Tuning Effect Research Articles

Investigating the Effects on Replacing Platinum by Molybdenum as Cathode Electrocatalysts in Proton Exchange Membrane Water Electrolysis

Water electrolysis coupled to renewable energy sources has become in a crucial technique for the green hydrogen production. Hydrogen economy is currently in the spotlight to solve energy demands due to its advantages such as clean, sustainable, low footprint, as well as high-power density which is 3 times above gasoline and diesel. To produce large quantities of hydrogen, proton exchange membrane water electrolysis (PEMWE) has arisen as a promising system to solve hydrogen demands. The heart of the PEMWE consists in a membrane electrode assembly (MEA), where two gas diffusion layers (GDLs), hosting the respective catalysts, are sandwiched between an ionomer membrane. Commonly, noble metals are needed as catalyst in the anode (Ir, Ru) and cathode (Pt) to reach high current densities maintaining low cell voltages. The large content of noble metals, their high cost, and accelerated corrosion rates at high cell voltages are some drawbacks that limit the industrialization and commercialization of PEMWE technology. Replacing, decreasing, or simply extending the operational lifetime of these noble metals have a positive impact on the hydrogen economy. Molybdenum is an earth-abundant metal with low-cost which has been already used to produce highly active electrocatalysts, that can potentially replace Pt. However, most electrocatalytic studies are restricted to three-electrode cells with different operational conditions than those employed in PEM cells. The latter makes a complicated task to predict the performance of the catalyst and stability under relevant instrial applications. Here, we investigated the viability of using three selected Mo-based nanomaterials (1T’-MoS2, Co-MoS2, and ß-Mo2C) as potential electrocatalysts to replace Pt in PEMWE systems. We investigated the effects of replacing Pt in the catalyst loading, charge transfer resistance, kinetics, operational stability, and hydrogen production efficiency during PEMWE operation. Furthermore, we developed a methodology to identify the effects of tuning the cathode catalyst while keeping the anode catalyst in the overall kinetics of PEMWE system. Our results point that electrochemical performance in traditional open cell might not strictly predict the performance that could be seen in PEMWE cells due to differences in interfaces, contact between the components and porosity of the macroscopic catalyst layers. Among the catalyst studied, 1T’-MoS2 exhibited low overpotential associated to low charge transfer resistance while reaching 1 A cm-2 at 1.9 V. Our study highlights the importance to continue developing efficient electrocatalysts to replace noble metals suitable for PEMWE systems. Figure 1

Optimizing piezoresistivity in SiTiOC through modulus-tunable Re-entrant structures

Piezoresistive ceramic materials have significant advantages in terms of high sensitivity and stability, but their inherent high stiffness limits macroscopic deformation and detection range. Modulus-optimizable SiTiOC re-entrant structures have been presented to enhance the detection limit of piezoresistive polymer-derived ceramic (PDC) through geometric adjustments, and the relationship between piezoresistive performance and structural geometric parameters was investigated. By modifying the inner folding angles of 30°, 45°, 60° and fix-connected, the deformation behavior and mechanical performance of the structures can be flexibly controlled, resulting in variable sensing properties with a highly sensitive load range tuned from 30 to 100 N. The SiTiOC material obtained showed an optimized sensitivity coefficient of 0.14 MPa−1, which was attributed to an innovative modification of SiOC ceramics by introducing 2 wt% titanium acetylacetonate, creating impurity carrier accumulation regions and evenly dispersed conductive phases, forming percolation networks. Furthermore, the SiTiOC re-entrant structures exhibited long-term cyclic stability, maintaining a steady signal output under different loading degrees and frequencies. This was attributed to the excellent mechanical performance of the structures, with a high compressive strength and energy absorption value reaching 32.6 MPa and 125.44 kJ m−3, respectively, which ensured sufficient load-bearing capacity and the preservation of internal percolation networks. This study demonstrates the effectiveness of piezoresistivity tuning in PDC structures through geometric adjustments, thus expanding their application range. The one-step manufacturing of structuralized sensing materials by 3D printing provides a straightforward approach for the customized design of piezoresistive devices.

Effect of shear strain on the electronic and optical properties of Al-doped stanane.

The quasi-metallic properties of stanene limit its prospects in optoelectronic devices. Based on first-principles calculations, a systematic study is conducted on the tuning effects of surface hydrogenation and Al atom doping on the electronic and optical properties of stanene. Surface hydrogenation serves as an ideal way to open the forbidden band of stanene, and Al atom doping further increases hydrogenated stanene (stanane) band gap to 0.460eV. Deformation has a minor impact on the stability of the stanane-Al structure, while shear strain can effectively modulate the band gap engineering of the doped system, reducing the band gap value from 0.460 to 0.170eV. Deformation induces a redshift in the absorption peak and reflectance, also slowing down the rate of decrease in the absorption coefficient, and enhancing the peak value of light reflectance, which is positively correlated with the degree of shear strain. These findings hold promise for expanding the potential application of monolayer stanane in semiconductor devices. All calculations are performed using CASTEP module in Materials Studio based on the density functional theory (DFT). The Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) is employed to describe the exchange-correlation energy (Perdew et al., Phys Rev Lett 77(18), 1996). We construct models for both stanene and stanane. The original unit cell of stanene has two Sn atoms, while stanane consists of two Sn atoms and two H atoms, and expand them to a 3 × 3 × 1 supercell with a vacuum layer of 20Å in height to prevent interlayer coupling. After convergence testing, the plane-wave cutoff energy is set to 450eV, and the energy convergence threshold is set to 1 × 10-5eV. The maximum residual stress for each atom is set to 0.01eV/Å. Brillouin zone sampling is performed using a 6 × 6 × 1k-point mesh based on the Monkhorst-Pack method (Monkhorst and Pack, Phys Rev B 13(12), 1976). The k-point accuracy of the density of states and optical properties is 9 × 9 × 1. All calculations are performed using the more advanced OTFG ultrasoft pseudopotential, and structural relaxations are performed using supercells to ensure that the model is fully relaxed. We use the HSE06 functional to calculate the energy band structures of stanane-Al deformed to 0%, 4%, and 8%, resulting in band gap values of 1.465eV, 1.368eV, and 1.016eV, respectively. These values are significantly higher than those obtained using the PBE functional (0.460eV, 0.397eV, and 0.170eV). However, the shapes and trends of the band structures obtained from both PBE and HSE06 calculations are similar. Additionally, the calculation time needed by HSE06 is greatly longer than PBE, which exceeds the capabilities of our computer hardware, and cannot support all subsequent calculations. To investigate the influence of deformations on the variation of band gap values and to conserve computational resources, the subsequent calculations in this study use the PBE functional.

Enhancing Seismic Performance of Steel-Plated Stress-Ribbon Bridge with Tuned Mass Dampers: A Finite-Element Study

There has been a noticeable rise in the construction of lightweight stress-ribbon pedestrian bridges. In regions with a high risk of seismic activity, it is crucial to employ advanced seismic control technology to mitigate the impact of earthquakes and improve the bridge’s performance and durability. The objective of this study is to investigate the effect of tuned mass dampers (TMDs) on a steel-plated stress-ribbon bridge using the finite element method. The study aims to analyze the performance of various TMD designs focusing on both vertical and torsional modes. Multiple TMD configurations are considered and numerically compared. The results indicate that TMDs offer both a tuning effect and a static mass effect for stress-ribbon bridges. The tuning effect is most pronounced when the mass ratio falls between 2% and 4%. Installing a single TMD with a mass ratio of 0.05 can decrease vertical displacement along the entire span by up to 36%. Furthermore, a torsional TMD effectively reduces both the torsion of stress ribbons and the pier forces. As a recommendation, the combination of a single vertical displacement TMD and a torsional TMD can be considered the most appropriate scheme for earthquake response control. Moreover, the addition of TMDs reduces the bridge’s sensitivity to the direction of earthquake excitation. These findings contribute to a broader understanding of the earthquake performance of stress-ribbon bridges and assist designers in selecting appropriate control schemes to address vibrational issues.

Effect of microstructures tuning on mechanical properties of an ultra-high strength marine steel via the TRIP effect

Effect of microstructures tuning on mechanical properties of an ultra-high strength marine steel via the TRIP effect

Polariton design and modulation via van der Waals/doped semiconductor heterostructures

Hyperbolic phonon polaritons (HPhPs) can be supported in materials where the real parts of their permittivities along different directions are opposite in sign. HPhPs offer confinements of long-wavelength light to deeply subdiffractional scales, while the evanescent field allows for interactions with substrates, enabling the tuning of HPhPs by altering the underlying materials. Yet, conventionally used noble metal and dielectric substrates restrict the tunability of this approach. To overcome this challenge, here we show that doped semiconductor substrates, e.g., InAs and CdO, enable a significant tuning effect and dynamic modulations. We elucidated HPhP tuning with the InAs plasma frequency in the near-field, with a maximum difference of 8.3 times. Moreover, the system can be dynamically modulated by photo-injecting carriers into the InAs substrate, leading to a wavevector change of ~20%. Overall, the demonstrated hBN/doped semiconductor platform offers significant improvements towards manipulating HPhPs, and potential for engineered and modulated polaritonic systems.

The In Situ Construction of NiFeS on Stainless Steel Mesh as an Efficient Electrocatalyst for UOR

Urea can be used as an effective hydrogen energy carrier, the development of efficient and stable anodic electrocatalysts for urea oxidation reaction (UOR) was crucial for achieved energy-saving hydrogen-production. Nanoflower like structures electrocatalysts have attracted increasing research interests due to its structural advantages. In this article, NiFeS/SSM with two dimensions (2D) nanosflower like structures in situ growth on three dimensions (3D) stainless steel mesh (SSM) is prepared by step-wise hydrothermal method. NiFeS/SSM was derived from NiFe layered double hydroxides, and the as-prepared electrode was directly served as a electrocatalysts for UOR. Benefiting from the electronic effect between Ni and Fe components which promotes the formation of the high valence Ni species in the NiFeS/SSM catalyst, the tuning effect from the in situ generated sulfate and its open 3D porous structure for SSM basement. The electrode displayed excellent catalytic activities towards UOR, the overpotential for UOR are 1.08 V in alkaline electrolyte at 50 mA·cm−2. This work not only illustrated that non-noble metal-based electrocatalysts have tremendous potential applications for electrocatalytic electrolysis of urea but also provided a feasible strategy to prepare high-efficiency and stable non-precious metal UOR anode catalysts in alkaline medium.

Fermi Level Pinning in Concentrated Light-Induced Band Edge Tuning Maximizes Photocatalytic Solar-to-Hydrogen Efficiency.

Here, we demonstrate a concentrated light-induced band edge tuning effect in photocatalytic hydrogen production. This band movement along with Femi level pinning leads to two distinct catalytic behaviors upon irradiation flux increase. Specifically, the concentration of the light promotes more long-lived carriers bound to the surface electronic states, progressively boosting energy conversion efficiency to a maximum value. Afterward, efficiency diminishes gradually due to poor carrier transfer. This work offers critical insights into efficient and economical photocatalytic hydrogen production.

Classifying real-world macroscopic images in the primary-secondary care interface using transfer learning: implications for development of artificial intelligence solutions using nondermoscopic images.

The application of deep learning (DL) to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion classification performance on curated datasets comparable to that of experienced dermatologists. Most skin disease images encountered in clinical settings are macroscopic, without dermoscopic information, and exhibit considerable variability. Further research is necessary to determine the generalizability of DL algorithms across populations and acquisition settings. To assess the extent to which DL can generalize to nondermoscopic datasets acquired at the primary-secondary care interface in the National Health Service (NHS); to explore how to obtain a clinically satisfactory performance on nonstandardized, real-world local data without the availability of large diagnostically labelled local datasets; and to measure the impact of pretraining DL algorithms on external, public datasets. Diagnostic macroscopic image datasets were created from previous referrals from primary to secondary care. These included 2213 images referred from primary care practitioners in NHS Tayside and 1510 images from NHS Forth Valley acquired by medical photographers. Two further datasets with identical diagnostic labels were obtained from sources in the public domain, namely the International Skin Imaging Collaboration (ISIC) dermoscopic dataset and the SD-260 nondermoscopic dataset. DL algorithms, specifically EfficientNets and Self-attention with Window-wise Inner-product based Network (SWIN) transformers, were trained using data from each of these datasets. Algorithms were also fine-tuned on images from the NHS datasets after pretraining on different data combinations, including the larger public-domain datasets. Receiver operating characteristic curves and the area under such curves (AUC) were used to assess performance. SWIN transformers tested on Forth Valley data had AUCs of 0.85 and 0.89 when trained on SD-260 and Forth Valley data, respectively. Training on SD-260 followed by fine tuning of Forth Valley data gave an AUC of 0.91. Similar effects of pretraining and tuning on local data were observed using Tayside data and EfficientNets. Pretraining on the larger dermoscopic image dataset (ISIC 2019) provided no additional benefit. Pretraining on public macroscopic images followed by tuning to local data gave promising results. Further improvements are needed to afford deployment in real clinical pathways. Larger datasets local to the target domain might be expected to yield further improved performance.

Spectral tuning of broadband terahertz radiation from dc-biased laser plasma filaments

Terahertz radiation from laser plasmas exhibits a broadband spectrum, making it a bright prospect for terahertz spectroscopy. In this work, we demonstrate that the continuous redshift of terahertz radiation from a dc-biased plasma filament can be achieved by increasing the length of the plasma filament. A theoretical model concerning a Lorentz distribution of the plasma density along a filament is proposed to interpret this spectral tuning effect. This finding provides not only an approach to spectral tuning but also a gentle way of diagnosing the distribution of the plasma density inside such plasmas.

Automated machine learning for deep learning based malware detection

Deep learning (DL) has proven to be effective in detecting sophisticated malware that is constantly evolving. Even though deep learning has alleviated the feature engineering problem, finding the most optimal DL model's architecture and set of hyper-parameters, remains a challenge that requires domain expertise. In addition, many of the proposed state-of-the-art models are very complex and may not be the best fit for different datasets. A promising approach, known as Automated Machine Learning (AutoML), can reduce the domain expertise required to develop custom DL models by automating the ML pipeline key components, namely hyperparameter optimization and neural architecture search (NAS). AutoML reduces the amount of human trial-and-error involved in designing DL models, and in more recent implementations can find new model architectures with relatively low computational overhead.Research on the feasibility of using AutoML for malware detection is very limited. This work provides a comprehensive analysis and insights on using AutoML for both static and online malware detection. For static, our analysis is performed on two widely used malware datasets: SOREL-20M to demonstrate efficacy on large datasets; and EMBER-2018, a smaller dataset specifically curated to hinder the performance of machine learning models. In addition, we show the effects of tuning the NAS process parameters on finding a more optimal malware detection model on these static analysis datasets. Further, we also demonstrate that AutoML is performant in online malware detection scenarios using Convolutional Neural Networks (CNNs) for cloud IaaS. We compare an AutoML technique to six existing state-of-the-art CNNs using a newly generated online malware dataset with and without other applications running in the background during malware execution. We show that the AutoML technique is more performant than the state-of-the-art CNNs with little overhead in finding the architecture. In general, our experimental results show that the performance of AutoML based static and online malware detection models are on par or even better than state-of-the-art models or hand-designed models presented in literature.

A Sample-Aware Database Tuning System With Deep Reinforcement Learning

Based on the relationship between client load and overall system performance, the authors propose a sample-aware deep deterministic policy gradient model. Specifically, they improve sample quality by filtering out sample noise caused by the fluctuations of client load, which accelerates the model convergence speed of the intelligent tuning system and improves the tuning effect. Also, the hardware resources and client load consumed by the database in the working process are added to the model for training. This can enhance the performance characterization ability of the model and improve the recommended parameters of the algorithm. Meanwhile, they propose an improved closed-loop distributed comprehensive training architecture of online and offline training to quickly obtain high-quality samples and improve the efficiency of parameter tuning. Experimental results show that the configuration parameters can make the performance of the database system better and shorten the tuning time.

Tuning In-Plane Magnetic Anisotropy and Interfacial Exchange Coupling in Epitaxial La2/3Sr1/3CoO3/La2/3Sr1/3MnO3 Heterostructures.

Controlling the in-plane magnetocrystalline anisotropy and interfacial exchange coupling between ferromagnetic (FM) layers plays a key role in next-generation spintronic and magnetic memory devices. In this work, we explored the effect of tuning the magnetocrystalline anisotropy of La2/3Sr1/3CoO3 (LSCO) and La2/3Sr1/3MnO3 (LSMO) layers and the corresponding effect on interfacial exchange coupling by adjusting the thickness of the LSCO layer (tLSCO). The epitaxial LSCO/LSMO bilayers were grown on (110)o-oriented NdGaO3 (NGO) substrates with a fixed LSMO (top layer) thickness of 6 nm and LSCO (bottom layer) thicknesses varying from 1 to 10 nm. Despite the small difference (∼0.2%) in lattice mismatch between the two in-plane directions, [001]o and [11̅0]o, a pronounced in-plane magnetic anisotropy was observed. Soft X-ray magnetic circular dichroism hysteresis loops revealed that for tLSCO ≤ 4 nm, the easy axes for both LSCO and LSMO layers were along the [001]o direction, and the LSCO layer was characterized by magnetically active Co2+ ions that strongly coupled to the LSMO layer. No exchange bias effect was observed in the hysteresis loops. In contrast, along the [11̅0]o direction, the LSCO and LSMO layers displayed a small difference in their coercivity values, and a small exchange bias shift was observed. As tLSCO increased above 4 nm, the easy axis for the LSCO layer remained along the [100]o direction, but it gradually rotated to the [11̅0]o direction for the LSMO layer, resulting in a large negative exchange bias shift. Therefore, we provide a way to control the magnetocrystalline anisotropy and exchange bias by tuning the interfacial exchange coupling between the two FM layers.

Notable improvements on LWFA through precise laser wavefront tuning

Laser wakefield acceleration (LWFA) continues to grow and awaken interest worldwide, especially as in various applications it approaches performance comparable to classical accelerators. However, numerous challenges still exist until this can be a reality. The complex non-linear nature of the process of interaction between the laser and the induced plasma remains an obstacle to the widespread LWFA use as stable and reliable particle sources. It is commonly accepted that the best wavefront is a perfect Gaussian distribution. However, experimentally, this is not correct and more complicated ones can potentially give better results. in this work, the effects of tuning the laser wavefront via the controlled introduction of aberrations are explored for an LWFA accelerator using the shock injection configuration. Our experiments show the clear unique correlation between the generated beam transverse characteristics and the different input wavefronts. The electron beams stability, acceleration and injection are also significantly different. We found that in our case, the best beams were generated with a specific complex wavefront. A greater understanding of electron generation as function of the laser input is achieved thanks to this method and hopes towards a higher level of control on the electrons beams by LWFA is foreseen.

Edge defects-regulated FeCo metalic sites confined in N-doped carbon polyhedron as an efficient ORR catalyst for durable Zn-air battery

Metal sites or nanoparticles immobilized on MOF-derived porous carbon matrix have gained enormous attention for oxygen electroreduction reaction (ORR), but they still suffered from the insufficient catalytic performance. Herein, we introduced edge defects to regulate the ORR performance of N-doped carbon polyhedron-supported FeCo bimetallic catalysts via a facile NaCl/KCl-assisted pyrolysis. HR-TEM, HAADF-STEM, Raman spectra, XPS and EPR analysis confirmed the existence of uniform FeCo atoms and edge defects within N-doped porous carbon polyhedrons and their mutual effect helped promoting ORR performance. The resulting FeCo-NC-30 exhibited more excellent ORR activity with E1/2 of 0.86 V, and faster kinetic process with a higher kinetic current density, Jk,E = 0.85V = 7.15 mA/cm2. As a cathode catalyst, it delivered a desirable power density of 196.9 mW/cm2, comparable to Pt/C, in home-made Zn-air batteries. Our findings demonstrated that the electronic tuning effect caused by carbon edge defects and synergistic bimetallic interactions is beneficial for enhancing ORR activity.

Design and optimization of roof-top fire water tanks as compliant deep tank dampers-inerter for seismic protection of multi-storeyed buildings

Water tanks are installed on the roofs of high-rise buildings for services and to fight fire outbreaks. These tanks are often considered unsuitable for design as tuned liquid dampers (TLDs) and have higher depth ratios than that required in conventional TLDs due to practical constraints in dimensioning the tank as per the tuning requirement. The present work aims at alleviating these hindrances and utilizing deep water storage tanks as effective passive damping devices for the host building. In this paper, a water storage tank for firefighting, situated on the roof of a building, is designed as a compliant deep tank damper inerter (CDTDI) in controlling the response of the host building subjected to seismic excitations. The tank is flexibly connected to the building through spring and damping elements to act as a compliant damper system. Further, the tuning effect, mass, and damping enhancement potential of an inerter element are employed to improve the volumetric efficiency of the compliant damper system. Two example buildings, 10- and 20-storey, are taken up and modeled as multi-degree-of-freedom (MDOF) systems whose masses are lumped at storey levels. The coupled dynamic equations of motion of the CDTDI-MDOF structural system are formed and solved in the time domain using the Newmark-β method. An external excitation-dependent, multi-objective Grey Wolf optimization is performed to estimate the optimum CDTDI parameters by subjecting the buildings to 100 recorded seismic ground excitations. The average values of the peak roof displacement, the maximum inter-storey drift ratio, and the peak floor acceleration are considered objective functions of the optimization. The results reveal the proficiency of the CDTDI in controlling the multiple modes of the structure. Further, the optimally designed CDTDI provides significant damping and stability to the structure, which is evident from the energy plots.

Unlocking enhanced gas storage capacity in tuned methane hydrates: Exploring eutectic compositions and water-to-hydrate conversion

The tuning effect of gas hydrates has received widespread interest as a promising approach for energy gas storage due to its unique features for enhancing gas storage capacity and formation conditions. However, its practical application has been stagnant because the effect of various factors has still not been revealed to induce the tuning effect. Here, we provide the dominant factors and optimized conditions related to the tuning effect to achieve the practical enhancement of gas storage capacity under mild formation conditions (272.0 K, 2.35 MPa). Based on the tetrahydrofuran (THF)/trimethylene oxide (TMO) + CH4 hydrates, dominant factors that can affect tuning effects such as eutectic composition, the concentration of the promoter, formation methods, and conversion of water-to-hydrate were experimentally examined by employing the in-situ Raman spectroscopy. The tuning effects of hydrates formed from the aqueous solutions (water-borne) were slowly advanced, unlike all hydrates formed from iced particles (ice-borne) which extremely rapidly induced them. It suggests that the eutectic point was the primary factor in water-borne hydrates, whereas the tuning effect in ice-borne hydrates is mainly governed by the formation rate and stochastic nature. Interestingly, our experiments confirmed for the first time that the gas uptake of water-borne tuned gas hydrates may be higher than those of untuned hydrates (i.e. 5.56 mol% stoichiometric concentration). This work provides not only the dominant parameters but also promising strategies for the practical application of tuning effects to efficient hydrate-based gas storage.

Accelerator tuning method using autoencoder and Bayesian optimization

During beam-accelerator operation, a large number of parameters need to be tuned. In recent years, tuning methods based on machine learning have been extensively studied. Bayesian optimization (BO) has attracted considerable attention as an excellent method for accelerator tuning. However, its applicability is limited by the number of parameters that can be tuned. In this study, we propose an optimization method that combines autoencoder and BO to tune a large number of parameters. We verified it using beam transport simulations. We confirmed a higher tuning effect in a shorter time than when using only BO. The proposed method is expected to speed up the accelerator operation and provide comprehensive tuning.

Effectiveness of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis: a study of 10,000 consecutive cases

Diagnostic image analysis for unruptured cerebral aneurysms using artificial intelligence has a very high sensitivity. However, further improvement is needed because of a relatively high number of false positives. This study aimed to confirm the clinical utility of tuning an artificial intelligence algorithm for cerebral aneurysm diagnosis. We extracted 10,000 magnetic resonance imaging scans of participants who underwent brain screening using the “Brain Dock” system. The sensitivity and false positives/case for aneurysm detection were compared before and after tuning the algorithm. The initial diagnosis included only cases for which feedback to the algorithm was provided. In the primary analysis, the sensitivity of aneurysm diagnosis decreased from 96.5 to 90% and the false positives/case improved from 2.06 to 0.99 after tuning the algorithm (P < 0.001). In the secondary analysis, the sensitivity of aneurysm diagnosis decreased from 98.8 to 94.6% and the false positives/case improved from 1.99 to 1.03 after tuning the algorithm (P < 0.001). The false positives/case reduced without a significant decrease in sensitivity. Using large clinical datasets, we demonstrated that by tuning the algorithm, we could significantly reduce false positives with a minimal decline in sensitivity.

Investigation on Tuning Power–Frequency Droop for Improved Grid–Forming Inverter and Synchronous Generator Transient Load Sharing

Poor transient load sharing has been observed during the parallel operation of synchronous generators (SGs) and droop-controlled grid-forming (GFM) inverter-based resources (IBRs) in islanded microgrids. This can result in overcurrent protections, causing the inverters to trip, which leads to the collapse of voltage and frequency. In this article, small-signal analysis supported by electromagnetic transients (EMTs) simulation is performed on a detailed model of a microgrid containing IBRs and an SG to identify underdamped modes and their sensitivities to the power–frequency (P-F) droop parameter. Further EMT simulation of a more complex microgrid model with multiple GFM IBRs and SGs is performed to determine whether trends identified from the modal analysis extend to cases where the number of SGs in operation may vary. The objective is to investigate the effectiveness of tuning this parameter to reduce the likelihood of overcurrents in response to load and generation disturbances. The work uncovers both fast and slow modes of concern. Primarily, the findings show that increasing the P-F droop value improves the damping of the slower mode while degrading the damping of the faster mode, leading to an upper limit on the damping improvement of the GFM IBR output current. In EMT simulations of the more complex microgrid, the greatest damping improvement still exhibits significant maximum overshoot. Furthermore, the optimal value of the P-F droop parameter is sensitive to the number of SGs in operation. The conclusion is that tuning the P-F droop parameter is not an effective means to avoid the overcurrent tripping of GFM IBRs during response to large load or generation disturbances when operating in parallel to SGs in islanded microgrids.