This paper investigates the performance of vacuum gate dielectric doping-free carbon nanotube/nanoribbon field-effect transistors (VGD-DL CNT/GNRFETs) via computational analysis employing a quantum simulation approach. The methodology integrates the self-consistent solution of the Poisson solver with the mode space non-equilibrium Green’s function (NEGF) in the ballistic limit. Adopting the vacuum gate dielectric (VGD) paradigm ensures radiation-hardened functionality while avoiding radiation-induced trapped charge mechanisms, while the doping-free paradigm facilitates fabrication flexibility by avoiding the realization of a sharp doping gradient in the nanoscale regime. Electrostatic doping of the nanodevices is achieved via source and drain doping gates. The simulations encompass MOSFET and tunnel FET (TFET) modes. The numerical investigation comprehensively examines potential distribution, transfer characteristics, subthreshold swing, leakage current, on-state current, current ratio, and scaling capability. Results demonstrate the robustness of vacuum nanodevices for high-performance, radiation-hardened switching applications. Furthermore, a proposal for extrinsic enhancement via doping gate voltage adjustment to optimize band diagrams and improve switching performance at ultra-scaled regimes is successfully presented. These findings underscore the potential of vacuum gate dielectric carbon-based nanotransistors for ultrascaled, high-performance, energy-efficient, and radiation-immune nanoelectronics.

Conventional methodologies such as Incremental Conductance (IC) and Perturbation and Observation (P&O) can be considered effective and low-cost solutions for PV Maximum Power Point Tracking (MPPT) problems in most cases. However, these methods fail to guarantee global maximum tracking in certain situations, such as multiple peak challenges caused by Partial Shading Conditions (PSCs). Therefore, metaheuristic algorithms, like Particle Swarm Optimization (PSO), are employed in literature to address the MPPT problems during PSCs. Nevertheless, traditional PSO encounters issues such as slow convergence and a high probability of failure in tracking the global maximum power point during complex PSCs, which cause a reduction of system efficiency. To address these issues, a modified PSO hybrid with a finite control set Model Predictive Control (MPSO-MPC) has been developed as a robust MPPT technique. The MPC is incorporated into the proposed method to increase the tracking speed. The proposed approach combines a new initialization scheme that ensures uniform initial population distribution across the P-I curve. Additionally, an innovative method is used to update the search space once the partial shading pattern is detected to include only the feasible solutions in the search process. Finally, Incremental Conductance (IC) is introduced to refine the tracking process of global peak and increase its efficiency. The proposed MPSO-MPC algorithm is implemented using dSPACE MicroLabBox for real-time applications. Comprehensive investigations through MATLAB/Simulink simulation and experimental studies validate that the developed method outperforms traditional PSO and Cuckoo Search (CS) algorithms, with a convergence time that does not exceed 0.35 s and a tracking efficiency above 99.5 % under various complex PSCs. Furthermore, the results demonstrate that the proposed technique outperforms both PSO and CS across a range of environmental conditions and load disturbances.

The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT-LeNet-5-LSTM technique in finding the complex characteristics in the data, while LSTM learns the trends in the dataset and performs the necessary analysis of vibrations occurring in faulty machines. The developed model was examined for various loads and faults to extract results having accuracies of 99.6%, 96.9%, 92.5% and 96.6% for load conditions 3, 2, 1, and 0, respectively. These results demonstrate the ability of the proposed model to adapt according to varying load conditions while having the necessary levels of accuracy. This validates the model to perform precise fault detection and diagnosis, offering capabilities of predictive maintenance in industrial settings.

This research paper focuses on the application of a new method for the simultaneous reconfiguration and the optimum placing of Soft Open Points (SOPs) in Radial Distribution Systems (RDS). The proposed Lévy Flight-based Improved Equilibrium Optimizer (LF-IEO) algorithm enhances the standard Equilibrium Optimizer (EO) by integrating several techniques to improve exploration and exploitation capabilities. SOPs are highly developed power electronics devices that can enhance distribution utility networks in terms of reliability and effectiveness. However, identifying their optimum place along with network reconfiguration is a challenging task that requires advanced computation techniques. The performance of the proposed LF-IEO algorithm has been first verified on several benchmark functions. Subsequently, it is implemented on a IEEE 33-Bus, 69-Bus, 118-Bus, and Algerian 116-Bus distribution network to solve the problem of simultaneous network reconfiguration and optimal SOP placement. For the Algerian 116-bus system case study, the algorithm achieved a significant 14.89% reduction in power losses, improved the minimum voltage, and generated substantial net annual savings of 74,426.40 $/year. To prove its superiority in terms of solution quality and robustness, the proposed LF-IEO approach was compared with several newly developed algorithms from the literature.

The monitoring of wind turbine (WT) systems allows operators to maximize their performance, consequently minimizing untimely shutdowns and related hazard situations while maximizing their efficiency. Indeed, the rational monitoring of WT ensures the identification of the main sources of risks at a proper time, such as internal or external failures, hence leading to an increase in their prevention by limiting the faults’ occurrence regarding the different components of wind turbines, achieving production objectives. In this context, the present paper develops a practical monitoring approach using a numerical fault-detection process for the pitch system based on a benchmark wind turbine (WT) model with the main aim of improving safety and security performance. Therefore, the proposed fault-diagnosis procedure deals with eventual faults occurring in the actuators and sensors of the pitch system. In this proposed approach, a simple, logical process is used to generate the correct residuals as fault information based on the redundancy in the actuators and sensors of the pitch sub-systems. The obtained results demonstrate the effectiveness of this proposed process for ensuring the tasks of the fault diagnosis and condition monitoring of the WT systems, and it can be a promising approach for avoiding major damage in such systems, leading to their operational stability and improved reliability and availability.

The primary objective of this paper focuses on developing a control approach to improve the operational performance of a three-level neutral point clamped (3LNPC) shunt active power filter (SAPF) within a grid-tied PV system configuration. Indeed, this developed control approach, based on the used 3LNPC-SAPF topology, aims to ensure the seamless integration of a photovoltaic system into the three-phase four-wire grid while effectively mitigating grid harmonics, grid current unbalance, ensuring grid unit power factor by compensating the load reactive power, and allowing power sharing with the grid in case of an excess of generated power from the PV system, leading to overall high power quality at the grid side. This developed approach is based initially on the application of the four-wire instantaneous p-q theory for the identification of the reference currents that have to be injected by the 3LNPC-SAPF in the grid point of common coupling (PCC). Whereas, the 3LNPC is controlled based on using the finite control set model predictive control (FCS-MPC), which can be accomplished by determining the convenient set of switch states leading to the voltage vector, which is the most suitable to ensure the minimization of the selected cost function. Furthermore, the used topology requires a constant DC-link voltage and balanced split-capacitor voltages at the input side of the 3LNPN. Hence, the cost function is adjusted by the addition of another term with a selected weighting factor related to these voltages to ensure their precise control following the required reference values. However, due to the random changes in solar irradiance and, furthermore, to ensure efficient operation of the proposed topology, the PV system is connected to the 3LNPN-SAPF via a DC/DC boost converter to ensure the stability of the 3LNPN input voltage within the reference value, which is achieved in this paper based on the use of the maximum power point tracking (MPPT) technique. For the validation of the proposed control technique and the functionality of the used topology, a set of simulations has been presented and investigated in this paper following different irradiance profile scenarios such as a constant irradiance profile and a variables irradiance profile where the main aim is to prove the effectiveness and flexibility of the proposed approach under variable irradiance conditions. The obtained results based on the simulations carried out in this study demonstrate that the proposed control approach with the used topology under different loads such as linear, non-linear, and unbalanced can effectively reduce the harmonics, eliminating the unbalance in the currents and compensating for the reactive component contained in the grid side. The obtained results prove also that the proposed control ensures a consistent flow of power based on the sharing principle between the grid and the PV system as well as enabling the efficient satisfaction of the load demand. It can be said that the proposal presented in this paper has been proven to have many dominant features such as the ability to accurately estimate the power sharing between the grid and the PV system for ensuring the harmonics elimination, the reactive power compensation, and the elimination of the neutral current based on the zero-sequence component compensation, even under variable irradiance conditions. This feature makes the used topology and the developed control a valuable tool for power quality improvement and grid stability enhancement with low cost and under clean energy.

This work presents an improved structure of a single-phase muti-input multilevel inverter (MIMLI) for distributed energy resources, which is capable of producing a nine-level output in symmetric mode and 21 levels in asymmetrical mode. The topology uses four DC sources and ten switches, with four switches being bidirectional and the remaining unidirectional. The operation of the circuit is analyzed in an asymmetrical mode, and switching signals are accomplished using the Nearest Level Control (NLC) PWM technique. Depending on the value of the DC sources used, the number of levels can vary. In this work, different DC source algorithms were also proposed, and the analysis of the inverter has been carried out considering the algorithms producing the maximum number of levels. The inverter was simulated in MATLAB/Simulink under steady state and dynamic conditions, achieving a 3.89% THD in output. The thermal analysis was conducted using PLECS software 4.1.2 to assess losses and efficiency. A laboratory prototype of the proposed topology was developed and tested, confirming its performance through simulation results and proving it economically viable for medium- and high-power applications.

Over the last decade, flexible alternating current transmission systems (FACTS) have been crucial in ensuring optimal power distribution within modern power systems. A vital component of FACTS devices is the distribution static compensator (DSTATCOM), which is essential for maintaining a reliable power supply. It is commonly used for reactive power compensation, voltage regulation, and harmonic reduction. Determining the appropriate size and placement of DSTATCOMs is vital to ensuring their efficiency. This study introduces the improved gray wolf optimizer (I-GWO), a refined version of the classical gray wolf optimization (GWO) method. The I-GWO incorporates a dimension learning-based hunting (DLH) strategy to preserve population diversity, balance exploration and exploitation, and prevent the premature convergence of classical GWO. In this research, the I-GWO was applied to determine the optimum allocation and sizing of the DSTATCOMs, considering system constraints, including those presented by the intermittent and stochastic nature of the load and renewable energy resources, specifically wind and solar energy. The suggested approach was successfully tested on 33-, 69-, and 85-bus distribution systems and then compared with existing studies. The results demonstrated the I-GWO-based approach’s superiority in terms of reducing power losses, improving voltage profiles, and enhancing voltage stability.