Operating Conditions Research Articles

Karakteristik Breakdown Voltage Pada Dielektrik Minyak Mineral dan Minyak Ester Natural

The increasing demand for electrical energy, driven by population growth and industrial development, necessitates efficient and sustainable power distribution systems. Transformers, essential components of these systems, typically use mineral oil for insulation and cooling, despite its environmental drawbacks, such as high carbon emissions and non-biodegradability. In response, natural ester oils have emerged as eco-friendly alternatives. This study aimed to compare the dielectric breakdown voltage characteristics of mineral oil (Diala B) and natural ester oil (FR3) in transformer applications. The testing followed the IEC 60156 standard, focusing on transformers operating at 20 kV, with a minimum breakdown voltage threshold of 30 kV/2.5mm. Both treated and untreated samples were examined.The results indicated that FR3 ester oil exhibited superior dielectric properties, including higher breakdown voltage, flashpoint, and firepoint, as well as lower carbon emissions compared to mineral oil. These findings suggest that natural ester oil has significant potential as a sustainable replacement for mineral oil, particularly in applications where environmental and safety concerns are prioritized.The study recommends further long-term testing under operational conditions to confirm the performance and stability of ester oil in real-world transformer applications. These findings contribute to the growing body of research supporting the transition to more environmentally friendly transformer insulation materials, which could have broad implications for the power industry’s sustainability efforts

The Analysis of the Thermal Processes Occurring in the Contacts of Vacuum Switches During the Conduction of Short-Circuit Currents

This article presents the results of research on the thermal state of vacuum switch contacts during the conduction of short-circuit currents. This state is directly related to the value of the flowing current and the operating conditions of the switch. These conditions are mildest in the case of the conduction of operating currents through closed contacts. The situation worsens significantly when short-circuit currents are conducted, and the greatest destructive effects occur during commutation processes. Exceeding a certain level of contact destruction usually leads to the loss of the switching capacity of the switch. In vacuum switches, tracking the thermal state of the contacts is particularly difficult due to the inaccessibility of transducers or measurement sensors inside the chamber. In such a case, simulation studies verified by experimental results are important. This paper presents the results of such studies, directed at their practical implementation in the design and operation of vacuum switches. Simulation studies were conducted to analyze the thermal processes occurring in the contacts of vacuum switches during the conduction of short-circuit currents. Special attention was paid to the influence of contact parameters on the thermal processes occurring during the conduction of short-circuit currents. In addition to simulations, experimental studies were carried out to verify the simulation results. Ultimately, the research results presented are intended to provide practical knowledge of the design and operation of vacuum switches, particularly with regard to the contact heating processes during the conduction of short-circuit currents.

Fe-N catalyst derived from and supported on Lycopodium clavatum sporopollenin exine capsules for the oxygen reduction reaction

A carbon-supported electrocatalyst, featuring carbon nanotubes anchored on 3D porous graphitic carbon, was developed with the aim to perform in the operating conditions of alkaline fuel cells and metal air batteries. The catalyst was developed via two steps: first powders of Sporopollenin exine capsules used as a bio-based carbon support were activated via CO2 gasification to obtain a high specific area and porosity, second the derived porous carbons were impregnated by an iron salt and a nitrogen source, to be carbonised in Nitrogen at high temperature. The prepared catalyst demonstrated an efficient oxygen reduction reaction activity showing a half-wave potential of ~ 0.775 V vs. Reversible hydrogen electrode, comparable with that of commercial 20 wt% Pt/C in alkaline conditions, a good stability after accelerated degradation testing, retaining ~ 86% of the initial limiting current density, and a higher diffusion limited current density (6.3 vs. 5.1 mA cm− 2) than the commercial counterpart. Overall, we show the suitability of Sporopollenin exine capsule as support for electrocatalysis and a promising methodology to develop sustainable catalysts for the oxygen reduction reaction.

The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data’s collection and pre-processing.

Research on Fault Prediction of Power Devices in Rod Control Power Cabinets Based on BiTCN-Attention Transfer Learning Model

The Insulated Gate Bipolar Transistor (IGBT) is the key power device in the rod control power cabinet of nuclear power plants; its reliable operation is of great significance for ensuring the safe and economical operation of the nuclear power plants. Therefore, it is necessary to conduct fault prediction research on IGBT to achieve better condition-based maintenance and improve its operational reliability. However, power cabinets often operate under multiple, complex working conditions, so predicting IGBT faults from single working condition data usually has limitations and low accuracy. Its failure probability has an important relationship with the actual operating conditions of the cabinet. In order to improve the reliability and maintainability of the control power cabinet in nuclear power plants, this paper takes IGBTs in the rod control power cabinet as the object and makes full use of the data of IGBTs under multiple working conditions to carry out research on the cross-condition fault prediction of IGBTs under multiple-source working conditions. A transfer learning (TL) model based on a bidirectional time convolutional network (BiTCN) combined with attention was proposed to solve the problem of low accuracy of cross-operating fault prediction in a multi-source domain. Firstly, an IGBT fault simulation model was built to collect the life cycle state data of the module under different working conditions. Then, after pre-processing such as removing outliers, kernel principal component analysis (KPCA) was used to integrate all source domain data, obtain source domain characterization data, and train the BiTCN-attention model. Finally, the BiTCN-attention model trained in the source domain was transferred, and the model was fine-tuned according to the target domain data. Simulation results show that the accuracy of the proposed BiTCN-attention transfer learning prediction method can reach more than 99%, which is significantly better than that of the recurrent neural network transfer learning (RNN-TL) model, long short-term memory network transfer learning (LSTM-TL) model, gated cyclic unit transfer learning (GRU-TL) model, and time convolutional network transfer learning (TCN-TL) model. This method can not only reduce the inconsistency of fault characteristic values caused by changes in working conditions but also accurately predict the degradation trend when only early fault data are available, providing an effective solution for IGBT fault prediction across working conditions in multi-source domains.

An Arrhenius-Based Simulation Tool for Predicting Aging of Lithium Manganese Dioxide Primary Batteries in Implantable Medical Devices

This article presents a novel aging-coupled predictive thermo-electrical dynamic modeling tool tailored for primary lithium manganese dioxide (Li-MnO2) batteries in active implantable medical devices (AIMDs). The aging mechanisms of rechargeable lithium batteries are well documented using computationally intensive physics-based models, unsuitable for real-time onboard monitoring in AIMDs due to their high demands. There is a critical need for efficient, less demanding modeling tools for accurate battery health monitoring and end-of-life prediction as well as battery safety assessment in these devices. The presented model in this article simulates the battery terminal voltage, remaining capacity, temperature, and aging during active discharge, making it suitable for real-time health monitoring and end-of-life prediction. We incorporate a first-order dynamic for internal resistance growth, influenced by time, temperature, discharge depth, and load current. By adopting Arrhenius-type kinetics and polynomial relationships, this model effectively simulates the combined impact of these variables on battery aging under diverse operational conditions. The simulation handles both the continuous micro-ampere-level demands necessary for device housekeeping and periodic high-rate pulses needed for therapeutic functions, at a constant ambient temperature of 37 °C, mimicking human body conditions. Our findings reveal a gradual, nonlinear increase in internal resistance as the battery ages, rising by an order of magnitude over a period of 5 years. Sensitivity analysis shows that as the battery ages and load current increases, the terminal voltage becomes increasingly sensitive to internal resistance. Specifically, at defibrillation events, the ∂V∂R trajectory dramatically increases from 10−12 to 10−8, indicating a fourth-order-of-magnitude enhancement in sensitivity. A model verification against experimental data shows an R2 value of 0.9506, indicating a high level of accuracy in predicting the Li-MnO2 cell terminal voltage. This modeling tool offers a comprehensive framework for effectively monitoring and optimizing battery life in AIMDs, therefore enhancing patient safety.

Enhanced cationic/anionic dyes removal in wastewater by green nanocomposites synthesized from acid-modified biomass and CuFe2O4 nanoparticles: Mechanism, Taguchi optimization and toxicity evaluation

This article addresses the nanoadsorption mechanisms of rhodamine B (RB), crystal violet (CV), and Congo red (CR) using acid-treated C.edulis (ATCE)/CuFe2O4 (ATCE@CuFe2O4) from an aqueous solution. The physical and chemical characterizations of nanobiomass were studied using different techniques. The specific surface areas of the ATCE and ATCE@CuFe2O4 composites were 15.88 and 337.81 m2/g, respectively, indicating a significant specific surface area of ​​the ATCE@CuFe2O4 nanocomposite. A number of functional groups were determined, which promote the binding of the dye to the adsorbent. The SEM also shows that the adsorbent has a homogeneous texture with deep voids and significant porosity, which likely explains the retention and binding of dye ions on the surface of the adsorbent. In fact, the Langmuir isotherm with a correlation coefficient of 99 % for CV, RB and CR, respectively, represents the most suitable model to explain the adsorption mechanism. The maximum adsorption amount is 666.6 mg/g for CV, 645.16 mg/g for RB and 434.71 mg/g for CR at 308 °K. The adsorption kinetic processes were predicted by the pseudo-second order kinetic model. The thermodynamic properties showed that the adsorption on ATCE@CuFe2O4 was possible and spontaneous. The ATCE@CuFe2O4 recycling and elimination CV, RB, and CR were 74.23 %, 72.75 %, and 67.84 %, respectively, after seven cycles. The design, modeling and optimization of the adsorption parameters were carried out using the Taguchi experimental design. The maximum removal efficiency of CV, RB and CR dyes in optimal operating conditions were 99.96, 98.29 and 97.76 %, respectively. Which at the optimal conditions of 1 g/L, 90 min, 20 mg/L, 298 °K, pH 10 for CV and RB dyes and 1 g/L, 90 min, 20 mg/L, 308 °K, pH 4 for CR. This research demonstrated the performance of ATCE@CuFe2O4 in bean seed germination test and its effectiveness in removing dyes from wastewater.

A Wide‐Temperature Adaptive Electrochromic Device Based on a Poly(vinyl alcohol)/Poly(acrylic acid) Gel Electrolyte

AbstractAs a promising energy‐saving technology, electrochromic technology has been widely investigated for practical applications. However, there is relatively few research about the applications under certain extreme climatic conditions since gel electrolytes often occur freezing and volatilizing. In this work, a poly(vinyl alcohol)/poly(acrylic acid) (PVA/PAA) gel electrolyte with good anti‐freezing and heat‐resistance performance is developed. Utilizing hydrated tungsten oxide (WO3·xH2O) and polyaniline (PANI) as electrochromic materials and the PVA/PAA gel as electrolyte, a WO3·xH2O/PVA/PAA‐ethylene glycol (EG)‐H2O/PANI electrochromic device (WO3·xH2O/PAEH/PANI ECD) is obtained, which can work efficiently at wide operating temperatures from −20 to 60 °C. The device has multiple reversible color changes, a large optical modulation of 66.2% at 600 nm, high coloration efficiency of 386.0 cm2 C−1, fast responses (with coloration/bleaching times of 1.3/1.1 s), as well as excellent cyclic stability (82.0% of the initial optical modulation is still retained after 2100 cycles). This work reveals the potential application prospects of PAEH gel electrolyte in practical extreme operating conditions.

Pressure‐Induced Capacity Recovery and Performance Enhancements in LTO/NMC‐LCO Batteries

AbstractLithium titanate oxide (LTO) batteries are a promising technology, particularly suitable for high‐power applications, owing to their inherent cyclic stability, fast charging capability, and superior safety. However, substantial gas generation and accelerated aging driven by the cathode remain substantial challenges. This study explores the mitigation of these aging mechanisms through the application of external mechanical compression. Continuous pressure of 0.3 MPa applied to pristine cells during cycling reduces capacity loss by 42% compared to unpressurized cells cycled under identical operating conditions. Applying short‐term pressure to aged cells leads to immediate capacity recovery, reclaiming up to 57% of the lost capacity. Subsequent cycling of these aged cells under continuous pressure demonstrates improved capacity retention. In contrast, intermittently applied transient pressure causes notable capacity fluctuations. This study reveals insights into aging and healing mechanisms influenced by external pressure, benefiting both first‐ and second‐life battery applications. Understanding these mechanisms is vital for enhancing performance and lifetime in battery packs, while the findings also highlight promising opportunities for capacity recovery in reused batteries.

Ceramic matrix composites for corrosion-resistant next-generation nuclear reactor systems: A conceptual review of enhancements in durability against molten salt attack

Ceramic matrix composites (CMCs) are emerging as a key material class for enhancing corrosion resistance in next-generation nuclear reactor systems, particularly in high-temperature molten salt environments. These environments, critical for advanced nuclear reactors such as molten salt reactors (MSRs), present severe challenges, including aggressive chemical attacks that degrade traditional structural materials over time. This conceptual review explores the development and application of CMCs to improve the durability and corrosion resistance of nuclear reactors exposed to molten salt attacks. CMCs, which consist of ceramic fibers embedded in a ceramic matrix, offer significant advantages, such as high thermal stability, mechanical strength, and improved corrosion resistance compared to conventional materials. This review examines how CMCs can be tailored to withstand harsh operational conditions, with a focus on the selection of ceramic phases, fiber-matrix interactions, and innovative fabrication techniques that enhance their protective capabilities. Key challenges addressed include the optimization of composite design to resist molten salt corrosion, the effects of temperature on the material properties, and the long-term stability of CMCs under extreme conditions. Advances in surface treatments, coatings, and the development of hybrid CMC systems are also discussed, highlighting their potential to further enhance durability. The review outlines the use of advanced characterization techniques, such as high-temperature corrosion testing and in situ microscopy, to evaluate CMC performance in molten salt environments. Additionally, it identifies knowledge gaps in current research, emphasizing the need for long-term studies on CMC behavior under realistic reactor conditions. This review concludes by proposing future research directions and technological advancements required to integrate CMCs into next-generation nuclear reactor designs, aiming to improve system reliability and operational safety.

Particulate fouling simulation in unit micropore using a hydrodynamically coupled Lagrangian framework

Predicting and mitigating pore clogging is challenging for the sustainable operation of water treatment systems. During transport and filtration through membrane micropores, buoyant contaminants in water gradually deposit on the surface, reducing the membrane's lifespan and performance, and sometimes completely blocking the pores. To alleviate the negative effects of fouling and to ensure sustainable operation, it is necessary to understand the fundamental mechanisms of fouling and to predict the probability of fouling formation under specific geometrical and material conditions. In this study, multiscale simulations are conducted to understand the fundamental mechanisms of particulate fouling at a microscopic level based on a Lagrangian framework incorporating inter-particle hydrodynamic interactions. We investigate both dead-end and cross-flow filtration, considering the direction of the feed stream relative to the unit micropore. The results elucidate the quantitative background of fouling history, which agrees with experimental findings. Depending on the level of hydrodynamic stress specific to the clog location and the nature of inter-particle interactions, deformation or resuspension of the clog is observed, competing with deposition, which leads to a two-way fouling history. Dominant deposition leads to micropore clogging, and to the best of the authors' knowledge, this is the first study to observe complete blockage and subsequent reopening. With this approach, the microscopic backgrounds between permanent and temporary pore blocking are distinguished. This study is expected to provide useful insights for controlling operational conditions to optimize anti-fouling performance in the transport and filtration through micropores.

Analysis of tooth root three-dimensional fatigue crack initiation, propagation, and fatigue life for spur gear transmission

The gears in aviation gear systems are susceptible to fatigue fracture due to high-speed, heavy-load, and load alternation operating conditions. Therefore, a numerical calculation model with multiple mesh positions loading form has been proposed in this paper to analyze the fatigue crack initiation and propagation behavior, and to estimate the fatigue life of these gears. The fatigue life of the gear is determined by separately calculating the fatigue crack initiation life and the fatigue crack propagation life. The tooth root fatigue stress and the fatigue initiation life are calculated by the multi-axial fatigue life prediction method with the Smith-Watson-Topper criterion and the critical plane method. Afterwards, the tooth root stress–strain field is calculated in Finite element (FE) software and the stress intensity factors of the crack tip are calculated in three-dimensional (3D) crack analysis software. Then, the tooth root fatigue crack propagation trajectory and the fatigue crack propagation life are obtained separately. Finally, the influence mechanism of the loading conditions, the geometry parameters of gears, and the initial crack positions on the tooth root fatigue crack initiation life, propagation life, and fatigue crack propagation trajectory are analyzed, and a tooth fatigue test bench is built for verifying the crack propagation trajectory.

Diclofenac recovery by means of using Emulsion Liquid Membranes: A possible role of surfactant in the mass transfer process

Emulsion Liquid Membranes (ELM) are a useful method for drug extraction in aqueous solution, as they allow their recovery in a highly concentrated acceptor phase after passing through a membrane phase with a high surface area. The success of this process depends on system formulation and operation conditions. One of the most important aspects in the formulation is the addition of a surfactant that stabilizes the system, which can also participate in the mass transfer process. Recently, resistance to mass transfer caused by the use of polymeric surfactants, such as Abil® EM 90, has been reported. To verify this phenomenon observed in previous studies, the effect of various factors on the recovery of diclofenac (DCF) dissolved in an aqueous medium was analyzed using the ELM technique. The study concluded that using a membrane consisting of Abil® EM 90 (2 % w/V), trioctylamine (5 % w/V), and dodecane, along with a stripping phase formed by NaOH (0.10 mol/L), results in successful DCF removal. When emulsified at 15,000 rpm for 10 minutes at room temperature, it is possible to remove over 99 % of DCF after 15 minutes and recover 18.45 % after 30 min. Furthermore, it was determined that the action of the surfactant is the main cause of the resistance to the passage of DCF to the internal phase and its retention in the organic phase, as had already been observed. This is probably due to strong intermolecular interactions between Abil ® EM 90 and DCF and the formation of a complex and stable interfacial structure, similar to a hydrated bilayer, which favors the rigidity of the interface and keeps the drug trapped. A better understanding of the role surfactants play in the system is essential for the future design of more efficient and stable ELMs. This knowledge will enable the adaptation of the technology to a wider range of processes, including drug recovery. As a result, the maximum benefits of this technology can be fully realized.

Enhancing Industrial Valve Diagnostics: Comparison of Two Preprocessing Methods on the Performance of a Stiction Detection Method Using an Artificial Neural Network

The detection and mitigation of stiction are crucial for maintaining control system performance. This paper proposes the comparison of two preprocessing methods for detecting stiction in control valves via pattern recognition via an artificial neural network (ANN). This method utilizes process variables (PVs) and controller outputs (OPs) to accurately identify stiction within control loops. The ANN was comprehensively trained using data from a data-driven model after processing them. Validation and testing were conducted with real industrial data from the International Stiction Database (ISDB), ensuring a practical assessment framework. This study evaluated the impact of two preprocessing methods on fault detection accuracy, namely, the D-value and principal component analysis (PCA) methods, where the D-value method achieved a commendable overall accuracy of 76%, with 86% precision in stiction prediction and a 66% success rate in nonstiction scenarios. This signifies that feature reduction leads to a degraded stiction detection. The data-driven model was implemented in SIMULINK, and the ANN was trained in MATLAB with the Pattern Recognition Toolbox. These promising results highlight the method’s reliability in diagnosing stiction in industrial settings. Integrating this technique into existing control systems is expected to enhance maintenance protocols, reduce operational downtime, and improve efficiency. Future research should aim to expand this method’s applicability to a wider range of control systems and operational conditions, further solidifying its industrial value.

Performance improvement of a centrifugal compressor for cogeneration

This work presents the performance improvement of a centrifugal compressor designed for cogeneration applications. The study involves modifications to geometric parameters, including impeller and diffuser configurations, to enhance pressure ratio, temperature ratio, and overall efficiency using computational fluid dynamics to verify the improvement of those parameters. The study has examined the compressor’s behaviour under surge, design, and choke conditions, showcasing improved homogeneity, reduced low-velocity zones, and an extended operating range. The compressor maps are presented to graphically observe the impact of each proposed modification, facilitating the analysis of each of these and evaluating the performance under various operating conditions. The final compressor configuration exhibits notable improvements in key performance metrics, with pressure ratio, temperature ratio, and efficiency showing increases of 1.753%, 1.640%, and 1.760%, respectively.

Improvement of heat transfer in microchannels using nanofluids by numerical contribution on a sinusoidal shaped dissipator

This research conducts an extensive numerical investigation into the enhancement of heat transfer in wavy-walled channels using aluminum oxide-water nanofluids. The study examines the impact of key parameters, including geometric aspects (wave spacing ratio: 0-1), operational conditions (Reynolds number: 5,000-20,000), and varying nanofluid concentrations (1-6% by volume). The results indicate that zero wave spacing combined with a nanofluid concentration of 4% provides the most favorable thermal performance, resulting in a heat transfer enhancement of up to 2.5 times when compared to conventional smooth channels. While the wavy channel geometries significantly improve heat transfer, they also impose additional pressure drops, revealing a critical trade-off between enhanced thermal performance and the increased pumping power required. The study highlights the balance necessary between maximizing heat transfer and maintaining operational efficiency, particularly in systems where space is limited. These findings contribute valuable insights to the design of heat exchange systems that demand both high thermal efficiency and compact dimensions, such as those used in electronics cooling or energy-efficient HVAC systems. Additionally, the study explores the influence of the Reynolds number on both thermal and hydrodynamic behavior within the wavy channels, providing a thorough performance evaluation across different operational ranges. The conclusions drawn from this investigation offer practical guidance for optimizing heat exchanger designs, taking into account both thermal resistance and overall system performance under varying conditions.

Harnessing Oxygenic Photosynthetic Microorganisms for Sustainable Wastewater Treatment Systems: A Comprehensive Review

Aquaculture systems globally face significant environmental challenges, particularly concerning wastewater management. This review explores the innovative application of oxygenic photosynthetic microorganisms (OPhMs), specifically microalgae and cyanobacteria, as a sustainable solution for wastewater treatment within these systems. OPhMs offer a dual role in wastewater treatment by removing harmful pollutants such as nitrogen, phosphorus, and heavy metals, while simultaneously improving water quality through oxygenation. We evaluate the integration of OPhMs into existing aquaculture operations, considering key factors such as system design, operational conditions, and economic viability. Additionally, we discuss the potential of algal biomass as a secondary resource for producing biofuels, animal feed, and other bio-products, contributing to the circular economy model. Our findings highlight the ability of OPhM systems to significantly reduce the ecological footprint of aquaculture while recovering valuable resources. However, challenges such as process stability, especially under low-temperature conditions, and the efficiency of biomass harvesting, require further research. This study provides a comprehensive framework for future development and optimization of OPhM-based wastewater treatment systems to improve the sustainability of aquaculture operations.

Comprehensive Experimental Assessment Of Unsteady Pressure And Heat Flux In A Small-Core Turbine Over-Tip Shroud

Abstract For novel high-speed small core turbines, with tip clearance below 0.5mm, even small blade-to-blade tip clearance variation is significant. The assessment of these complex flows is pertinent to the design of the next generation of small-core turbines. This manuscript provides a thorough experimental analysis of the shroud?s unsteady heat flux and static pressure in a small-core squealer-tip blade turbine. Atomic Layer Thermopile sensors and fast response pressure transducers were used to perform high-frequency acquisition at 2MHz around the 51% axial blade chord on the shroud. Measurements were taken at engine-representative conditions at several operational conditions and tip clearances. The signals were phase-locked averaged over the revolution period and synchronized to identify individual blade and row signatures. The linear relationship between the rotational tip Reynolds and the static pressure ratio across the blade tip reveals the transition point to reverse over-tip flows. Total heat flux is decomposed into different steady and unsteady heat flux contributions. It is demonstrated that the adiabatic wall temperature governs the unsteady heat flux and contributes to one third of the total surface heat flux. A linear trend was observed between the unsteady heat flux and the tip clearance measured at the pressure and suction side rims. Similar trends were observed between the local heat flux and the pressure ratio across the tip. A comparison with CFD predictions highlights some limitations on resolving the detached and secondary flows, evidencing the necessity of complementary experimental data.

Simulation of Hybrid PV Solar System with Fuel Cell in MATLAB Simulink

This paper discusses the simulation of a fuel cell hybrid solar photovoltaic system in MATLAB Simulink. To achieve the stated objective, it is proposed to dynamically model a hybrid system using Simulink to analyze its performance and optimize its operation, improving the efficiency and stability of the power supply under different operating conditions, then the dynamic simulation model employs energy conversion equations of the solar PV panel and the fuel cell to meet the electrical load requirements, where the solar panel uses solar radiation and ambient temperature, while the fuel cell produces electricity by electrolysis of water, which separates hydrogen from oxygen. In the first graph, the power at load increases sharply at 0.5 seconds and stabilizes between 0.5 and 1.5 seconds, with a sharp drop at 1.6 seconds. In the second graph, PV power increases at 0.5 seconds, reaching 80 kW between 0.5 and 1.5 seconds, dropping to zero at 1.6 seconds. The fuel cell shows similar behavior. These results indicate a coordinated operation for a stable power supply to the load, responding efficiently to changes in demand and generation.

Localized Bearing Fault Analysis for Different Induction Machine Start-Up Modes via Vibration Time–Frequency Envelope Spectrum

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time–frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes.