Operational Scenarios Research Articles

Control of Power Converters in Micro Grids

Owing to several operational drawbacks in the existing power management schemes for interlinked AC-DC micro grids which are either concerned about only on sharing power or regulation of voltage but not on both, so as to overcome these issues this paper is proposed. This proposed autonomous power management scheme will consider the specific loading condition of the DC micro grid before importing power from the interlinked AC microgrid. This strategy not only enables voltage regulation in the DC micro grid but also reduces the number of converters in operation which will in turn reduces the power transfer losses. Theproposed scheme is fully self governed as it holds on the plug-nplay features for generators and tie-converters. The performance of the proposed control scheme has been validated under different operating scenarios. The results reveal the usefulness of the proposed scheme in managing the shortcoming of power in the DC microgrid efficiently and autonomously, on the other hand maintaining the better voltage regulation in the DC micro grid. In this project a DG interfacing network and its control also to be simulated to analyze the system stability The results are verified through MATLAB/SIMULINK environment.

Investigation of fast ion effects on core turbulence in FIRE mode plasmas

Further investigation of fast ion effects on turbulence and transport in the fast ion regulated enhancement (FIRE) mode discharge (Han et al 2022 Nature 609 269–275) was performed in this work as a continuation of a previous study (Kim et al 2023 Nucl. Fusion 63 124001) that showed that the dominant turbulence suppression mechanism by fast ions is the dilution effect in the FIRE mode discharge. The current study includes (i) the impact of the fast ion relevant mode observed in the simulation of thermal energy flux, (ii) dilution effects by fast ions compared to dilution effects by other species, and (iii) fast ion effects on electron-scale turbulence. First, nonlinear gyrokinetic simulation results show that turbulence is significantly suppressed even without the fast ion relevant mode, indicating that the impact of this mode on thermal transport is not significant in this discharge. Second, further analysis on the dilution effects shows the three following results: Turbulence is not completely suppressed by the reduced main ion density fraction effect due to impurities; the reduction in energy flux can be limited by a certain impurity mode that is destabilized by a high impurity density gradient from adjusting the main ion density gradient; electrons can contribute to turbulence suppression through the main ion density gradient change, although this effect is less significant compared to other species. Third, we observe that two fast ion effects can influence the linear growth rate of the electron-scale turbulence mode. The growth rate decreases by an increase in β∗(≡(−8π/B2)dp/dr) and increases by dilution effects, suggesting that fast ion effects on electron-scale turbulence can differ depending on the operation scenario, such as the fast ion fraction. The comprehensive analysis performed in this study can enhance our understanding of fast ion physics, required for burning plasma operation in the future.

Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

AbstractIn the context of the dual carbon goal strategy, the proportion of new energy generation has increased annually, large‐scale renewable energy integration has been achieved, and the intermittent and uncertain operating characteristics pose an enormous challenge to the complete and stable operation of an integrated energy system (IES), promoting the complexity of IES optimization models. To increase the stability and accuracy of the system and improve the operation efficiency of the system, a Gaussian mixture model is used to fit the probability distribution of wind power and the prediction errors of the photovoltaic output. In addition, the expected maximization method is used to solve a model with hidden variables, the results show that the expectation maximization algorithm can improve the fitting accuracy, reduce the error caused by the subjective setting of the initial value, and make the fitting accuracy of the wind‐solar power prediction error greater. Then, to solve the problem in which the low resolution of the day‐ahead scheduling time leads to large errors, day‐ahead, day‐to‐day multitime scale operation optimization model takes into account the comprehensive demand response according to the differences in wind and solar output and load forecasting accuracy. Finally, simulation validation is conducted in multiple scenarios, and a comparative analysis is performed for single and multitime scale comprehensive demand response scenarios. The simulation results show that compared with the optimization results of no demand response day, the optimization results of this model reduce the total cost by 23.21%, carbon emissions by 13.98%, purchased electricity by 13.87%, and purchased gas by 19.31%, effectively improving the use of gas turbines in the system. The multitime scale scheduling strategy increases the overall operating cost of the IES, modifies the scheduling results, and agrees with real operating scenarios.

Preliminary Envelope for Large Transport Aircrafts Operating with Non-Primary Fuels AVGAS, MOGAS and F76-Dieso

This study explores the operational implications and safety considerations of using non-primary fuels—AVGAS, MOGAS, and F76 Dieso—in military transport aircraft, against the backdrop of standard aviation fuels. Through an analysis of fuel properties such as vapor pressure, density, viscosity, freeze temperature, water solubility, and thermal conductivity, this work outlines the operational envelopes for the mentioned non-primary fuels, highlighting the temperature and altitude limitations inherent to their use. The evaluation underscores the necessity of relevant testing, certification, and adherence to operational guidelines and constrains to ensure aircraft safety and reliability when standard fuels are unavailable, and hence, non-primary fuels may be required in special missions under emergency. Key findings include the specific altitude and temperature limitations for AVGAS and MOGAS to prevent fuel freezing and boiling, as well as the operational challenges posed by F76 Dieso due to its higher density and viscosity. The study also addresses the importance of managing water content in the fuel system, the flammability range of the non-primary fuels, and the considerations for fuel mixing to maintain aircraft performance and safety standards. This analysis aims to enhance the understanding of non-primary fuel usage in military transport aircraft, providing insights for system design, performance assessment, and the development of operational procedures to support military aviation in diverse operational scenarios.

Concept Drift Mitigation in Low-Cost Air Quality Monitoring Networks.

Future air quality monitoring networks will integrate fleets of low-cost gas and particulate matter sensors that are calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of data quality loss in machine learning application operational scenarios. The present study focuses on addressing the calibration model update of low-cost NO2 sensors once they are triggered by a concept drift detector. It also defines which data are the most appropriate to use in the model updating process to gain compliance with the relative expanded uncertainty (REU) limits established by the European Directive. As the examined methodologies, the general/global and the importance weighting calibration models were applied for concept drift effects mitigation. Overall, for all the devices under test, the experimental results show the inadequacy of both models when performed independently. On the other hand, the results from the application of both models through a stacking ensemble strategy were able to extend the temporal validity of the used calibration model by three weeks at least for all the sensor devices under test. Thus, the usefulness of the whole information content gathered throughout the original co-location process was maximized.

Improved Incremental Conductance MPPT Technique Designed to Addressing Drift Problem in a Photovoltaic System

Many researchers have concentrated on improving the efficiency of photovoltaic (PV) systems by optimizing control mechanisms aimed at extracting the maximum power from PV panels. The variable step size incremental conductance control (VSS-INC) technique has been the primary focus of the majority of these studies. However, this strategy faces challenges in the form of drift when confronted with swift changes in solar irradiation, temperature variations, and resistive load fluctuations. In addressing this issue, the present study proposes an innovative VSS-INC method. It is suggested to utilize a buck-boost converter as an impedance adaptor in achieving maximum power point tracking (MPPT). This involves controlling the duty cycle and aligning its input with that of the PV module. The efficiency of the suggested approach was evaluated through MATLAB software, and the outcomes were compared with traditional algorithms across various operational scenarios. Simulation results demonstrate the notably satisfactory and efficient performance of the proposed method when compared to conventional approaches.

Integrated analysis of plasma rotation effect on HL-3 hybrid scenario

Abstract The hybrid scenario, which has good confinement and moderate MHD instabilities, is a proposed operation scenario for international thermonuclear experimental reactor (ITER). In this work, the effect of plasma rotation on the HL-3 hybrid scenario is analyzed with the integrated modeling framework OMFIT. The results show that toroidal rotation has no obvious effect on confinement with a high line averaged density of n bar ∼ 7 × 1019 m−3. In this case, the ion temperature only changes from 4.7 keV to 4.4 keV with the rotation decreasing from 105 rad/s to 103 rad/s, which means that the turbulent heat transport is not dominant. While in the scenarios characterized by lower densities, such as n bar ∼ 4 × 1019 m−3, turbulent transport becomes dominant in determining heat transport. The ion temperature rises from 3.8 keV to 6.1 keV in the core as the rotation velocity increases from 103 rad/s to 105 rad/s. Despite the ion temperature rising, the rotation velocity does not obviously affect electron temperature or density. Additionally, it is noteworthy that the variation in rotation velocity does not significantly affect the global confinement of plasma in scenarios with low density or with high density.

Swarm of Drones in a Simulation Environment—Efficiency and Adaptation

In the swiftly advancing field of swarm robotics and unmanned aerial vehicles, precise and effective testing methods are essential. This article explores the crucial role of software-in-the-loop (SITL) simulations in developing, testing, and validating drone swarm control algorithms. Such simulations play a crucial role in reproducing real-world operational scenarios. Additionally, they can (regardless of the type of application) accelerate the development process, reduce operational risks, and ensure the consistent performance of drone swarms. Our study demonstrates that different geometrical arrangements of drone swarms require flexible control strategies. The leader-based control model facilitates coherent movement and enhanced coordination. Addressing various issues such as communication delays and inaccuracies in positioning is essential here. These shortcomings underscore the value of improved approaches to collision avoidance. The research described in this article focused on the dynamics of drone swarms in a simulated context and emphasized their operational efficiency and adaptability in various scenarios. Advanced simulation tools were utilized to analyze the interaction, communication, and adaptability of autonomous units. The presented results indicate that the arrangement of drones significantly affects their coordination and collision avoidance capabilities. They also underscore the importance of control systems that can adapt to various situations. The impact of communication delays and errors in positioning systems on the required distance between drones in a grid structure is also presented. This article assesses the impact of different levels of GPS accuracy and communication delays on the coordination of group movement and collision avoidance capabilities.

The factors affecting the performance of the tunnel wall drilling task and their priority

Clarifying the relationship between the man–machine environment and its impact on the tunnel wall drilling task performance (TWDTP) is crucial for enhancing the task performance. Based on a questionnaire survey, indicators of the man–machine environment that affect the TWDTP were proposed in this study, and exploratory factor analysis and a structural equation model were employed to examine the potential factors influencing the task performance and their degrees of influence. By comparing the discrepancy between the perceived performance and importance, the satisfaction of potential factors was evaluated, and the priority order for optimizing these factors was determined by considering the degree of influence and dissatisfaction. The results of survey data analysis based on actual tunnel drilling operation scenarios indicated that tools had the greatest impact on the TWDTP, followed by the quality of the physical environment, while human factors had the least influence on the task performance. Convenient functional maintenance is the key to improving the TWDTP, along with enhancing the quality of the working environment. Once these main aspects are optimized, it is important to consider additional factors such as availability of spare tools, efficient personnel organization, man–tool matching, and safety and health assurance. This research approach provides significant guidance in understanding the relationships between the man–machine environmental factors affecting the performance of complex engineering tasks and identifying key influencing factors, thus providing essential insights for optimizing the TWDTP.

Intelligent fault diagnosis under imbalanced multivariate working conditions leveraging dynamic unsupervised domain adaptation with sample and margin regularization

Utilizing unsupervised domain adaptation for intelligent fault diagnosis (IFD) has demonstrated significant potential for ensuring the security of machinery systems. Nonetheless, the inherent imbalance attribute of collected data affects the performance of diagnostic model. Especially, for machines working under varied conditions, the acquired unlabeled data frequently exhibits diverse degrees of distributional deviations, thus further undermining the transferable model’s generalization capability. To address this challenge, we introduce a method termed Dynamic Unsupervised Imbalanced Domain Adaptation (DUIDA) for IFD. Employment of class rebalancing and label-dependent margin regularization strategies optimizes the selection of decision boundaries which counteract the distributional deviations introduced by the imbalance. In addition, by integrating a dynamic weighting mechanism, encompassing both adversarial-based and MMD-based domain adaptation, our model becomes versatile across varied UIDA tasks, assigning higher weights to fundamental faulty features. Finally, our empirical analyses on two faulty bearing datasets substantiate the efficacy and superior performance of the proposed framework across diverse operational scenarios.

EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management

Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, and maintenance. However, the data available need to be improved and enriched, partly due to their high dimensionality and low reliability, and the lack of appropriate data analysis and processing tools for such systems. This paper presents a visual analytics-based platform for WWTP that allows users to identify relationships among data through data inspection. The results show that the tool developed and implemented for a full-scale WWTP allows operators to construct machine learning (ML) models for water quality and other water treatment process variables. Consequently, analyzing and optimizing plant operation scenarios can enhance key variables, including energy, reagent consumption, and water quality. This improvement facilitates the development of a more sustainable WWTP, contributing to a beneficial environmental impact. Domain experts validated the variables influencing the created ML models and proved their appropriateness.

Numerical Simulation of the Dovetail Tee and Hydraulic Optimization of the Height Difference for Pipeline in a Liquefied Natural Gas Filling Station

Certain configurations of liquefied natural gas refueling stations exhibit a deficiency in managing boil-off gas. Furthermore, the ill-conceived linkage between the submersible pump and the gas storage tank pipeline leads to impeded natural gas transmission. This study employed the computational fluid dynamics (CFD) methodology to scrutinize the hydrodynamic attributes of the T-type tee and dovetail tee configurations implemented in the pipeline design connecting the submersible pump and storage tank in a liquefied natural gas (LNG) filling station across diverse operational scenarios. The T-type tee induces detachment of the primary flow from the inner wall due to inertial forces, which results in vortex formation and heightened resistance, accompanied by increased energy dissipation. The transition of the rounded inner wall of the dovetail tee results in the reduction of eddy current generation and a smaller separation zone, thus minimizing resistance and energy loss. The maximum static differential pressure between the inlet and outlet of the dovetail tee is reduced by 52.52% compared to that of the T-type tee. In practical engineering applications, the use of dovetail tees leads to a reduction in the height difference for the pipeline by 17.58%, resulting in more uniform and stable flow rates and pressures in the flow field. These improvements contribute to engineering efficiency and environmental sustainability and are particularly evident in the design of LNG filling stations.

Wind Turbine Tower State Reconstruction Method Based on the Corner Cut Recursion Algorithm

This study introduces an innovative approach for the reconstruction of wind turbine tower states using a tangential recursion algorithm. The primary objective is to enable real-time monitoring of the operational condition of wind turbine towers. The proposed method is rooted in strain–load theory, which enables the accurate identification of tower load states. The tangential recursion algorithm is utilized to translate the strain data acquired from strategically placed sensors into reconstructed point positions. The subsequent refinement of these positions incorporates considerations of torsional loads and geometric deformations, culminating in the comprehensive and precise reconstruction of the tower’s deformation behavior. Through the use of the OpenFAST V8 simulation software, a thorough analysis is conducted to investigate the load and deformation characteristics of the NREL 5 MW wind turbine tower across diverse operational scenarios. Furthermore, the load conditions corresponding to rated operating circumstances are applied to a finite element model constructed with the lumped mass method. The identification of tower load states and the comprehensive reconstruction of deformation patterns are realized through the extraction of strain data from critical points in the finite element model. The credibility and accuracy of the proposed method are rigorously evaluated by juxtaposing the identification and reconstruction outcomes with the results derived from the OpenFAST simulations and finite element analyses. Notably, the proposed method circumvents the requirement for external auxiliary calibration equipment for the tower, rendering it adaptable to a broader spectrum of operational contexts and making it consistent with unfolding trajectories in wind power advancement.

Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions to establish technical and ecological indicators for a diesel engine across several operational scenarios. VALLUM01, a novel tool, has been created with a user-friendly interface for data input/output, intended for the purposes of testing and prediction. There was a comprehensive collection of 12 input parameters and 10 output parameters that were identified as relevant and sufficient for the objectives of training, validation, and prediction. The proper value ranges for transforming into fuzzy sets for input/output to an ANN were found. Given that the ANN’s training session comprises 1,000,000 epochs and 1000 perceptrons within a single-hidden layer, its effectiveness can be considered high. Many statistical distributions, including Pearson, Spearman, and Kendall, validate the prediction accuracy. The accuracy ranges from 96% on average, and in some instances, it may go up to 99%.

Electron ITB formation in EAST high poloidal beta plasmas under dominant electron heating

Plasma confinement and transport in tokamaks play a crucial role in the development of high poloidal beta steady-state operation scenarios. Therefore, it is very important to study the relevant mechanism of internal transport barriers (ITBs), which can help plasmas to obtain better confinement in order to achieve higher fusion gain. This paper mainly introduces the analysis of the characteristics of electron heat transport of discharges with ITB in high βP operation regime in Experimental Advanced Superconducting Tokamak (EAST). Based on the statistical analysis of stable discharges with βP > 1.5, it is found that there is an obvious bifurcation of the normalised electron temperature gradient (ETG) ( R/LTe ) in the range of βP = 2–2.2. Then the discharges of lower βP ( βP < 2, where the value of βP is below the bifurcation threshold) and of higher βP ( βP > 2.2, where the value of βP is above the threshold) were selected for analysis. The diagnostic data provided by Thomson scattering, x-ray crystal spectrometry and charge exchange recombination spectroscopy are used to provide reliable parameter profiles and then combined with the data of external magnetic probe measurements and the polarisation interferometer diagnosis system to fully reconstruct the balance. A relevant plasma current calculation model is used to calculate and analyse the current density profiles and power deposition, and then the transport analysis is carried out. Interestingly, in the higher βP discharges, it is found that the turbulence intensity provided by the CO2 laser collective scattering system gradually decreases and the normalised ETG R/LTe gradually increases. It is also found that the electron heat transport coefficient decreases in the discharge with higher βP and the growth rate of the electron-scale turbulence calculated by transport gyro-Landau fluid (TGLF) is significantly reduced. Meanwhile, similar conclusions are also obtained in the discharges when the βP is further increased.

Biogas reforming as a sustainable solution for hydrogen production: Comparative environmental metrics with steam-methane reforming and water electrolysis in the Portuguese context

This study delves into the dynamics of hydrogen production, with a specific focus on biogas reforming (BGSMR) for hydrogen generation. It compares the environmental impact of this solution with hydrogen production from natural gas-steam reforming (NGSMR) and commercial electrolysis in the Portuguese context. Various metrics, including carbon footprint, water depletion, energy utilization, and waste valorization are employed for a comprehensive comparison. The assessment explores the impact of operational parameters and different off-gas combustion scenarios, incorporating water recycling practices. Due to challenges in obtaining detailed data on the actual reforming process, the study relies on process simulation techniques, primarily using DWSIM. Commercially available data for water electrolysers were used for comparison. In the context of decarbonizing power systems, hydrogen from water electrolysis emerges as a competitive option only in a scenario where the power system is 100% reliant on renewable sources, particularly with respect to the carbon footprint metric. Biogas systems, characterized by near-zero carbon emissions, stand out as a favourable option from the near future to the long run. This research contributes valuable insights into the dynamics of hydrogen production, shedding light on environmentally viable alternatives across a range of power system scenarios.

Empirical Insights into 5G Deployments in Highway Operational Environments and Comparative Performance with 4G

Despite the promising benefits, the integration of 5G, particularly through Non-Standalone (NSA) architectures that rely on existing 4G infrastructures, introduces challenges in maintaining optimal radio network performance and service quality. This study evaluates 4G and 5G radio network performance through empirical field trials across highway operational scenarios, a domain less scrutinized compared with urban environments. By conducting simultaneous measurements and comparing all available networks, this research focuses on dissecting the performance of critical quality indicators to gauge the intricacies of radio network behavior in high-speed travel conditions. Our findings illuminate distinct behavioral patterns, highlighting the unique challenges and optimization opportunities in these scenarios. Variability in signal strength across the highway routes underscores the influence of geography and infrastructure on coverage, while the relative stability in signal quality suggests the networks’ capability to maintain signal quality amid fluctuating strength. Interference results indicate effective management of signal interference, crucial for high-quality links, whereas latency and throughput metrics highlight a lag behind anticipated goals for reduced latency but promising data rates. This study not only showcases the variances in network quality and performance, thus pinpointing areas for operator-specific enhancements, but also emphasizes the comparison between the robustness of 4G infrastructures and the challenges in optimizing 5G networks.

Improvement of Efficiency of Inverters in Hydro Photovoltaic Power Station with Particle Swarm Optimization

In the sparsely populated areas without electricity, the hydro photovoltaic power station is a feasible solution for electricity supply. The strategy of distributing the power among the inverters is critical to the efficiency of them. The conventional distributing strategies result in low efficiency of the inverters. In order to improve the efficiency, this paper analysed the loss and efficiency characteristics of the inverter and expressed the power distributing problem as an optimal control problem minimizing the total loss for the inverters. The optimal control problem was solved with particle swarm optimization and the efficiency optimum power distribution strategies in three operation scenarios were obtained. The quantitative analysis method was adopted to evaluate the effect of the efficiency optimum power distribution strategies. The total efficiency of the inverters with the optimal strategies and the conventional strategies were calculated respectively. The optimal distribution strategies were compared quantitatively with conventional power distribution strategies on the basis of the efficiency. The results demonstrated the validity of the strategies obtained in this paper in improving the total efficiency of the inverters.

Improving electronics stability via covalent-linked interfaces for ultra-robust pressure sensing array

There is an urgent need for developing ultra-stable sensors with robust interfacial connections to promote the progress of intelligent technology. Here we propose a strategy for fabricating porous sensors with covalent-linked interfaces, which significantly improve the device’s mechanical and environmental stability. By endowing the polymer reactive siloxane group for further dehydration and condensation process, the modified polymer can on one hand form interface covalent Ti-O-Si bonds with hydroxyl-rich MXene nanosheets for improved interfacial adhesion, and on the other hand, form internal self-condensed Si-O-Si bonds for strengthened mechanical resilience. Owing to the firmly undetached conductive materials, the resulting sensor can maintain stable signal output, and survive harsh conditions including repeated mechanical compression and rigorous water and acid washing. These features make the covalent-linked sensor feasible for human sitting posture monitoring over prolonged periods and spatial distribution detection in various complicated operation scenarios.

A Multifaceted Approach for Enhancing Anomaly Detection in Industrial Systems with Adaptive Synthetic Sampling and Machine Learning Evaluation

Anomalies in industrial systems refer to deviations from expected behaviour, indicating potential malfunctions, faults, or security breaches. These anomalies can disrupt operations, compromise safety, and lead to costly downtime, making their detection and mitigation critical for industrial reliability and security. This research addresses the imperative task of enhancing anomaly detection within industrial systems by integrating a multifaceted approach encompassing various techniques and methodologies. A primary challenge in anomaly detection, the imbalanced distribution of data, is tackled by employing Adaptive Synthetic Sampling (ADASYN), which dynamically generates synthetic samples for minority classes. This effectively mitigates the impact of class imbalance and substantially enhances detection performance. Additionally, filter-based feature selection methods are utilized, incorporating statistical measures like the Chi-Square Test and ANOVA F-Value to identify pertinent features essential for anomaly detection. The study further delves into supervised anomaly detection approaches, leveraging machine learning algorithms such as Support Vector Machines (SVM) and Gradient Boosting (GB) to differentiate between normal and anomalous activities. These algorithms undergo rigorous evaluation within a Virtual Testing Environment (VTE) simulator, meticulously replicating industrial processes for comprehensive assessment. By leveraging the VTE simulator, this research ensures thorough evaluations across diverse operational scenarios, thereby fortifying anomaly detection systems and advancing the security of industrial systems. The research evaluates anomaly detection models for industrial systems using performance metrics like precision, recall, F1-score, and AUC-ROC. It compares Support Vector Machine (SVM) and Gradient Boosting (GB) models, finding GB outperforms SVM in discrimination ability. Both models show effective anomaly detection with minimal false positives and false negatives. A comparative analysis of key performance metrics aids in selecting the most suitable approach for enhancing system reliability and safety.