Search Results (1,246)

Photovoltaic power conversion systems require maximum power point tracking (MPPT) strategies capable of fast dynamic response with low computational burden, while remaining reliable under variable environmental conditions. While neural-network-based methods have been widely investigated, their practical deployment is often limited by the availability of representative training data and by the gap between offline algorithm development and real-time converter-level validation. This paper presents a reproducible hardware-in-the-loop workflow for the development and assessment of a lightweight neural MPPT controller applied to a photovoltaic system with a two-phase interleaved boost converter. The proposed approach generates a large synthetic training dataset using the single-diode photovoltaic model, leveraging only measurable quantities (PV voltage, PV current, and module temperature) as neural network inputs. The trained network estimates the voltage and current corresponding to the maximum power point, while a proportional-integral controller drives the converter toward the predicted operating point. The trained network is deployed on an STM32 microcontroller interfaced with the Typhoon HIL platform, allowing its real-time behavior to be tested against the emulated system. The measured neural MPPT execution time on the microcontroller is around 65 $\mathsf{\mu }$s, with an overall CPU occupancy of nearly 4%, considering the PI controller stage. The implemented setup reproduces the photovoltaic generator, converter dynamics, switching behavior, and realistic irradiance and temperature profiles under repeatable real-time conditions. The interleaved boost architecture also reduces input current ripple and distributes current stress, making the setup suitable for medium-power photovoltaic applications. The main contribution of this work lies in the integrated modeling, training, control, and hardware-in-the-loop validation procedure, supporting the implementation of neural MPPT strategies. Full article

Existing parameter extraction methods for photovoltaic modules are mostly developed for specific module types or structural configurations, making it difficult to achieve unified modeling across different materials, structures, and operating conditions. To address this issue, this study proposes a unified five-parameter identification method based on the single-diode model of photovoltaic cells and combines it with a genetic algorithm. The photocurrent, reverse saturation current, series resistance, shunt resistance, and diode ideality factor are selected as the identification parameters, and the parameter extraction problem is formulated as a global optimization problem. On this basis, a cell-unit-based parameter correction model considering the effects of irradiance and temperature is established, and unified modeling of photovoltaic modules with different architectures is achieved according to their internal electrical connections, enabling the prediction of their electrical characteristics. The proposed method is validated using experimental data from a half-cell mono-crystalline silicon module under different shading conditions, as well as mono-crystalline silicon, multi-crystalline silicon, and thin-film modules under varying irradiance and temperature conditions. In addition, the RTC France solar cell and Photowatt-PWP201 module benchmark datasets are employed to further assess the reliability of the parameter identification procedure. The results demonstrate that the proposed method can effectively reproduce the electrical characteristics of the investigated photovoltaic modules, with maximum relative errors of 2.48%, 2.17%, and 4.32% for open-circuit voltage, short-circuit current, and maximum power, respectively. The benchmark validation further demonstrates that the proposed method achieves fitting accuracy comparable to that of other representative optimization algorithms while exhibiting good repeatability and convergence performance. These results collectively demonstrate the feasibility of the proposed cell-unit-based five-parameter modeling framework for photovoltaic modules with different materials and structures under the investigated operating conditions, providing a simple and feasible approach for unified parameter identification, performance characterization, and engineering modeling. Full article

This work presents a numerical investigation of an asymmetric Pt/HfSiON/Ti metal–insulator–metal (MIM) tunnel diode for long-wave infrared (LWIR) rectenna applications at 28.3 THz (10.6 μm). HfSiON is investigated as the tunneling dielectric owing to its favorable electronic properties, thermal stability, and compatibility with nanoscale device fabrication. The electrical transport and rectification characteristics are evaluated using the full Simmons quantum-mechanical tunneling model implemented in MATLAB release 2025b. The analysis encompasses the current density–voltage (J–V) and current–voltage (I–V) characteristics, zero-bias dynamic resistance, current asymmetry, nonlinearity, responsivity, and temperature dependence. Under AC excitation, the Pt/HfSiON/Ti diode exhibits a calculated rectified current density of 1.78 × 10² A/cm² at zero DC bias, while a current density of 6.32 × 10⁵ A/cm² is obtained at an applied voltage amplitude of ±0.5 V. The asymmetric electrode configuration, arising from the difference in the work functions of Pt and Ti, results in a calculated asymmetry of 2.5 × 10⁴. At zero DC bias, the diode exhibits a zero-bias dynamic resistance of 3.85 × 10⁵ Ω and a zero-bias responsivity of approximately 10 V⁻¹. The calculated rectification characteristics show only weak sensitivity to temperature over the investigated range, indicating that the transport response is predominantly governed by quantum-mechanical tunneling rather than thermally activated processes. These results demonstrate the potential of HfSiON as a tunneling dielectric for nanoscale MIM rectifiers and indicate that the asymmetric Pt/HfSiON/Ti architecture provides strong nonlinear rectification and favorable zero-bias response for LWIR rectenna and energy-harvesting applications. Full article

This article presents a multi-objective optimization framework for fluorescent organic light-emitting diodes (OLEDs), combining numerical simulation with an analysis of exciton populations and the associated loss mechanisms. An ITO/NPB/Alq₃:C545T/Alq₃/LiF–Al structure was modeled, calibrated, and validated against experimental electro-optical characteristics. The influence of the emissive layer thickness ($t_{\mathrm{EML}}$) and the C545T doping concentration ($D_p$) was systematically studied across 25 configurations, with $t_{\mathrm{EML}}$= 20–40 nm and $D_p=1$–$9\%$, under two operating conditions: $J=0.15$ A/cm² and $L=5000$ cd/m². Increasing the dopant concentration led to a marked deterioration in current efficiency (${\eta }_c$) and power efficiency (${\eta }_p$), together with a progressive localization of singlet excitons near the interface between the emissive layer (EML) and the hole transport layer (HTL) in structures with thinner EMLs. This exciton localization was accompanied by an increased contribution from non-radiative deactivation pathways, which were incorporated into a dopant singlet-exciton loss fraction, $F_{\mathrm{loss},d}$. The systematic increase in this loss fraction with increasing dopant concentration and its overall inverse relationship with ${\eta }_c$ and ${\eta }_p$ motivated the development of a three-objective formulation that maximizes ${\eta }_c$ and ${\eta }_p$ while minimizing $F_{\mathrm{loss},d}$. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) identified compromise solutions at the lowest dopant concentration, $D_p=1\%$, with $t_{\mathrm{EML}}=24$–25 nm under both operating conditions. Multi-objective Particle Swarm Optimization (MOPSO) identified a trade-off region comparable to that obtained by NSGA-II, providing a cross-algorithm consistency check of the reported numerical results. The proposed methodology thus establishes a physically grounded link between device design, the spatial redistribution of excitons, the modeled loss pathways, and the macroscopic performance of OLEDs, thereby providing an interpretable framework for the multi-objective optimization of fluorescent OLEDs prior to fabrication. Full article

This methodological diagnostic tests whether broad building descriptors support retrofit-class shortlists beyond popularity. We mapped 2623 New York City Local Law 87 audit rows to six classes with five decision states, trained on 2019–2022, validated on 2023, and tested on unseen 2024 property groups. Under natural recorded-label visibility, state-aware and equal-information global policies both achieved an observed-positive Recall@3 of 0.832. The state model returned the same Top-3 set for 99.7% of test rows; all evaluated natural-condition neural models missed every recorded insulation and window-upgrade positive. Light-emitting diode (LED) lighting and heating, ventilation, and air conditioning (HVAC) controls comprised 277 of 394 test positives, explaining why high recall did not demonstrate personalization. In a secondary synthetic 10% visibility stress test, excluding unlabelled entries from negative supervision improved recall over naive binary cross-entropy by 0.279; the natural state–naive difference was 0.002 (95% confidence interval (CI) [−0.007, 0.012]). Post hoc capacity, ontology, status-precedence, and stopping sensitivities did not establish reliable superiority over popularity. The benchmark diagnoses label misspecification and nearly constant shortlists. Full article

This paper presents a complete modeling flow for GaN Schottky barrier diodes (SBDs), encompassing parasitic parameter de-embedding, small-signal equivalent circuit extraction, and DC characteristic analysis. The open/short de-embedding method is adopted to extract the parasitic parameters of the Ground–Signal–Ground (GSG) pads. A bias-partitioning strategy is employed to extract the intrinsic small-signal parameters, and the depletion capacitance model is used to physically fit the C-V characteristics. The forward Direct Current (DC) conduction current is described by the thermionic emission model, while the reverse leakage is modeled using a piecewise approach: the Poole–Frenkel (PF) trap-assisted emission model is applied in the low-bias region, and a double-exponential decay empirical model is introduced in the high-bias region, achieving high-precision fitting over the full bias range (−100 V~3 V). More importantly, this paper identifies a significant discrepancy between the series resistance extracted from DC measurements and that from Radio Frequency (RF) measurements, and attributes it to the frequency dispersion effect induced by trap states. The study demonstrates that combining DC and high-frequency characterization not only enables the construction of an accurate modeling framework, but also reveals the trap-related physical mechanisms within the device. Full article

To address the challenge of quantitatively identifying thermal-resistance contributions from multi-layer packaging structures within liquid-cooled insulated-gate bipolar transistor (IGBT) modules, this study establishes a three-dimensional conjugate heat-transfer model incorporating the chip, solder layer, direct-bonded copper (DBC) ceramic layer, substrate, elliptical pin-fin heat sink, and fluid domain. The IGBT and fast-recovery diode (FRD) power losses under typical motor controller operating conditions are modeled as volumetric heat sources applied to the chip region. Based on model validation, an internal thermal bottleneck evaluation method is proposed using inter-layer temperature-drop decomposition, introducing the thermal bottleneck number (BN) to quantify the temperature-drop contribution of each packaging layer along the target chip’s heat-dissipation path. Results show that in the baseline structure, the DBC ceramic layer is the critical internal thermal bottleneck, with a BN value of 11.05%. When the DBC thermal conductivity increases from 20 W/(m·K) to 80 W/(m·K), the maximum junction temperature of the IGBT decreases from 413.32 K to 396.79 K, and the BN drops from 11.05% to 3.69%. Conversely, when the DBC thickness increases from 0.20 mm to 0.50 mm, the maximum junction temperature rises from 405.45 K to 424.42 K. Further analysis reveals that after weakening the DBC thermal bottleneck, the relative temperature-drop contribution of the solder interface becomes increasingly apparent; notably, a central 20% low-conductivity defect in the solder layer raises the maximum junction temperature to 517.16 K, 103.84 K higher than under normal solder conditions. These findings provide valuable insights for identifying internal thermal bottlenecks and optimizing packaging structures in liquid-cooled IGBT modules. Full article

BOOLE is a four-face educational platform integrating analog, combinational-logic, and sequential-logic activities with optional AI-assisted component identification and datasheet support. The system was developed through requirements translation, circuit simulation, two-layer PCB design, mechanical review, fabrication, assembly, functional verification, and iterative refinement. A Raspberry Pi 5, Camera Module 3 NoIR, and touchscreen support image capture and local interaction, while an Arduino Mega provides deterministic control of the physical learning faces. Segmented power energizes only the selected face and activity, and removable boards improve maintenance and fault isolation. Hardware demonstrations reproduced the intended voltage-divider, diode threshold/polarity, counter, and sequential-logic states. Ten one-versus-rest classifiers were fine-tuned from a pretrained ViT-Base model using 2000 original photographs, with 200 images for each of ten categories. The dataset was partitioned class-wise into mutually exclusive 80/10/10 training, validation, and final-test sets before augmentation, which was applied only to training data. Final-test accuracy ranged from 91.0% to 99.5%, with precision, recall, F1-score, specificity, balanced accuracy, and confusion matrices also evaluated. A 73-student pilot produced 89–96% positive (Yes) responses across six binary survey items, providing preliminary evidence of learner-perceived effectiveness, engagement, usability, and theory-to-practice support. Overall, BOOLE demonstrates a feasible, serviceable architecture for progressive electronics education. Full article

Metal–Insulator–Metal (MIM) tunneling diodes are among the most promising rectifying devices for long-wave infrared (LWIR) rectenna systems due to their ultrafast response and zero-bias operation. However, their performance is strongly dependent on the choice of electrode and dielectric materials, making the identification of optimal material combinations a key challenge. To address this issue, this theoretical study presents a numerical investigation of a new class of MIM diodes based on a quantum-mechanical tunneling framework. Novel combinations of transition-metal dichalcogenides (NbS₂, VSe₂, and TaS₂) as anode materials (M1), conductive carbides and nitrides (Mo₂C, VN, and V) as cathode materials (M2), and rare-earth oxide and oxyhalide compounds (Sc₂O₃, LaOF, and LaOBr) as tunnel barriers (I) were selected through an extensive literature survey. These materials were combined to design previously unexplored MIM architectures for LWIR rectification. The electrical transport and rectification properties were evaluated using the Simmons tunneling model by calculating the current density–voltage (J–V) and current–voltage (I–V) characteristics, together with key figures of merit (FOMs), including zero-bias resistance, asymmetry factor, nonlinearity, and responsivity, at room temperature (300 K). The effects of tunnel barrier height and dielectric properties on device performance were systematically investigated. Among all the investigated architectures, the TaS₂/LaOBr/V MIM diode exhibited the most promising overall performance, achieving an asymmetry factor exceeding 2.5 × 10⁵, a nonlinearity factor of 1, and a zero-bias responsivity of 10 V⁻¹ at 300 K. Furthermore, this structure demonstrated the highest current density and the most favorable I–V characteristics among the proposed material combinations. These results identify the TaS₂/LaOBr/V material system as a promising candidate for high-performance LWIR energy harvesting applications, owing to its optimized tunnel barrier height, which promotes efficient electron tunneling while maintaining excellent rectification properties. Full article

Accurate parameter estimation of photovoltaic (PV) models is essential for performance evaluation, efficiency enhancement, and reliable energy forecasting in solar energy systems. However, the nonlinear, multimodal, and implicit nature of the current–voltage (I–V) relationship makes this task challenging for conventional optimization methods, which often suffer from premature convergence and sensitivity to initial conditions. In this study, a modified variant of the Artificial Circulatory System Algorithm, termed Dynamic Elite Cooperative ACSA (DEC-ACSA), is proposed for estimating the unknown parameters of the Single-Diode Model (SDM). The proposed approach extends the original ACSA by incorporating dynamic population grouping, elite-guided cooperative interaction, and directional elite refinement, thereby aiming to improve convergence stability and the utilization of high-quality population information. The objective is to minimize the residual root mean square error (RMSE) of the implicit SDM equation using measured I–V data from four established benchmarks: the RTC France solar cell and the PWP201, STM6-40/36, and STP6-120/36 PV modules. The performance of DEC-ACSA is evaluated against the original ACSA, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Henry Gas Solubility Optimization (HGSO) over 30 independent runs under an equal budget of 50,100 function evaluations. The DEC-ACSA configuration selected on RTC France was retained unchanged for the three additional module benchmarks. DEC-ACSA achieved mean residual RMSE values of 1.2514 × 10⁻³, 2.656 × 10⁻³, 2.647 × 10⁻³, and 1.8108 × 10⁻² for RTC France, PWP201, STM6-40/36, and STP6-120/36, respectively, while consistently reducing run-to-run variability relative to ACSA. Holm-corrected tests showed no significant difference from PSO on RTC France and PWP201, whereas significant differences from all comparison algorithms were observed on STM6-40/36 and STP6-120/36. I–V reconstruction further confirmed close agreement with the measured data across all four PV systems. Full article

Half-cell photovoltaic modules have been widely applied in distributed photovoltaic systems and building-integrated photovoltaic systems. Linear edge shading caused by buildings, guardrails, and adjacent modules is a key factor affecting their power generation performance. To address this problem, this paper establishes an equivalent prediction model for edge-shading of half-cell modules by combining the series–parallel topology of the module and the conduction behavior of bypass diodes. The genetic algorithm is employed to extract the model parameters, and the bisection method is used to solve the output current and obtain the corresponding I–V and P–V characteristics of the module under shading conditions. Simulation analysis is conducted by considering the edge-shading ratio and module installation orientation as variables, and the output characteristics and variation trends of PV modules under two configurations, namely long-edge shading with horizontal installation and short-edge shading with vertical installation, are investigated and compared. The results show that long-edge shading can cause current mismatch in cell strings and conduction of bypass diodes, leading to step features in the I–V curve, a multi-peak structure in the P–V curve, and rapid power attenuation. Short-edge shading only results in a linear decrease in photogenerated current, with smooth output curves and an approximately linear reduction in power along with the shading ratio, resulting in relatively smooth output characteristics. This study reveals the coupled effects of edge-shading and module installation orientation and provides a reference for evaluating shading-induced performance degradation and selecting suitable installation orientations for half-cell PV modules under the investigated shading conditions. Full article

The flexible DC transmission scheme based on modular multilevel converters (MMCs) is currently the mainstream solution for offshore wind power delivery. With the ongoing growth in both installed capacity and the offshore distance of wind power projects, the dimensions and weight of corresponding offshore converter stations have increased substantially. These developments present significant economic constraints and engineering challenges, thereby complicating the deployment of large-scale, long-distance offshore wind energy systems. Compared with MMCs, diode rectifier units (DRUs) offer advantages such as compact size, light weight, low cost, reduced operating losses, and high reliability. Nevertheless, DRUs lack active control capability, and conventional grid following wind turbines cannot independently support the voltage of the offshore AC network, which severely limits their application in offshore wind scenarios with stringent economic requirements. Grid forming control of wind turbines is an effective approach to address this issue. Given the widespread application of doubly-fed induction generators (DFIGs) in engineering practice and their relatively low capital costs, this study investigates the implementation of grid-forming control strategies in DFIGs to address the stability challenges of DRU-based HVDC transmission systems during fault conditions. First, the mathematical model of the DFIG is established. Then, a suitable control strategy is designed to endow the turbine with certain grid forming capabilities. Finally, the developed simulation model and control strategy are verified in PSCAD/EMTDC. The results demonstrate that the proposed grid forming DFIG control strategy can maintain stable offshore AC voltage and frequency under various fault conditions, ensure continuous and reliable operation of the DRU, and achieve fault ride through. On this basis, to account for engineering practicality and cost considerations, this study further proposes a hybrid transmission scheme combining grid-following and grid-forming DFIGs. Simulation results confirm that this hybrid scheme also achieves satisfactory operational performance, while reducing the potential cost increase associated with full-scale grid-forming retrofits, it effectively ensures fault ride-through capability and system operational stability. This method provides an effective solution for low cost, highly reliable offshore wind power DC transmission. Full article

Accurate modeling of photovoltaic (PV) cell behavior under varying operational conditions is essential for optimizing energy yield and system reliability. This study presents an extended simulation-based methodology for analyzing monocrystalline silicon PV cells using a double-diode model (DDM) with a physics-based, temperature- and irradiance-dependent parameterization. Building on a SPICE-equivalent circuit formulation, the governing implicit DDM equation is solved numerically to regenerate every current–voltage (I–V) and power–voltage (P–V) curve, and all circuit, block and flow diagrams are redrawn as vector-quality figures. Beyond the deterministic analysis, the manuscript introduces two extensions: (i) a quantitative statistical analysis of the influence of temperature (T), irradiance (G) and series resistance $\left(R_s\right)$ on open-circuit voltage, short-circuit current, maximum power and fill factor, using linear/log-linear regression, a multiple linear regression model and a Pearson correlation analysis; and (ii) a machine-learning (ML) validation study in which a random-forest surrogate model is trained on a 600-point physics-consistent synthetic dataset spanning the full $(T, G, R_s)$ operating envelope and evaluated with a held-out test split and 5-fold cross-validation. The surrogate reproduces the DDM outputs with cross-validated coefficients of determination above 0.98 for maximum power, open-circuit voltage, short-circuit current and fill factor, confirming that the DDM response surface is smooth, learnable and suitable for fast surrogate-based design optimization and maximum-power-point-tracking (MPPT) algorithm testing. Simulated outputs at standard test conditions (25 °C, 1000 W/m², AM 1.5) are compared against manufacturer datasheet values, and residual errors are analyzed and attributed to specific modeling assumptions. Full article

Background/Objectives: Berberis species are recognized for their ethnomedicinal importance and phytochemical richness associated with antidiabetic activity. Although B. crataegina is widely distributed in the Turkish flora and has a history of traditional use, its phytochemical properties and biological activities remain less extensively investigated than those of other Berberis species. This study aimed to comparatively evaluate the phytochemical profiles, in vitro antidiabetic potential, and cytoprotective effects of extracts prepared from different parts of B. crataegina in a high-glucose-induced SH-SY5Y cell-based model of diabetic neuropathy. Methods: Crude hydroethanolic extracts (70% ethanol) were prepared from the leaves, flowers, shoots, roots, and fresh fruits of B. crataegina collected from Kızılcahamam, Ankara, Türkiye. The total phenolic and flavonoid contents, antioxidant capacities and α-glucosidase inhibitory activities of leaf, flower, shoot, root, and fruit extracts were determined. The cytoprotective effects of the crude extracts were evaluated in a high-glucose-induced SH-SY5Y cell model. The metabolomic profiles of the extracts were comparatively analyzed using liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). The most active fruit extract was fractionated by reversed-phase vacuum liquid chromatography (RP-VLC), and the phytochemical contents and α-glucosidase inhibitory activities of the fractions were evaluated. The major phenolic compounds in the most active fruit extract were quantitatively determined using high-performance liquid chromatography–diode array detection (HPLC-DAD). The relationships between metabolites and biological activities were investigated through correlation analysis. Results: The fruit extract was distinguished by its α-glucosidase inhibitory activity, total phenolic content, antioxidant capacity, and cytoprotective effect in the high-glucose-induced SH-SY5Y cell model. It showed no marked cytotoxicity at concentrations ranging from 25 to 400 µg/mL and, at 25 µg/mL, attenuated the high-glucose-induced reduction in cell viability, restoring viability to a level close to that of the normal control. Untargeted metabolomic analysis identified 191 metabolites detected in at least two crude extracts, while the metabolites detected exclusively in the fruit extract were predominantly flavonoids and flavonoid glycosides. Fractionation showed that α-glucosidase inhibitory activity was mainly concentrated in the medium-polarity fractions; however, none of the fractions reached the activity level of the crude extract. Chlorogenic acid, rutin, caffeic acid, protocatechuic acid, quercetin, and quercetin-3-O-glucoside were detected and quantified in the fruit extract by HPLC-DAD. Correlation analysis indicated that the observed biological activities may be associated with phenolic acids, flavonoids, and other polyphenolic metabolites. Conclusions: The fruit extract of B. crataegina showed promising α-glucosidase inhibitory activity and cytoprotective effects in the high-glucose-induced SH-SY5Y cell model. The findings suggest that these effects may arise from the combined contribution of multiple constituents within a polyphenolic matrix rather than from a single compound; however, synergistic interactions were not experimentally demonstrated. These results support further evaluation of the fruit extract through mechanistic, in vivo, and standardization studies. Full article

This study proposes a hybrid analytical–numerical method for accurately extracting five physically meaningful single-diode-model (SDM) parameters from measured illuminated I–V data. Five experimental data points are randomly selected to construct a system of five nonlinear implicit SDM equations. Hejri’s analytical method provides physically meaningful initial estimates, and an improved five-dimensional Newton–Raphson method is then used to solve the resulting equation system. When accurate characteristic-point data are unavailable, a metaheuristic fitting procedure is employed to determine the characteristic parameters required by initialization. The method is evaluated on the RTC cell, PWP 201 module, TOPCon cell, and TOPCon module and benchmarked against six state-of-the-art techniques: Ghani’s method, Hejri’s method, Kumar’s method, the artificial ecosystem-based optimization (AEO) algorithm, Phang’s method, and Villalva’s method. The proposed method achieves RMSE values of 8.31 × 10⁻⁴, 2.10 × 10⁻³, 2.40 × 10⁻², and 2.36 × 10⁻² A, respectively, consistently yielding the lowest RMSE among the four hybrid analytical–numerical methods. Its RMSE values are only 0.5–7.5% above the corresponding minima obtained by AEO. Moreover, in the equation-solving step, the proposed solver is approximately 30–2954 times faster than Ghani’s method and achieves higher convergence rates for three of the four devices. These results demonstrate that the proposed method enables accurate and efficient extraction of physically meaningful SDM parameters. Full article