A substantial share of the hotel sector's consumption and carbon footprint is in air conditioning (HVAC), lighting, and occupancy-related operational dynamics. In the context of the global energy transition goals, rising energy costs, and increasing pressure to reduce greenhouse gas emissions, improving energy efficiency in hotel rooms has become a strategic priority. In recent years, Internet of Things (IoT) technologies and building automation systems have been increasingly proposed as key enablers for optimizing energy use and mitigating CO₂ emissions in building interiors. Despite the rapid growth of scientific publications in this field, there remains a lack of consolidated evidence on performance indicators, technological architectures, empirical robustness, and real-world applicability, particularly in the hospitality sector. This study aims to systematically analyze international research on IoT-based energy efficiency in hotel rooms by identifying energy and environmental indicators used to evaluate performance, examining the reported benefits and limitations of intelligent systems, and assessing the overall maturity of the research field from a universal implementation perspective. A systematic literature review was conducted in accordance with the PRISMA guidelines. One guiding research question informs the selection criteria: which achievements are associated with the use of IoT tools in the hotel sector? After applying the predefined inclusion and exclusion criteria, a final corpus of 60 peer-reviewed scientific articles has been selected. To ensure a structured and replicable synthesis, a hierarchical analytical framework composed of seven research questions was developed and organized into four analytical levels: (A) What is studied (technologies and system definitions), (B) how it is studied (experimental design and measurement approaches), (C) what is demonstrated (energy and CO₂-related outcomes and limitations), (D) for what purpose it is applied (implementation strategies and decision support). Each article was evaluated using an ordinal scoring scale ranging from 1 (not relevant) to 4 (in-depth analysis with precise empirical results), resulting in a 60 × 7 analytical matrix. Quantitative synthesis was performed using frequency analysis, average depth scores, and consistency measures. Additionally, a research maturity index was developed to assess the developmental stage of IoT-based energy-efficiency research. The results indicate that most studies focus on HVAC and lighting systems, with occupancy smart meters, environmental sensors, smart thermostats, and centralized energy management platforms being the most frequently analyzed technologies. Energy consumption (kWh), HVAC operating time, and indoor temperature are the most reported indicators, while carbon footprint metrics are less consistently quantified. Although a large proportion of studies report energy savings associated with IoT-based control strategies, only a limited subset provides robust empirical evidence derived from experimental or quasi-experimental designs under real operating conditions. Human and operational factors, such as occupant behavior and system overrides, are repeatedly identified as significant constraints on actual energy performance, but there is a lack of empirical evidence. The research maturity index reveals that, while IoT-based energy-efficiency research is generally progressing toward consolidation, its application to hotel rooms remains largely exploratory, with persistent gaps in indicator standardization, scalability, and replicability. Within this investigation, an indicator system has been proposed, built with all the systems that have been mentioned in most articles, to contribute to further implementations of future research. From an operational perspective, these results have direct implications for hotel management. IoT systems not only reduce energy consumption but also enable predictive maintenance strategies, improve the guest experience through automated climate control, and generate valuable data for daily operations, including indoor temperature and humidity, daily guest presence times, and AC activation times, among others. The hotel industry operates with narrow margins and is subject to increasing corporate sustainability goals; therefore, systems capable of dynamically responding to actual occupancy and optimizing HVAC performance represent a competitive advantage. However, the lack of consolidated evidence hinders informed decision-making, making further studies in real-world operating environments essential to transforming these potential benefits into standard industry practices. This study concludes that IoT-based intelligent systems have substantial potential to improve energy efficiency and mitigate CO₂ emissions from the hospitality sector. However, advancing toward scalable and replicable energy solutions requires greater methodological rigor, standardized energy performance indicators, and pilot-scale experimental implementations conducted in real hotel environments. By systematically synthesizing existing evidence and identifying critical technical and methodological gaps, this research provides a robust foundation for future experimental studies. It supports the development of data-driven energy management strategies aligned with the objectives of sustainable, low-carbon building operations.

Introduction:
MXenes are a family of two-dimensional inorganic nanomaterials composed of transition-metal carbides and nitrides, well known for their high electrical conductivity, hydrophilic surfaces, and tunable surface chemistry, which make them promising candidates for advanced energy materials. These properties make them attractive in the next generation of energy storage and conversion equipment, including batteries and supercapacitors.

Methods:
In this study, density functional theory calculations are performed on finite cluster models of Ti₂C and Ti₂N MXenes to investigate their local electronic structure, bonding, and relative stability. The analysis is based on important electronic descriptors, namely total electronic energies, HOMO–LUMO gaps, charge distributions, dipole moments, symmetry properties, and differences in Ti–C and Ti–N bonding interactions.

Results:
The calculated results show clear contrasts between carbide and nitride MXenes. Ti₂C exhibits a total electronic energy of −47256.9008 eV with a HOMO–LUMO gap of 1.39 eV, indicating narrow-gap electronic behavior and favorable local electronic characteristics. In contrast, Ti₂N shows a lower total electronic energy of −47713.4497 eV, reflecting higher relative stability, along with spin-dependent HOMO–LUMO gaps of about 1.50 eV (α spin) and 1.72 eV (β spin). The Ti₂N cluster also has a finite dipole moment of 1.0858 Debye, fits within the CS point-group symmetry, and has a doublet spin state characteristic, indicating stronger polarization effects and stronger Ti–N bonding as compared to Ti–C. The systematically weakened HOMO–LUMO gaps and stronger interactions between the metals and nitrogen in the nitride MXenes indicate favorable electronic characteristics for energy applications and stronger bonding when compared to their carbide counterparts, which is also adjusted to parameters of thickness and composition.

Conclusions:
These findings show how first-principles electronic structure calculations can directly link atomic-scale bonding and electronic behavior to energy-relevant properties in MXenes. The results support the rational design of titanium carbide and nitride MXenes as advanced energy materials and underline the suitability of density functional theory-based electronic structure analysis for understanding and optimizing next-generation two-dimensional energy materials.

Single-stage single-phase onboard electric vehicle chargers have been widely adopted because of their compact structure and high efficiency. However, the performance of grid-connected operation is highly sensitive to variations in grid impedance, and this may severely compromise stability and current regulation performance. In practice, the distribution networks have uncertain and time-varying grid impedance, which, along with the charger's L-filter impedance, presents significant challenges to conventional controller designs based on nominal system parameters. This paper presents a robust inner-loop current control strategy for the single-stage single-phase onboard EV charger that explicitly takes into account both L-filter dynamics and uncertain grid impedance. The uncertain grid impedance is incorporated into the plant's representation by a small-signal model of the charger, and then, based on this model, an LMI-based robust control framework is employed to synthesize the inner-loop controller in such a way that closed-loop stability and robust performance are guaranteed over a prescribed range of grid conditions, without online estimation of grid impedance or any adaptive mechanisms. To enable implementation in the synchronous rotating reference frame, an all-pass filter is adopted to generate an artificial orthogonal (β) signal from single-phase grid voltage and current signals, and thus to facilitate dq-frame transformation and control. The proposed approach now meets a systematic design methodology for reducing the sensitivity to parameter variations while maintaining implementation simplicity. Additionally, the robust control synthesis is performed by enforcing linear matrix inequality constraints at all vertices of the polytopic uncertainty model. Specifically, sixteen vertex systems, corresponding to different combinations of L-filter parameter variations and grid impedance uncertainty, are incorporated into the LMI-based optimization, where the optimization objective is to minimize the convergence time while guaranteeing closed-loop stability and robust performance over the entire uncertainty domain. Simulation results under various grid impedance conditions confirm stable operation and reliable current tracking performance, demonstrating the effectiveness of the proposed dq-frame LMI-based robust control strategy for single-stage single-phase onboard EV chargers operating under uncertain grid conditions.

Introduction:

Electricity price forecasting in power markets is critical for energy trading, grid dispatch, and risk management. However, traditional forecasting models often focus on improving accuracy while lacking the ability to explain the underlying drivers of predictions, especially during key scenarios such as price spikes. To address this challenge, this study focuses on the high volatility and pronounced price spikes observed in the Australian National Electricity Market (NEM). It proposes a two-stage integrated framework that combines time-series feature engineering with interpretability analysis. The framework is designed to achieve both high predictive accuracy and strong interpretability, providing decision-makers in this market with analytical tools that offer both robust performance and meaningful insight.

Methods:

This study focuses on a systematic two-stage random forest analysis framework, empirically validated using historical operational data from the Australian NEM. The first stage involves dynamic feature engineering, constructing a multi-dimensional feature pool that includes original variables, lagged features (1–24 hours, 48 hours, 168 hours), rolling statistics (mean, standard deviation, extreme values), time indicators (hour, day of week, month, etc.), interaction terms, differentials and change rates, and statistical features, generating over a hundred candidate features in total. The second stage focuses on feature selection and modelling: first, feature importance is evaluated based on out-of-bag error increment from the random forest, selecting key features that either cumulatively account for more than 95% of importance or the top 30 features; subsequently, a final random forest model is trained using the selected features. To further enhance model interpretability, the framework integrates SHapley Additive exPlanations (SHAP) analysis, obtaining feature contribution values through simplified calculations, with special attention given to identifying driving factors during the Australian market's electricity price spike periods.

Results:

Testing results show that the proposed framework achieves prediction performance on the validation set with a root mean square error (RMSE) of 10.1626, a mean absolute error (MAE) of 3.8179, and a mean absolute percentage error (MAPE) of 33.36%. Compared to the baseline model using only original features, both RMSE and MAPE are significantly reduced. Compared to the model using all features without selection, RMSE improves by 1.01% and MAPE by 12.56%, indicating that feature selection effectively enhances the model’s generalization capability and robustness. Interpretability analysis further reveals that interaction features (such as price–demand interaction terms), price rolling statistics, and key variables among the original features contribute most significantly to predictions. Among the 209 identified Australian market electricity price spike moments, SHAP analysis clearly points out the main driving factors influencing spike formation, providing a quantitative basis for understanding extreme price volatility in this market.

Conclusion:

The integrated framework of time-series feature engineering and interpretability analysis proposed in this study not only improves the accuracy of Australian electricity market price forecasting through a two-stage feature selection mechanism, but also achieves transparency and interpretability in model decision-making via the SHAP method. This framework can effectively identify key causes of this market's electricity price spikes, providing electricity market operators, traders, and policymakers with an analytical tool that combines predictive performance with mechanistic explanation.

The increasing penetration of renewable resources, particularly wind generation, introduces considerable stochasticity into modern transmission networks, thereby complicating system monitoring, fault diagnosis, and post-disturbance restoration. Sudden fluctuations in wind power injections can distort network states, reduce observability, and hinder the accuracy of conventional protection schemes. As a result, the ability to accurately locate faults and minimize restoration time has become a critical requirement for achieving resilient power system operation. Motivated by these evolving challenges, this paper proposes a comprehensive multi-objective optimization framework for simultaneous fault location and restoration time minimization in a modified IEEE 39-Bus transmission system that incorporates both thermal generating units and probabilistic wind-energy resources.

The proposed methodology extends the OPF-embedded fault-location paradigm established in earlier research by formulating fault resistance, faulted-bus index, and corrective operational decisions as optimization variables. Wind uncertainty is represented using probabilistic forecast distributions, ensuring that the model realistically captures variability in renewable injections. The multi-objective problem is solved using a hybrid MOABC–NSGA-II metaheuristic, chosen for its complementary strengths: the Multi-Objective Artificial Bee Colony (MOABC) algorithm contributes strong global exploration and adaptability under non-convex OPF landscapes, while NSGA-II provides efficient non-dominated sorting, diversity preservation, and convergence control. Together, these capabilities allow the hybrid framework to effectively navigate the intricate solution space induced by fault scenarios and renewable variability. The optimization simultaneously minimizes two conflicting objectives: (i) fault-location mismatch, defined as the error between the measured system quantities and the OPF-estimated post-fault responses; and (ii) restoration time, expressed as a composite of switching, feeder reconfiguration, generator rescheduling, and ramping delays.

The resulting Pareto front reveals the trade-offs inherent in achieving high diagnostic accuracy while reducing operational downtime. Simulation studies were carried out across multiple wind-power uncertainty cases, enabling a robust performance assessment under realistic stochastic operating conditions. Results indicate that the hybrid MOABC–NSGA-II approach consistently outperforms standalone MOABC and NSGA-II algorithms across all tested scenarios. The hybrid formulation achieves smoother, denser Pareto fronts with enhanced convergence behavior and improved solution diversity. Notably, the method demonstrates superior robustness under large wind-power fluctuations, maintaining both accurate fault localization and minimal restoration times even in the presence of significant uncertainty. In addition, the framework exhibits computational scalability suitable for real-world transmission-level applications.

Overall, the proposed hybrid multi-objective OPF-based strategy advances the state of the art in fault management for renewable-integrated grids. By unifying fault localization and restoration optimization within a probabilistically informed decision-support mechanism, the framework provides power system operators with a powerful tool for improving situational awareness and accelerating service recovery. The results underscore the potential of metaheuristic hybridization and uncertainty-aware OPF modeling to strengthen grid resilience and operational reliability in an emerging era of high renewable penetration.

Introduction

In recent years, Equivalent Circuit Models (ECMs) have been extensively investigated in the literature as effective tools to accurately predict the current–voltage performance of lithium-ion cells. Batteriy performance is highly influenced by operating conditions: temperature, load current and state of charge (SOC). To characterize the circuit elements of ECMs, Hybrid Pulse Power Characterization (HPPC) tests are commonly employed. This study proposes the characterization of Panasonic NCR18650B lithium-ion batteries as a function of SOC and C-rate at constant temperature, using a modified HPPC-based methodology aimed at reducing testing time while preserving the completeness of the battery characterization: instead of performing separate HPPC tests for each C-rate, the proposed approach integrates multiple C-rates within a single dynamic test profile. A key contribution of this work is the development of a parameter estimation algorithm derived from MATLAB fitting functions originally meant for traditional HPPC tests, generalized to achieve universal applicability to both conventional and modified HPPC test profiles. Based on the identified parameters, a two-parallel ECM battery model is developed in a Simulink/Simscape environment using the Battery Equivalent Circuit block. The model accuracy is finally evaluated by comparing simulated and experimental voltage responses.

Methods

In this work, the model circuit consists of a dc voltage source, a series resistance and two RC parallel networks. The dc voltage source is used to represent the open circuit voltage of battery (Eocv), series resistance (Rs) is used to represent the internal dc resistance and RC parallel branches (R1, C1, R2, C2) are used to characterize the transient response of voltage. The model input is the applied current profile (Iin) and the output is the terminal voltage Vt; the parameters are dependent on SOC and current, while temperature dependance is neglected with tests performed at fixed 25 °C. The model is developed in MATLAB/Simulink using the Simscape Battery Equivalent Circuit block. Parameters are identified from experimental measurements obtained through a modified Hybrid Pulse Power Characterization (HPPC) test. Unlike conventional approaches, where model parameters are identified through separate HPPC tests for each C-rate, the proposed methodology unifies all current levels within a single experimental test in which four different C-rates (0.25C, 0.5C, 0.75C, and 1C) are applied at each SOC level, with SOC ranging from 100% to 0% in 5% intervals. The open-circuit voltage is estimated as the voltage value immediately preceding each current pulses series in each soc value. The ohmic resistance and RC branches parameters are identified using MATLAB’s fitECM function. Since this function is originally intended for standard HPPC datasets, dedicated pre-processing algorithms have been developed to adapt it to the modified test structure. These algorithms automatically detect, classify and extract current pulses directly from the experimental data by associating each pulse with its corresponding C-rate based on the structure of the applied current profile. Pulses belonging to the same current level are grouped and stored in dedicated pulseData objects. Each pulseData object is then used as input to the fitECM routine to estimate the ECM parameters corresponding to the specific operating condition. The identified parameters are finally organized into two-dimensional lookup tables as functions of SOC and current, which are used to parameterize the Simscape ECM block.

Results

The comparison between experimental and simulated voltage profiles shows that the model successfully reproduces dynamic voltage, confirming the effectiveness of the proposed parameter identification strategy. Discrepancies become more evident at low SOC values, where the simulated voltage slightly deviates from the experimental measurements. These errors highlight intrinsic limitations of the chosen second-order ECM in representing complex electrochemical behavior (i. e. nonlinear dynamics caused by phenomena like electrolyte lithium-ion depletion and solid-phase diffusion limitations close to the cell's working limits). This nonlinearity is reflected in the open-circuit voltage (specifically the 'knee' of the OCV curve) and significant parameter variation, which collectively degrade model performance. Nevertheless, the overall accuracy remains suitable for system-level simulations and control-oriented applications.

Conclusions

This study demonstrates that a pulse extraction and MATLAB-based parameter fitting algorithm enable efficient identification of ECM parameters as functions of both SOC and C-rate from a single modified HPPC dataset. The proposed methodology not only reduces experimental effort but also provides a fast and universally applicable tool for ECM parameter estimation.

The rapid expansion of electric vehicle (EV) fast-charging infrastructure has increased the demand for reliable and real-time fault diagnosis techniques in power electronic converters. The Vienna rectifier and the DC/DC converter are widely adopted in DC charging stations due to their high efficiency, low harmonic distortion, and reduced component count; however, they remain vulnerable to open-circuit and short-circuit faults in semiconductor devices and DC-link components. Signal-based diagnostic methods that rely on grid-synchronized estimators have gained attention due to their low computational burden and suitability for real-time implementation. Among these, Second-Order Generalized Integrator (SOGI)-based Frequency-Locked Loop (FLL) structures can be used for frequency, amplitude, and harmonic estimation. Despite their widespread use in synchronization and power quality monitoring, a systematic comparison of different SOGI-based structures for converter fault diagnosis—particularly under real-time operating conditions—has not yet been reported. This work addresses this gap by comparatively evaluating multiple SOGI-FLL variants for fault diagnosis in rectifiers used in EV DC charging applications.

This paper will present a comparative analysis of different SOGI-based grid estimation structures. Each structure is integrated into an identical fault diagnosis framework based on total harmonic distortion (THD) estimation, frequency deviation, and amplitude variation of the measured signals. The diagnostic method is applied to a three-phase Vienna rectifier model subjected to representative open-circuit and short-circuit fault scenarios affecting switching devices and DC-link components. To ensure a fair comparison, all SOGI variants are tuned using equivalent nominal parameters and evaluated under identical operating conditions. Performance metrics include fault detection time, sensitivity to fault severity, robustness to noise and grid disturbances, and computational complexity. To validate real-time feasibility and practical applicability, the proposed framework is implemented and experimentally tested using a Typhoon Hardware-in-the-Loop (HIL) simulation platform, enabling realistic fault injection, switching behavior, and sensor non-idealities.

The comparative results demonstrate that all investigated SOGI-based structures are capable of detecting converter faults using THD-based indicators; however, significant differences are observed in detection speed, noise immunity, and stability under distorted grid conditions. Hardware-in-the-Loop experiments confirm the simulation findings and validate the real-time operation of the diagnostic algorithms, with detection delays remaining within a few tens of milliseconds for all studied fault cases.

This study provides a systematic and experimentally validated comparison of SOGI structures for fault diagnosis in Vienna rectifiers. The results highlight clear trade-offs between estimation accuracy, robustness, and computational complexity, offering practical design guidelines for selecting suitable SOGI configurations in real-time diagnostic applications. The successful implementation on a Typhoon HIL platform confirms the feasibility of SOGI-based THD estimation as a reliable and interpretable diagnostic tool for EV DC charging systems. The findings support the extension of this methodology to other converter topologies and real-world charging infrastructure.

Introduction.
The accelerated integration of distributed energy resources into alternating current microgrids has increased the complexity of operational planning, particularly in reducing electrical energy losses and carbon dioxide emissions associated with slack-node power supply. These challenges are intensified by the intermittent nature of photovoltaic generation and the dynamic operation of battery energy storage systems. Population-based metaheuristic optimization methods have shown strong potential for addressing such nonlinear and constrained problems. Nevertheless, quantitative, statistically robust comparisons among algorithms remain limited, especially when technical and environmental objectives are treated independently.

Methods.
An intelligent energy management framework for AC microgrids is assessed, where the coordinated dispatch of photovoltaic units, battery energy storage systems, and D-STATCOMs is formulated as a nonlinear optimization problem. Two independent mono-objective functions are considered: minimizing total daily energy losses and minimizing total daily CO₂ emissions. A master–slave structure is adopted, in which candidate solutions are generated by population-based algorithms and evaluated using hourly power flow via successive approximations. The Population-Based Genetic Algorithm and Particle Swarm Optimization are implemented under identical tuning parameters and operational constraints, including nodal voltage limits, line loading limits, power balance equations, and battery state-of-charge bounds. The framework is tested on an IEEE 33-node AC microgrid with distributed energy resources over a 24-hour horizon with 30-minute resolution. To ensure statistical significance, each algorithm is executed 100 independent times for each objective function and operating mode.

Results.

The performance of the Population-Based Genetic Algorithm and Particle Swarm Optimization was assessed through 100 independent executions for each objective function and operating mode, enabling a statistically consistent comparison.

Under grid-connected operation, loss-minimization results show that the Genetic Algorithm achieves a best solution of 1,825.6 kW and a worst solution of 1,834.4 kW, with a mean of 1,829.6 kW and a standard deviation of 0.0965%. In contrast, PSO attains a best solution of 2,515.7 kW and a worst solution of 2,868.0 kW, yielding a mean of 2,673.9 kW and a significantly higher standard deviation of 3.2277%. The average computational time per execution is 150.0 s for the Genetic Algorithm and 216.1 s for PSO, resulting in total runtimes of 4.17 h and 6.00 h for the complete statistical assessment, respectively.

For CO₂ emission minimization in grid-connected mode, the Genetic Algorithm produces best and worst solutions of 13.5855 tCO₂ and 13.6112 tCO₂, with a mean value of 13.5989 tCO₂ and a standard deviation of 0.0442%. PSO yields a best solution of 15.1910 tCO₂ and a worst solution of 16.2613 tCO₂, with a mean of 15.7686 tCO₂ and a standard deviation of 1.5987%. The corresponding average execution times are 148.0 s for the Genetic Algorithm and 218.1 s for PSO, indicating a lower computational burden for the GA under these operating conditions.

In islanded operation, similar trends are observed. For loss minimization, the Genetic Algorithm reaches a best solution of 1,857.3 kW and a worst solution of 1,868.2 kW, with a mean of 1,862.4 kW and a standard deviation of 0.1347%. PSO presents a best value of 3,030.0 kW and a worst value of 3,320.0 kW, with a mean of 3,148.5 kW and a standard deviation of 1.5778%. Due to the increased computational complexity of islanded operation, the average execution time rises to 1,093.5 s for the Genetic Algorithm and 665.7 s for PSO, corresponding to total runtimes of 30.37 h and 18.49 h for the 100-run analysis.

For CO₂ emission minimization in islanded mode, the Genetic Algorithm achieves best and worst solutions of 22.5306 tCO₂ and 22.5939 tCO₂, with a mean of 22.5630 tCO₂ and a standard deviation of 0.0526%. PSO attains a best solution of 25.6710 tCO₂ and a worst solution of 27.9578 tCO₂, resulting in a mean of 26.8340 tCO₂ and a standard deviation of 1.6421%. The average execution times are 1,075.7 s for the Genetic Algorithm and 680.6 s for PSO, confirming that GA maintains lower variability and tighter convergence despite increased computational effort.

Conclusions.
The results demonstrate that both GA and PSO can reduce energy losses and CO₂ emissions in AC microgrids through coordinated management of distributed energy resources. However, based on extensive statistical testing with 100 independent runs per objective, the Population-Based Genetic Algorithm consistently achieves superior solution quality, significantly lower variability, and more stable convergence behavior than PSO. These findings provide statistically grounded evidence supporting the suitability of GA-based strategies for sustainable and reliable microgrid energy management.

We explore the fascinating features of unique hybrid structures composed of single-molecule magnets (SMMs) integrated with two-dimensional nanomaterials, such as graphene. These hybrid systems exhibit novel magnetic, electronic, and quantum properties arising from the synergistic interaction between the molecular magnets and the two-dimensional substrate. The combination enhances functionalities like magnetic anisotropy, spin coherence, and charge transport, opening new avenues for applications in quantum computing, spintronics, and advanced nanoelectronics. The fascinating features of the unique hybrid structures made of single molecule magnets (SMMs) and two-dimensional nano-materials (like graphene) have drawn the attention of experimental researchers. Such systems might eventually form a reconfigurable array of magnetic nano-objects and give rise to cutting-edge spin-based technology like spin quantum memory. The stability of these hybrid structures and the kinds of interactions between the localized spin in SMM and the 2D material to which the molecule is grafted are examined in detail theoretically, and results are presented here. Our theoretical investigation is supported by density functional theory based on ab initio computations. We pay close attention to how to properly describe magnetism and van der Waals dispersive forces. In addition, we looked at a few types of SMMs (molecules with tetrahedrally coordinated Fe and Cr bound to two double bis(methanesulfonamido) benzene ligands), and we examined their adsorption to a graphene monolayer. In addition, we address the crucial topic of how structural defects in graphene (such as vacancies, N-dopants, etc.) affect the adhesion and magnetic characteristics of SMMs.

This work provides valuable insights into the design of graphene–SMM hybrid materials, emphasizing the role of molecular coordination and ligand architecture in achieving stable and functional interfaces. The findings pave the way for future exploration of SMM–graphene composites in nanoscale magnetic sensors, data storage, and quantum computing technologies, where the integration of magnetic molecules with two-dimensional materials could offer unprecedented control over spin-dependent phenomena.

With the increasing complexity of modern power systems, grid synchronization has become more challenging due to the presence of DC offsets, subharmonics, and voltage distortions. One of the recent approaches capable of fully addressing these issues single-handedly is the symmetrical second-order generalized integrator phase-locked loop (SSOGI-PLL). It uses a low-pass filter (LPF) tuned to the system frequency to make the quadrature output of the SOGI function like a band-pass filter (BPF). As a result, the system can operate effectively in the presence of subharmonics and DC-offset. Moreover, the low-pass filter (LPF), tuned to the grid frequency, facilitates accurate measurement of the total harmonic distortion (THD) in the grid voltage. Although the small-signal model of the SSOGI-PLL has been developed, certain dynamic parameters, particularly the third pole introduced by internal filtering and control dynamics, are approximated using empirical assumptions. These approximations, often expressed as multiples of the fundamental frequency, result in uncertainty in modeling accuracy and limit the reliability of the analysis.

This paper focuses on the systematic refinement of the existing small-signal model of the SSOGI-PLL by identifying the most accurate location of the third pole. Starting from the established analytical model, multiple candidate representations are formulated by assigning different values to the uncertain pole, such as 2.5, 2.8, and 3 times the fundamental angular frequency. The dynamic behavior of these models is evaluated through time-domain and frequency-domain analysis and compared with the response of the nonlinear SSOGI-PLL system.

Extensive validation is performed using MATLAB/Simulink simulations under various grid disturbance scenarios, including voltage sag and swell, harmonic and subharmonic distortion, frequency deviations, and phase-jump perturbations. The comparative results demonstrate that the proposed methodology enables accurate identification of the third pole and significantly improves the agreement between the small-signal model and the nonlinear system behavior.

The refined model provides a reliable analytical foundation for stability assessment and performance evaluation of SSOGI-PLL-based synchronization systems. By resolving the uncertainty associated with the third pole, this work enhances the credibility of small-signal analysis and supports more reliable design and optimization for this grid synchronization scheme.