Power Components Research Articles

Optimal Configuration Strategy of Soft Open Point in Flexible Distribution Network Considering Reactive Power Sources

The intelligent soft open point (SOP) has powerful power flow regulation capabilities in the distribution network. If applied to the distribution network, it can flexibly cope with the output uncertainty of unmanageable distributed energy sources. However, considering the investment, operation, and maintenance costs, as well as the assistance of reactive power equipment, the site selection and capacity determination of SOP have become an urgent problem to be solved. This article proposes the optimal configuration strategy of SOP in a flexible interconnected distribution network, taking into account the features of distributed generation and reactive power sources. Firstly, based on the unpredictability of DG output, this paper uses improved sensitivity analysis to determine the optimal SOP installation location. Subsequently, with the optimization objective of minimizing the annual cost of the distribution network, this paper considers the characteristics of DGs, CBs, and OLCTs and uses second-order cone programming to optimize and solve SOP capacity under constraints such as trends. Finally, in the enhanced IEEE 33-node distribution system model, the effects of different scenarios on node voltage, reactive power components, and SOP location and capacity are compared.

Topology optimization of the front loader’s working equipment

BACKGROUND: Topological optimization is widely used in aircraft construction and architecture, but is still of limited use in heavy machine building. At the same time, the production of parts, frames and other structures is often carried out by CNC (computer numerical control) plasma cutters. This makes it possible to produce flat parts of almost any complexity, including the use of topology optimization methods. Moreover, there is no need to use additive technologies to create three-dimensional structures. AIMS: The purpose of the study is to reduce the weight of the power components of the front end loader's working equipment without loss of rigidity and strength, as compared with traditional designs, and to explore the possibilities of topological optimization. MATERIALS AND METHODS: We used the DM-30 loader as the base machine. We converted its work equipment into a set of flat shape design volumes, to which the topological optimization methods of Autodesk Inventor Professional (AIP) were applied. As the structural steelwork of working equipment is subject to load in different directions during operation, we used a method of sequentially generating part forms for each design position and then synthesizing all forms into a single object. We determined the forces acting on the work equipment components by dynamically modeling the design positions for the base machine. It allows us to study the most operating situations. RESULTS: As a result, we reduced the weight of the front loader by 36%, while maintaining the same strength characteristics. CONCLUSIONS: The methodology proposed allows the use of simple topological optimization modules and produces up to 40% less metal-intensive spatial structures.

Cooling Methods for a Typical Printed Circuit Board Assembly in Spacecraft: Simulation and Experiment

In this study, cooling methods for a typical spacecraft circuit board assembly are investigated. The power dissipation of the assembly is more than 100 W, and the max heat dissipation of a component is 16 W, making it very difficult to cool the assembly. According to the packaging characteristics and heat dissipation of the components on the circuit board, cooling methods such as potting brackets, cooling springs, and cooling blocks are used, and the effects of various cooling methods are analyzed. Through simulation and experimental research, it is proven that the power components in the printed circuit board assembly meet the requirements of temperature derating, which provides a reference for the thermal design of spacecraft electronic equipment.

Association of ventilation volumes, pressures and rates with the mechanical power of ventilation in patients without acute respiratory distress syndrome: exploring the impact of rate reduction

SummaryIntroductionHigh mechanical power is associated with mortality in patients who are critically ill and require invasive ventilation. It remains uncertain which components of mechanical power – volume, pressure or rate – increase mechanical power the most.MethodsWe conducted a post hoc analysis of a database containing individual patient data from three randomised clinical trials of ventilation in patients without acute respiratory distress syndrome. The primary endpoint was mechanical power. We used linear regression; double stratification to create subgroups of participants; and mediation analysis to assess the impact of changes in volumes, pressures and rates on mechanical power.ResultsA total of 1732 patients were included and analysed. The median (IQR [range]) mechanical power was 12.3 (9.3–17.1 [3.7–50.1]) J.min‐1. In linear regression, respiratory rate (36%) and peak pressure (51%) explained most of the increase in mechanical power. Increasing quintiles of peak pressure stratified on constant levels of respiratory rate resulted in higher risks of high mechanical power (relative risk 2.2 (95%CI 1.8–2.6), p < 0.01), while decreasing quintiles of respiratory rate stratified on constant levels of peak pressure resulted in lower risks of high mechanical power (relative risk 0.2 (95%CI 0.2–0.3), p < 0.01). Mediation analysis showed that a reduction in respiratory rate, with the increase in tidal volume, partially mediates an effect of reduction in mechanical power (average causal mediation effect ‐0.10, 95%CI ‐0.12 to ‐0.09, p < 0.01), but still with a direct effect of tidal volume on mechanical power (average direct effect 0.15, 95%CI 0.11–0.19, p < 0.01).DiscussionIn this cohort of patients without acute respiratory distress syndrome, pressure and respiratory rate were the most important determinants of mechanical power. The respiratory rate may be the most attractive ventilator setting to adjust when targeting a lower mechanical power.

Asymmetric structure endows thermal radiation and heat conduction of graphene film for enhancing dual-mode heat dissipation

To prevent heat accumulation generated by high power components inside spacecraft, it is necessary to encapsulate a heat dissipation material on the exterior surface of the spacecraft to homogenize local...

Adaptive energy management strategy for a DC microgrid using intelligent Controller

ABSTRACT This paper presents the enhanced operation of a standalone DC microgrid (DCMG) consisting of PV, Fuel cell (FC), battery and supercapacitor (SC). Intermittent variations in PV power and loads are a common problem in standalone systems. Therefore, a novel energy management and control strategy based on hybrid fuzzy and proportional-integral controllers (H-FPI) is proposed to effectively distribute power between the battery and the SC. FLCs are designed based on Mamdani fuzzy inference system (FIS) to generate a duty cycle to control bidirectional DC–DC controllers (BDCs) associated with battery and SC. The proposed work utilizes a battery and SC as a hybrid energy storage system (HESS), while the reference current for controlling the charge through them is generated via a filtration-based technique. This optimization enhances battery longevity by diverting high-frequency and uncompensated power components toward the SC. Digital simulations substantiate the effectiveness of this energy management and control strategy. The proposed controller demonstrates its efficacy across all considered scenarios, with maximum undershoot and overshoot limited to 3.79% and 3.65%, respectively, well within the acceptable range prescribed by IEEE std. 519–1992 (±5%). Furthermore, the results are compared with conventional controller and robustness of H-FPI controller is tested against the benchmarks such as settling time, overshoot, and undershoot in DC bus voltage ( V DC ) .

Bi2WO6/Nb4C3Tx/dsDNA bio-nano-engineered composite as a powerful biosensor component to diagnose and monitor pemetrexed in pharmaceutical and environmental fluids

Bi2WO6/Nb4C3Tx/dsDNA bio-nano-engineered composite as a powerful biosensor component to diagnose and monitor pemetrexed in pharmaceutical and environmental fluids

Aperiodic neural activity distinguishes between phasic and tonic REM sleep.

Traditionally categorized as a uniform sleep phase, rapid eye movement sleep exhibits substantial heterogeneity with its phasic and tonic constituents showing marked differences regarding many characteristics. Here, we investigate how tonic and phasic states differ with respect to aperiodic neural activity, a marker of arousal and sleep. Rapid eye movement sleep heterogeneity was assessed using either binary phasic-tonic (n = 97) or continuous (in 60/97 participants) approach. Slopes of the aperiodic power component were measured in the low (2-30 Hz, n = 97) and high (30-48 Hz, n = 60/97) frequency bands with the Irregularly Resampled Auto-Spectral Analysis applied on electroencephalography. Rapid eye movement amplitudes were quantified with the YASA applied on electrooculography (n = 60/97). The binary approach revealed that the phasic state is characterized by steeper low-band slopes with small effect sizes and some topographical heterogeneity over datasets. High-band aperiodic slopes were flatter in the phasic versus tonic state with medium-to-large effect sizes over all areas in both datasets. The continuous approach confirmed these findings. The temporal analysis within rapid eye movement episodes revealed that aperiodic activity preceding or following EM events did not cross-correlate with eye movement amplitudes. This study demonstrates that aperiodic slopes can serve as a reliable marker able to differentiate between phasic and tonic constituents of rapid eye movement sleep and reflect phasic rapid eye movement event intensity. However, rapid eye movement events could not be predicted by preceding aperiodic activity and vice versa, at least not with scalp electroencephalography.

Limitations of Inverters by Weight of Power Components

Limitations of Inverters by Weight of Power Components

Aperiodic and oscillatory systems underpinning human domain-general cognition

Domain-general cognitive systems are essential for adaptive human behaviour, supporting various cognitive tasks through flexible neural mechanisms. While fMRI studies link frontoparietal network activation to increasing demands across various tasks, the electrophysiological mechanisms underlying this domain-general response to demand remain unclear. Here, we used MEG/EEG, and separated the aperiodic and oscillatory components of the signals to examine their roles in domain-general cognition across three cognitive tasks using multivariate analysis. We found that both aperiodic (broadband power, slope, and intercept) and oscillatory (theta, alpha, and beta power) components coded task demand and content across all subtasks. Aperiodic broadband power in particular strongly coded task demand, in a manner that generalised across all subtasks. Source estimation suggested that increasing cognitive demand decreased aperiodic broadband power across the brain, with the strongest modulations overlapping with the frontoparietal network. In contrast, oscillatory activity showed more localised patterns of modulation, primarily in frontal or occipital regions. These results provide insights into the electrophysiological underpinnings of human domain-general cognition, highlighting the critical role of aperiodic broadband power.

Hardware and software system for simulation, control, and communication between industrial automation programs and PLC control used in educational institutions

This work presents the design and implementation of a system for studying industrial automation in the classroom, enabling students to conduct automation practices using Siemens, Festo, and RealGames software. It complements their learning with physical digital devices connected to a computer to activate and deactivate both analog and digital inputs and outputs, as well as sensors and actuators connected through communication networks and bus-type protocols. This system allows for save cost in the classroom. With minimal investment, each student can have their own “PLC”; using an ESP32 microprocessor-based device. They can couple power components onto baseboards for the device and run the developed server, enabling the device to communicate and activate the specified outputs in the control system, with minimal programming required. The clientserver communication libraries were developed using Python, and the ESP32 programming was done using microPython. The maximum number of applications that can be interconnected is up to 127 devices. The maximum response speed or latency is 500ms, and the approximate cost per complete simulation unit is one thousand Mexican pesos. Compared to the cost of a traditional laboratory of this type, it is less than 10% of the cost of a laboratory equipped with PLCs from recognized brands.

Assessment of the Influence of the Life Cycle of Solar Power Plant Materials and Components on Ecosystem Quality.

Currently, silicon is the most often utilized material for photovoltaic cell manufacturing, as it has the potential to convert solar energy directly into electricity. The silicon used in photovoltaic solutions must be highly pure. Large amounts of power, raw materials, and fossil fuels are consumed in the production process. Post-consumer treatment of polymers, materials, and components also requires energy and matter. These processes have a significant influence on the environment. As a result, the primary purpose of this article is to evaluate the influence of a photovoltaic power plant's material and component life cycle on ecosystem quality. The research focuses on an actual photovoltaic power plant with a capacity of 2 MW located in northern Poland. According to the findings, photovoltaic modules are the part that has the most negative environmental impact, since their manufacturing requires a substantial amount of materials and energy (primarily from conventional sources). Post-consumer management, in the form of recycling after use, would provide major environmental advantages and reduce detrimental environmental consequences throughout the course of the solar power plant's full life cycle.

How to minimize mechanical power during controlled mechanical ventilation

High intrapulmonary pressures, large tidal volumes, and elevated respiratory rates during controlled mechanical ventilation can lead to barotrauma, volutrauma, and atelectrauma. Mechanical power—defined as the product of the pressure–volume integral and respiratory rate—consolidates these three risk factors into a single, intuitive parameter. Several studies have demonstrated that higher mechanical power correlates with an increased risk of lung injury and mortality, prompting the suggestion that mechanical power should be minimized. However, under the constraint of maintaining a fixed alveolar minute ventilation and positive end-expiratory pressure (PEEP), it remains unclear how to adjust respiratory rate and tidal volume to minimize mechanical power. This study provides an analytical solution to this optimization problem. Accordingly, only the elastic component of mechanical power should be targeted for minimization. Regardless of lung elastance or resistance, or the mode and settings of the ventilator, the elastic power is minimized at a tidal volume equal to twice the anatomic dead space, or approximately 4.4 ml/kg of body weight.

Severity Estimation of Inter-Turn Short-Circuit Fault in PMSM for Agricultural Machinery Using Bayesian Optimization and Enhanced Convolutional Neural Network Architecture

The permanent magnet synchronous motor (PMSM) is a key power component in agricultural machinery. The harsh and variable working environments encountered during the operation of agricultural machinery pose significant challenges to the safe operation of PMSMs. Early diagnosis of inter-turn short-circuit (ITSC) faults is crucial for improving the safety of the motor. In this study, a fault diagnosis method based on an improved convolutional neural network (CNN) architecture is proposed, featuring two main contributions. First, a dilated convolutional neural network is combined with residual structures, multi-scale structures, and channel attention mechanisms to enhance the training efficiency of the model and the quality of feature extraction. Second, Bayesian optimization algorithms are applied for the automatic tuning of architecture hyperparameters in deep learning models, achieving automatic optimization of the hyperparameters for the fault diagnosis model of ITSCs. To validate the effectiveness of the proposed algorithm, 17 simulated tests of ITSC fault severities were conducted under both constant conditions and dynamic conditions. The results show that the proposed model achieves the best performance regarding the validation accuracy (98.2%), standard deviation, F1 scores, and feature learning capability compared to four other models with different architectures, demonstrating the effectiveness and superiority of the algorithm.

Development of GaN-Based, 6.6 kW, 450 V, Bi-Directional On-Board Charger with Integrated 1 kW, 12 V Auxiliary DC-DC Converter with High Power Density.

Automotive-grade GaN power switches have recently been made available in the market from a growing number of semiconductor suppliers. The exploitation of this technology enables the development of very efficient power converters operating at much higher switching frequencies with respect to components implemented with silicon power devices. Thus, a new generation of automotive power components with an increased power density is expected to replace silicon-based products in the development of higher-performance electric and hybrid vehicles. 650 V GaN-on-silicon power switches are particularly suitable for the development of 3-7 kW on-board battery chargers (OBCs) for electric cars and motorcycles with a 400 V nominal voltage battery pack. This paper describes the design and implementation of a 6.6 kW OBC for electric vehicles using automotive-grade, 650 V, 25 mΩ, discrete GaN switches. The OBC allows bi-directional power flow, since it is composed of a bridgeless, interleaved, totem-pole PFC AC/DC active front end, followed by a dual active bridge (DAB) DC-DC converter. The OBC can operate from a single-phase 90-264 Vrms AC grid to a 200-450 V high-voltage (HV) battery and also integrates an auxiliary 1 kW DC-DC converter to connect the HV battery to the 12 V battery of the vehicle. The auxiliary DC-DC converter is a center-tapped phase-shifted full-bridge (PSFB) converter with synchronous rectification. At the low-voltage side of the auxiliary converter, 100 V GaN power switches are used. The entire OBC is liquid-cooled. The first prototype of the OBC exhibited a 96% efficiency and 2.2 kW/L power density (including the cooling system) at a 60 °C ambient temperature.

Research and Development on a 334 kJ Pulse Power Module for Electromagnetic Launch

Abstract As electromagnetic launch technology continues to evolve, the demands on pulse power supply are becoming increasingly stringent. Megajoule-level capacitive energy storage pulse power supply(PPS) are generally composed of several hundred-kilojoule pulse power modules (PPMs) connected in parallel, with the performance of these modules determining the capabilities of the entire PSS. PPMs need to have higher energy storage density, higher peak currents, and the ability to operate continuously to enhance compactness, launch efficiency, and launch frequency. Continuous operation requires improved cooling capabilities in the PPMs. This paper presents the design of an actively cooled pulse power module (PPM) with an energy storage capacity of 334 kJ, a rated operating voltage of 6.67 kV, and an output peak current of approximately 100 kA, achieving an energy density of 1.13 MJ/m3. The module utilizes water cooling, with designed structures for main power components and simulated cooling capabilities under continuous operation. The design discharge frequency can reach up to 10 shots per minute. Ultimately, the module underwent discharge performance tests using a simulated railgun load. The tests confirmed that this actively cooled PPM meets the design specifications, with a compact structure and reliable operation.

A comprehensive framework of the decomposition-based hybrid method for ultra-short-term wind power forecasting with on-site application

Ultra-short-term wind power forecasting (UWPF) is crucial for the large-scale grid connection of wind energy. The state grid in China has strict multi-step forecasting requirements, which pose challenges to on-site efficiency and accuracy. This paper proposes a comprehensive framework of the hybrid method for UWPF with on-site application, consisting of data decomposition, model prediction, and post-processing. In the first stage, rolling decomposition and feature reconstruction are employed to decompose the wind power data into sub-components without future information leakage. The feature extraction and model matching processes are then performed to make full use of different machine learning prediction models and distinct characteristics of wind power components. Finally, a novel error tracking strategy is proposed to enable real-time error correction for multi-step forecasting by capturing the fluctuation characteristics of wind power data. The proposed framework is evaluated on two field wind power datasets through comparative experiments with five benchmark methods and nine reference methods. The experimental results demonstrate that (a) Each module of the proposed method effectively contributes to improving forecasting accuracy. (b) The proposed method significantly outperforms traditional machine learning methods in both single-step and multi-step forecasts, indicating its capability to handle practical UWPF tasks effectively.

Camera-view supervision for bird's-eye-view semantic segmentation.

Bird's-eye-view Semantic Segmentation (BEVSS) is a powerful and crucial component of planning and control systems in many autonomous vehicles. Current methods rely on end-to-end learning to train models, leading to indirectly supervised and inaccurate camera-to-BEV projections. We propose a novel method of supervising feature extraction with camera-view depth and segmentation information, which improves the quality of feature extraction and projection in the BEVSS pipeline. Our model, evaluated on the nuScenes dataset, shows a 3.8% improvement in Intersection-over-Union (IoU) for vehicle segmentation and a 30-fold reduction in depth error compared to baselines, while maintaining competitive inference times of 32 FPS. This method offers more accurate and reliable BEVSS for real-time autonomous driving systems. The codes and implementation details and code can be found at https://github.com/bluffish/sucam.

Weather-aware energy management for unmannedaerial vehicles: a machine learning application with global data integration

This study introduces a machine learning (ML) framework to predict unmanned aerial vehicle (UAV) energy requirements under diverse environmental conditions. The framework correlates UAV flight patterns with publicly accessible weather data, to yield an energy management tool applicable to a wide range of UAV configurations. The model employs the Cross-industry standard process for data mining and advanced feature engineering, offering an in-depth analysis of meteorological factors and UAV energy demands. The study assesses several multi-regression linear and ML models, whereby ensemble models gradient boosting (GB) and eXtreme gradient boosting demonstrate superior performance and accuracy. Specifically, the GB model achieved a test mean absolute error (MAE) of 0.0395 V (V) for voltage, 0.808 A (A) for current, and 9.758 mA-hours (mAh) for discharge, with prediction accuracy of over 99.9% for voltage and discharge, and 97% for current, derived from the coefficient of determination (R2). A novel integration of real-world UAV logs and weather data underpins the development of a weather-aware ML prediction model for UAV energy consumption. Our framework is capable of concurrently predicting three components of energy and power with almost uniform accuracy, a feature not found in contemporary models. Empirical test flights show a discrepancy of only 0.005 W-hour (Wh) between total predicted and actual energy consumption. This work enhances both efficiency and safety in UAV operations. The resulting energy-predictive flight planning tool sets a new benchmark for artificial intelligence (AI) applications in intelligent automation for UAVs.

Review on the Application of Living PSA in Nuclear Power

With the increasing standards of safety management in nuclear power plants, Living Probabilistic Safety Assessment (Living PSA) technology has begin to play an increasingly important role in their operation. This paper aims to provide an overview of the application and development of Living Probabilistic Safety Assessment (Living PSA) technology in nuclear power plant safety monitoring and risk assessment, examining the key technologies and future challenges. Initially, we summarize the current safety needs in regard to nuclear power, examine the policy on configuration risk management technology for nuclear power plants, and outline its importance and development process in nuclear power plant safety management. Subsequently, we discuss the basic principle of Living PSAs and the working method of risk monitoring based on Living PSAs, including information monitoring data collection, online identification, real-time model updating, and risk calculation. Within the Living PSA framework, model development is not merely about creating a theoretical or static representation; it is a dynamic and ongoing process that involves a deep understanding and precise simulation of the behavior of nuclear power plant systems and components. This represents the main research efforts in Living PSAs at present. Additionally, this paper identifies the key technologies of Living PSAs in an in-depth manner, such as the reliability-model-updating technology and model building in dynamic reliability analyses, including the fault tree model, multi-layer flow model, GO-FLOW model etc. The paper lists the work of some scholars in this area in recent years, which helps readers and researchers to clearly understand the current progress of Living PSA technologies in terms of model establishment and updating. Finally, the paper summarizes the challenges and future development of Living PSA and emphasizes the possible problems in data quality, human factor engineering, and the development of Living PSA technologies in the future. In the future, Living PSAs will provide more solid support for the realization of safer and more economical methods of operating nuclear power plants.