the double and triple diode models of solar cells: Superior fitness, accuracy and convergence

Inspired by the fact that the Special Trans Function (STF) is more accurate than Lambert W function (LWF), this paper rigorously derives two types of STF-based exact expressions for the double-diode model (DDM) and triple-diode model (TDM) of solar cells. The former involves multi-STF (mSTF), while the latter contains single-STF (sSTF), bridging the remaining gap in describing the nonlinear I–V characteristic of solar cells using STF. The proposed mSTF and sSTF are closely linked with but different from the multi-Lambert W function (mLWF), single-LWF (sLWF) and implicit exponential function (IEF) based expressions, particularly in terms of fitness and parameter extraction. Through a fitness test involving 203 cases in the space of STF branch x∈R+, it was found that under the same parameter values, the proposed mSTF consistently outperforms in fitting the measured I–V data of various solar cell/modules, followed by the proposed sSTF, mLWF, sLWF, and finally IEF-based expression. Notably, all optimal STF branch x fall within the interval of x∈[0,6] rather than the preset x∈[0,20]. A normalized trust-region-reflective (NTRR) algorithm and a V-shaped selection strategy are developed to improve the parameter extraction of proposed mSTF and sSTF. Results from the derivative-dependent V-shaped NTRR algorithm and population-based Rcr-IJADE algorithm indicate that the proposed sSTF consistently achieves the highest accuracy and fastest convergence speed, followed by the mSTF, sLWF, mLWF and IEF-based expression. Moreover, the proposed sSTF exhibits fewer degeneracies from TDM to DDM compared to other expressions. Given these advantages, the proposed mSTF and sSTF show great promise for PV simulation and therefore deserve serious attention.

Quantum annealing-aided design of an ultrathin-metamaterial optical diode

Thin-film optical diodes are important elements for miniaturizing photonic systems. However, the design of optical diodes relies on empirical and heuristic approaches. This poses a significant challenge for identifying optimal structural models of optical diodes at given wavelengths. Here, we leverage a quantum annealing-enhanced active learning scheme to automatically identify optimal designs of 130 nm-thick optical diodes. An optical diode is a stratified volume diffractive film discretized into rectangular pixels, where each pixel is assigned to either a metal or dielectric. The proposed scheme identifies the optimal material states of each pixel, maximizing the quality of optical isolation at given wavelengths. Consequently, we successfully identify optimal structures at three specific wavelengths (600, 800, and 1000 nm). In the best-case scenario, when the forward transmissivity is 85%, the backward transmissivity is 0.1%. Electromagnetic field profiles reveal that the designed diode strongly supports surface plasmons coupled across counterintuitive metal–dielectric pixel arrays. Thereby, it yields the transmission of first-order diffracted light with a high amplitude. In contrast, backward transmission has decoupled surface plasmons that redirect Poynting vectors back to the incident medium, resulting in near attenuation of its transmission. In addition, we experimentally verify the optical isolation function of the optical diode.

Parameters extraction of photovoltaic models using enhanced generalized normal distribution optimization with neighborhood search

The photovoltaic system has been widely integrated into electrical power grids to produce clean and sustainable energy sources. Precisely modeling of PV systems is crucial to simulate and asset the performance of such power system. Modeling of PV system is a challenge because the characteristic curve of current and voltage is nonlinear and has unknown parameters due to insufficient data points in manufacture’s data sheet. This work proposes generalized normal distribution optimization based on neighborhood search strategies (NSGNDO) to extract the parameter of single diode model (SDM), double diode model (DDM), and PV module model (PVM). The root means square error (RMSE) is used as a performance indicator. Two commercial PV models like RTC France solar cell and PWP201 are used to validate the ability of NSGNDO to precisely estimated the PV system’s parameters. The results show the superiority of NSGNDO over competitive optimization methods and can reduce the RMSE to 2.05296E-03 for PWP201 and to 9.8248E-04 for RTC France solar cell which prove that NSGNDO can be used as competitor method to identify the parameters of PV solar system. The statistical analysis shows the robustness of NSGNDO through statistical measurements and Wilcoxon rank test.’

Approaches for Maximizing Maximum Power Point Tracking and Evaluating Operational Constraints in Photovoltaic Systems

Abstract: Optimization techniques have broad utility in multiple engineering fields, including activities such as modeling, system identification, parameter estimation, forecasting, and control of intricate systems. This innovation emphasizes the use of optimization approaches to control the performance of Photo-voltaic (PV) array systems. The main difficulty lies in the nonlinear correlation between current and voltage (I-V) characteristics of PV arrays, resulting in variations in the location of the Maximum Power Point under varied climatic circumstances. It is crucial to track this Maximum electricity Point efficiently in order to collect the highest amount of electricity possible from the photovoltaic devices. Maximum Power Point Tracking is a tracking procedure that optimizes power generation by dynamically altering the operating voltage to coincide with the MPP. In order to tackle this difficulty, the paper suggests a unique two-stage Maximum Power Point Tracking technique. The initial phase applies the Weightless Swarm Algorithm to evaluate the Maximum Power Point in an offline manner. The subsequent phase employs an online Adaptive Perturb & Observe technique to continuously track the MPP in real-time. In addition, an Approximate Single Diode Model is created to quickly assess the output power. In order to verify the suggested method, computer simulations are performed utilizing a Maximum Power Point Tracking system that is combined with a Single-Ended PrimaryInductor Converter

Fraction order particle swarm optimization for parameter extraction of triple-diode photovoltaic models

This work proposes an application of Fractional Order Particle Swarm Optimization (FO-PSO), a meta-heuristic method for parameters estimation of photo-voltaic (PV) module as a non-linear, transcendental, multi-modal and implicit optimization problem. The uses single diode model (SDM), double diode model (DDM) and three-diode model (TDM) of PV modules with the constraint that only data-sheet information may be utilized. A fitness function based on the error amongst the computed values of current and voltage and the ones given in characteristic I-V curves of data-sheet, is minimized using the FO-PSO to get the required parameters. The comparative study between the estimated and data-sheet values provided by PV module manufacturers will determine the effectiveness of this research. The effectiveness of the FO-PSO is demonstrated by comparing the fitness values with that of other techniques. The FO-PSO technique makes a novel contribution to the PV power systems industry by making it possible to obtain a nearly realistic model of any commercial PV module. The effectiveness of the FO-PSO is determined by comparing the results for all the three models with the state of the art optimization techniques. The Root Mean Square error values calculated for the TDM is less than 10e–16, producing very consistent FO-PSO results. Therefore, FO-PSO is anticipated to be a competitive method for obtaining PV module specifications.

Numerical characterization of solar radiation applied to a simplified five parameters diode model of a photovoltaic module in the city of Ngaoundere

Numerical characterization of solar radiation applied to a simplified five parameters diode model of a photovoltaic module in the city of Ngaoundere

A new recursive method based on datasheet information to calculate the double diode model parameters of commercially available photovoltaic modules

In this study, a new method is introduced to compute the double-diode model parameters of commercially available photovoltaic modules. The new method has been based solely on datasheet-supplied electrical data. The new method offers two novelties. First, the dark reverse saturation currents are calculated analytically. Second, the parameters of the double-diode model are calculated by a new recursive procedure using tuning parameters. The validity of the new method is tested for twenty commercially photovoltaic modules with different technologies via the normalized root mean square error tool by comparing the datasheet current-voltage curves with simulated curves. In addition, the proposed method is compared with other methods at different operating conditions and also tested in the field under actual operating conditions using the root mean square error tool. It was founded that the new method calculates the parameters of the double diode model and simulates the current-voltage curves of commercially photovoltaic modules under various operating conditions with a high accuracy. Moreover, the new method is also very competitive with the selected methods and even achieves better simulation accuracy with a very low error level. The obtained results show that the new method is highly accurate, practical, and simple for all photovoltaic users.