Current Deviation Research Articles

Framework and case study for the assembly accuracy prediction of prefabricated buildings using BIM and TLS

ABSTRACT Tolerance management in the Architecture, Engineering, and Construction (AEC) sector faces challenges due to a lack of systematic scientific methods and tools. Quality issues in construction, coupled with inefficiencies, rework, and waste arising from deviation problems, hinder the sustainable development of the AEC sector. Current deviation control methods in the AEC field rely on compliance inspections and on-site rework, which are reactive and costly. In contrast, the manufacturing industry, closely linked to AEC, has developed highly automated methods for tolerance management. Although prefabricated buildings are manufactured off-site with high precision, the on-site assembly precision remains low, failing to fully leverage manufacturing industry capabilities. This research proposes a framework for predicting assembly accuracy of prefabricated buildings during the design and early construction stages. The framework, based on tolerance analysis methods from the manufacturing industry, integrates Building Information Modeling (BIM) and Terrestrial 3D Laser Scanning (TLS) technologies. This innovative approach offers a computer-aided method for conducting tolerance analysis in prefabricated buildings. A completed prefabricated building project serves as a case study, utilizing the framework to predict variations in critical dimensions, and the predictions align with measured results, demonstrating the feasibility of the proposed framework.

Dynamics and structure of network magnetic fields: supergranular vortex expansion–contraction

ABSTRACT We report on the formation of a large magnetic coherent structure in a vortex expansion–contraction interval, resulting from the merger of two plasmoids driven by a supergranular vortex observed by Hinode in the quiet-Sun. Strong vortical flows at the interior of vortex boundary are detected by the localized regions of high values of the instantaneous vorticity deviation, and intense current sheets in the merging plasmoids are detected by the localized regions of high values of the local current deviation. The spatiotemporal evolution of the line-of-sight magnetic field, the horizontal electric current density, and the horizontal electromagnetic energy flux is investigated by elucidating the relation between velocity and magnetic fields in the photospheric plasma turbulence. A local and continuous amplification of magnetic field from 286 G to 591 G is detected at the centre of one merging plasmoid during the vortex expansion–contraction interval of 60 min. During the period of vortex contraction of 22.5 min, the line-of-sight magnetic field at the centre of plasmoid-1 (2) exhibits a steady decrease (increase), respectively, indicating a steady transfer of magnetic flux from plasmoid-1 to plasmoid-2. At the end of the vortex expansion–contraction interval, the two merging plasmoids reach an equipartition of electromagnetic energy flux, leading to the formation of an elongated magnetic coherent structure encircled by a shell of intense current sheets. Evidence of the disruption of a thin current sheet at the turbulent interface boundary layers of two merging plasmoids is presented.

A Vine-Copula Method for Outlier Identification in Photovoltaic Arrays

To improve the operational efficiency and reliability of photovoltaic power stations, this paper introduces a novel approach to detect outliers in photovoltaic arrays using a Vine-Copula method. The procedure is divided into two distinct phases. Initially, it identifies deviations in the direct current (DC) component of the photovoltaic (PV) system. The following phase extends this by pinpointing irregularities in the DC voltage of the array. To model the interconnection between the PV current, irradiance, and temperature, the Vine-Copula is employed in this process. The optimisation of this function is based on the Akaike information criterion. Subsequently, a conditional probability model for the PV current is developed along with a formula to determine the quantile of this probability. This interval is then employed as the primary metric for detecting and eliminating current deviations. After refining the current data, a similar approach is taken to address voltage irregularities. The results of the simulation tests indicate that this proposed method is more effective, showing lower error rates and higher accuracy in detecting outliers, compared to other methods.

Exponential stability of stochastic inertial neural networks with generalised piecewise constant argument

This paper investigates the exponential stability of stochastic inertial neural networks (SINNs) with generalised piecewise constant argument. By applying variable substitution, the second-order system is transformed into a first-order system. Subsequently, several sufficient conditions are introduced to ensure the existence of a unique solution. Due to the presence of generalised piecewise constant argument, calculus theory is applied to estimate the dynamic effects of network states in current time and deviation function. The global mean-square exponential stability of SINNs with generalised piecewise constant argument is established using the Lyapunov function method. Finally, two numerical examples are provided to verify the validity of derived theoretical results.

Research on Gate Charge Degradation of Multi-Chip IGBT Modules in Power Supply for Unmanned Aerial Vehicles

In recent years, with the burgeoning application of high voltage in various industrial sectors, the deployment of unmanned equipment, such as industrial heavy-load Unmanned Aerial Vehicles (UAVs), incorporating high-capacity Insulated-Gate Bipolar Transistors (IGBTs), has become increasingly prevalent. The demand for high-voltage IGBT modules in UAV is continuously growing; therefore, exploring methods to predict fault precursor parameters of multi-chip IGBT modules is crucial for the operational health management of unmanned equipment like UAVs. This paper analyzes the gate charge degradation in multi-chip IGBT modules after thermal cycling, which can be used to evaluate the operational state of these modules. Furthermore, to delve into the electrical response of a gate drive circuit caused by local damage within the IGBT module, an RLC model incorporating parasitic parameters of the gate drive circuit is established, and a sensitivity analysis of the peak current in the gate charge circuit is provided. Additionally, in the experimental circuit, an open sample of an IGBT module with partial bond wires lifted off is used to simulate actual faults. The analysis and experimental results indicate that the peak current of the gate charge is closely related to L and C. The significant deviation in the gate current, influenced by the partial bond wires lift-off, can provide a basis for the development of predictive methods for IGBT modules.

Arm Current Deviation Minimizing Method for Co-Phase Traction Power Supply System Using Direct AC/AC Full-Bridge Modular Multilevel Converter

Arm Current Deviation Minimizing Method for Co-Phase Traction Power Supply System Using Direct AC/AC Full-Bridge Modular Multilevel Converter

Adaptive current decoupling control scheme based on online multi-parameter identification for high-speed permanent magnet synchronous motor in fuel cell

The precise control of centrifugal hydrogen compressor (CHC) driven by high-speed permanent magnet synchronous motor (PMSM) is the key to the stable and efficient operation of hydrogen fuel cell (HFC). In this paper, an adaptive current deviation decoupling control (CDDC) strategy based on variable step size affine projection algorithm (VSS-APAs) is proposed to solve the high-speed PMSM control problem caused by current cross-coupling and parameter perturbation under complex operating conditions. Firstly, the step size is dynamically adjusted by VSS-APA through a normalized gradient descent to respond to system fluctuations, achieving rapid parameter identification during dynamic changes and accurate tracking in steady states. Secondly, stator inductance, magnetic flux linkage, stator resistance, and disturbance torque are identified in two-time-scale based on the characteristics of parameter changes, overcoming the rank deficiency in motor mathematical equations. Finally, utilizing online-identified motor parameters, the gains of the CDDC are adjusted, and compensation is applied for back electromotive force (BEMF) and disturbance torque, thereby ensuring the control accuracy of the motor under parameter perturbations and system disturbances. Experimental results demonstrate that the proposed method enhances the responsiveness and accuracy of the control strategy through efficient parameters identification. Consequently, it guarantees high-efficiency and stable performance of the CHC across different working conditions, markedly advancing the overall functionality and dependability of hydrogen energy system.

Deep learning-based evaluation of photovoltaic power generation

Photovoltaic (PV) power generation has emerged as a rapidly growing renewable energy source. However, the PV system output’s intermittent and weather-dependent nature poses challenges when integrating with the power grid. These challenges manifest as critical issues, including voltage fluctuations, harmonic distortion, and current deviation, making it difficult to accurately predict grid conditions. Moreover, the spatial variability caused by PV system intermittency further complicates the situation. To address these challenges and ensure efficient grid integration, this paper proposes a comprehensive approach encompassing deep learning-based state prediction of PV power output. The paper introduces the utilization of a long short-term memory (LSTM) model, a type of deep learning architecture, for learning patterns from historical PV power generation data and weather forecasts. The LSTM model enables accurate predictions for effective grid management by capturing long-term dependencies in PV power generation data. Real-world PV power generation data was employed to evaluate the proposed approach. The results demonstrated the significant improvement in PV power generation prediction accuracy achieved by the LSTM model compared to traditional methods. The proposed approach offers a promising solution for addressing the challenges associated with PV system integration into the power grid. It enables enhanced grid planning, resource allocation, and protection measures to accommodate the increasing penetration of solar energy harnessed through PV systems and related power electronics interfaces.

Large deviations in the symmetric simple exclusion process with slow boundaries: A hydrodynamic perspective

We revisit the one-dimensional model of the symmetric simple exclusion process slowly coupled with two unequal reservoirs at the boundaries. In its non-equilibrium stationary state, the large deviations functions of density and current have been recently derived using exact microscopic analysis by Derrida, Hirschberg and Sadhu in [J. Stat. Phys. 182, 15 (2021)]. We present an independent derivation using the hydrodynamic approach of the macroscopic fluctuation theory (MFT). The slow coupling introduces additional boundary terms in the MFT-Action, which modifies the spatial boundary conditions for the associated variational problem. For the density large deviations, we explicitly solve the corresponding Euler-Lagrange equations using a simple local transformation of the optimal fields. For the current large deviations, our solution is obtained using the additivity principle. In addition to recovering the expression of the large deviations functions, our solution describes the most probable path for these rare fluctuations.

Alleviation of Leakage Current and Neutral Point Voltage Deviation for Using an Eight-Switch Inverter

Alleviation of Leakage Current and Neutral Point Voltage Deviation for Using an Eight-Switch Inverter

Queen honey bee migration (QHBM) optimization for droop control on DC microgrid under load variation

Transmission line impedance in DC microgrids can cause voltage dips and uneven current distribution, negatively impacting droop control and voltage stability. To address this, this study proposes an optimization approach using heuristic techniques to determine the optimal droop parameters. The optimizcv ation considers reference voltage constraints and virtual impedance at various load conditions, particularly resistive. The optimization problem is addressed using two techniques: queen honey bee migration (QHBM) and particle swarm optimization (PSO). Simulation results show that QHBM reaches an error of 0.8737 at the fourth iteration. The QHBM and PSO algorithms successfully optimized the performance of the DC microgrid under diverse loads, with QHBM converging in 5 iterations with an error of about 0.8737, and PSO in 40 iterations drawn error is 0.9 while keeping the current deviation less than 1.5 A and voltage error less than 0.5 V. The deviation of current control and virtual impedance values are verified through comprehensive simulations in MATLAB/Simulink.

Investigation of current density and temperature distribution characteristics under transient conditions based on a 108 cm2 segmented proton exchange membrane fuel cell

Visualizing the electrochemical and temperature distribution characteristics inside proton exchange membrane fuel cell (PEMFC) should greatly contribute to the product development and performance optimization. In the study, a customized 27-segment 108 cm2 fuel cell was fabricated based on the printed circuit board methodology to achieve the local current density and local voltage measurement. In addition, 27 thermocouples were embedded in the segmented cathode graphite gas flow plate for local temperature measurement. Detailed information of fuel cell core components, multi-channel data acquisition system, external temperature control system, and uncertainty analysis was presented. According to the test results of 4000 s current rising conditions and 2400 s current decreasing conditions, the measurement deviation of total current between data acquisition system and TOYO test station is 2.4%–3.2%. Meanwhile, the deviation of output voltage ranges from 0.1% to 2.6%. To quantify the degree of distribution non-uniformity, the range and standard deviation of local parameter values as well as contour map are demonstrated. The non-uniformity of local current density and local temperature becomes more significant under higher operating current conditions. Furthermore, the degree of parameter non-uniformity is more noticeable under current rising conditions compared with current decreasing conditions. The transient increase of operating current leads to the sudden overshoot phenomena of local current range, and the change of local current density among all segments is instantaneously finished. However, the range of current demonstrates relatively smooth transition trends when reactant flow rate rises. Higher temperature area appears in the middle of membrane electrode assembly, especially in the position with higher local current density. The parameter distribution and transition characteristics could provide helpful guidance for fuel cell optimization and diagnosis.

Comparative analysis and enhancement of conical component calculation under internal pressure in European pressure vessel Standards

This paper presents a comprehensive analysis of conical components in pressure vessels emphasizing their significance when utilized to transition between different diameters or slopes. A thorough design and analysis are underscored as essential for ensuring the safety and reliability of these components under diverse loading circumstances. The paper aims to propose enhancements to the European Pressure Vessel Standards concerning conical components under internal pressure, with a focus on improving safety, reliability, and compliance with industry standards. Existing European standards are reviewed, identifying potential gaps and proposing practical solutions. The paper also presents research findings on the influence of cone apex angles, shell geometries and design pressure on cone plate thickness calculations. A new methodology for the design and analysis of conical components in pressure vessels is proposed, offering a potential pathway to safer and more efficient pressure vessels. A range of cylinder diameter between 1000 and 2500 mm and cone angles between 30° and 60° are used as input to quantify the difference in cone thickness for a design pressure ranging between 10 and 200 barg. The proposed method yields results much closer to AD2000 compared to EN13445, leading to slightly thicker cones (up to 2.2 mm for the selected range of cone diameters and angles) compared to the former, resulting in safer pressure vessel design, and thinner cones (up to 22 mm) compared to the latter resulting in significant material savings. A comparative analysis was performed through Finite Element Analysis validating the EN13445 unsuitability for cone thickness calculations. A modification is proposed for equation (7.6)-(8) in EN13445 resulting in thinner plates reducing the cone plate thickness difference to 0.9 % on average compared to the current deviation of -9.6 % on average for the selected range of cylinder diameters and cone angles.

Modeling and Experimental Validation on Current Uniformity Characteristics of Parallel Spiral Structure Surge Arrester in ±550 kV DC GIS

The employment of a multi-column parallel connection is intended to enhance the energy absorption capability and reliability of surge arresters. However, the disparity in reference voltage between each varistor column and the uneven current distribution may result in a reduction in performance or even failure of the surge arrester. The objective of this study is to investigate the spiral structure of a ±550 kV DC gas-insulated switchgear (GIS) parallel arrester and its influence on the current distribution characteristics. This research develops a model of a ±550 kV DC GIS arrester and performs an in-depth theoretical analysis using multi-physics field simulations. Subsequently, a ±66 kV miniature prototype is constructed, and the accuracy of the theoretical analysis and simulation results is validated by experiments, validating the effectiveness of the proposed method. This study calculates the self-generated inductance in the spiral structure of ZnO varistors using simulations. The influence of the self-generated inductance on the current distribution of the multi-column arrester when absorbing energy is further investigated. The results indicate that the self-generated inductance of the spiral structure can reduce the current deviation factor by 28–65%. This research provides a novel approach to improving current equalization in the parallel surge arresters of DC GISs for offshore wind power converter platforms.

A novel adaptive and modified controller for tracking global peak under partial shading conditions

The present state of Maximum Power Point Tracking (MPPT) techniques face challenges in accurately tracking the Global Maximum Power Point (GMPP) under non-uniform conditions. Swift transitions between operational points, significant transient fluctuations, and imprecise tracking contribute to notable power losses. To address these issues, a novel modified Perturb and Observe (P&O) algorithm is proposed. It mitigates persistent oscillations and enhances tracking accuracy in photovoltaic (PV) systems. Compared to traditional P&O and incremental conductance methods, the proposed algorithm achieves approximately 9.52 % higher average MPPT efficiency. Even under challenging conditions like partial shading, it maintains superior performance with faster tracking and less complexity. The algorithm ensures proper power sharing, achieving an average maximum stabilized power efficiency of 98.48 % with a tracking time below 0.06 s. Notably, it exhibits a 16.5 % increase in power increment across various shading scenarios. Additionally, the proposed method complies with IEC 60904-3 standards, minimizing voltage and current deviations during irradiance dynamics. MATLAB/Simulink and hardware experiments confirm the efficacy of the algorithm, demonstrating improved stability and reduced settling time. The proposed method outperforms existing algorithms, exhibiting the lowest mismatch power loss and achieving a remarkable 15.08 % enhanced power increment. This research contributes to advancing MPPT techniques, offering improved performance and stability in PV systems.

Detection and Quantification of 5moU RNA Modification from Direct RNA Sequencing Data.

Chemically modified therapeutic mRNAs have gained momentum recently. In addition to commonly used modifications (e.g., pseudouridine), 5moU is considered a promising substitution for uridine in therapeutic mRNAs. Accurate identification of 5-methoxyuridine (5moU) would be crucial for the study and quality control of relevant in vitro-transcribed (IVT) mRNAs. However, current methods exhibit deficiencies in providing quantitative methodologies for detecting such modification. Utilizing the capabilities of Oxford nanopore direct RNA sequencing, in this study, we present NanoML-5moU, a machine-learning framework designed specifically for the read-level detection and quantification of 5moU modification for IVT data. Nanopore direct RNA sequencing data from both 5moU-modified and unmodified control samples were collected. Subsequently, a comprehensive analysis and modeling of signal event characteristics (mean, median current intensities, standard deviations, and dwell times) were performed. Furthermore, classical machine learning algorithms, notably the Support Vector Machine (SVM), Random Forest (RF), and XGBoost were employed to discern 5moU modifications within NNUNN (where N represents A, C, U, or G) 5-mers. Notably, the signal event attributes pertaining to each constituent base of the NNUNN 5-mers, in conjunction with the utilization of the XGBoost algorithm, exhibited remarkable performance levels (with a maximum AUROC of 0.9567 in the "AGTTC" reference 5-mer dataset and a minimum AUROC of 0.8113 in the "TGTGC" reference 5-mer dataset). This accomplishment markedly exceeded the efficacy of the prevailing background error comparison model (ELIGOs AUC 0.751 for site-level prediction). The model's performance was further validated through a series of curated datasets, which featured customized modification ratios designed to emulate broader data patterns, demonstrating its general applicability in quality control of IVT mRNA vaccines. The NanoML-5moU framework is publicly available on GitHub (https://github.com/JiayiLi21/NanoML-5moU). NanoML-5moU enables accurate read-level profiling of 5moU modification with nanopore direct RNA-sequencing, which is a powerful tool specialized in unveiling signal patterns in in vitro-transcribed (IVT) mRNAs.

Seamless multi‐mode switching controller of four‐switch buck–boost converter based on integrated natural trajectory method

SummaryWith the characteristics of high efficiency and high power density, the four switch buck–boost (FSBB) converter is widely used in electric vehicles, direct current microgrids, and so forth. However, how to achieve smooth switching between different modes of FSBB has always been a hot issue in the theoretical and engineering fields. In this paper, considering the dynamic performance and output voltage or inductance current deviation, a control algorithm with integrated natural trajectory method is designed to realize the switching of different modes of FSBB converter. The proposed control algorithm is based on the theory of large signal natural locus and average natural locus. The normalized phase locus of inductance current and output voltage divides the phase plane into five regions and performs large signal or average switching operations in different regions. Theoretical analysis shows that the proposed control algorithm can achieve the balance between the dynamic performance and output voltage or inductance current deviation of FSBB converter in different mode switching. Finally, the performance of the proposed control algorithm is verified by experiments results.

Data Acquisition and Intraoperative Tissue Analysis on a Mobile, Battery-Operated, Orbitrap Mass Spectrometer.

Mass spectrometry has been increasingly explored in intraoperative studies as a potential technology to help guide surgical decision making. Yet, intraoperative experiments using high-performance mass spectrometry instrumentation present a unique set of operational challenges. For example, standard operating rooms are often not equipped with the electrical requirements to power a commercial mass spectrometer and are not designed to accommodate their permanent installation. These obstacles can impact progress and patient enrollment in intraoperative clinical studies because implementation of MS instrumentation becomes limited to specific operating rooms that have the required electrical connections and space. To expand our intraoperative clinical studies using the MasSpec Pen technology, we explored the feasibility of transporting and acquiring data on Orbitrap mass spectrometers operating on battery power in hospital buildings. We evaluated the effect of instrument movement including acceleration and rotational speeds on signal stability and mass accuracy by acquiring data using direct infusion electrospray ionization. Data were acquired while rolling the systems in/out of operating rooms and while descending/ascending a freight elevator. Despite these movements and operating the instrument on battery power, the relative standard deviation of the total ion current was <5% and the magnitude of the mass error relative to the internal calibrant never exceeded 5.06 ppm. We further evaluated the feasibility of performing intraoperative MasSpec Pen analysis while operating the Orbitrap mass spectrometer on battery power during an ovarian cancer surgery. We observed that the rich and tissue-specific molecular profile commonly detected from ovarian tissues was conserved when running on battery power. Together, these results demonstrate that Orbitrap mass spectrometers can be operated and acquire data on battery power while in motion and in rotation without losses in signal stability or mass accuracy. Furthermore, Orbitrap mass spectrometers can be used in conjunction to the MasSpec Pen while on battery power for intraoperative tissue analysis.

Current-Prediction-Controlled Quasi-Z-Source Cascaded Multilevel Photovoltaic Inverter

To address problems that traditional two-stage inverters suffer such as high cost, low efficiency, and complex control, this study adopts a quasi-Z-source cascaded multilevel inverter. Firstly, the quasi-Z-source inverter utilizes a unique impedance network to achieve single-stage boost and inversion without requiring a dead zone setting. Additionally, its cascaded multilevel structure enables independent control of each power unit structure without capacitor voltage sharing problems. Secondly, this study proposes a current-predictive control strategy to reduce current harmonics on the grid side. Moreover, the feedback model of current and system state is established, and the fast control of grid-connected current is realized with the deadbeat control weighted by the predicted current deviation. And a grid-side inductance parameter identification is added to improve control accuracy. Also, an improved multi-carrier phase-shifted sinusoidal PWM method is adopted to address the issue of switching frequency doubling, which is caused by the shoot-through zero vector in quasi-Z-source inverters. Finally, the problems of switching frequency doubling and high harmonics on the grid side are solved by the improved deadbeat control strategy with an improved MPSPWM method. And a seven-level simulation model is built in MATLAB (2022b) to verify the correctness and superiority of the above theory.

A Virtual Resistance Optimization Method Based on Hybrid Index in Low-voltage Microgrid

Introduction: The introduction of virtual resistance can effectively suppress the circulating current between micro-sources and improve power allocation in low-voltage microgrid, but it also causes the voltage deviation of micro-sources’ inverters. An optimization method of virtual resistance based on hybrid index is proposed in order to suppress circulating current and improve voltage deviation at the same time in this paper. The gradient descent method is used to design the virtual resistance optimization process, aiming at the optimization of hybrid index composed of circulating current and voltage deviation. The constraints are deduced with power quality requirements, capacity limitation and static stability, and then virtual resistance values are optimized. The effects of switching load and micro-source on the optimization results are analyzed through the simulation of low-voltage microgrid, and the simulation results show that the virtual resistance optimization method can significantly suppress circulating current while improving power quality. When distributed generators are connected to utility grid through inverters and feeders, differences in feeder parameters and inverter control strategies easily cause circulating current and uneven power distribution among micro-sources. The introduction of virtual resistance can effectively suppress the circulating current between micro-sources and improve power allocation in low-voltage microgrids, but it also causes the voltage deviation of micro-sources’ inverters. An optimization method of virtual resistance based on hybrid index is proposed in order to suppress circulating current and improve voltage deviation. Methods: The gradient descent method is used to design the virtual resistance optimization process, aiming at the optimization of hybrid index composed of circulating current and voltage deviation. The constraints are deduced with power quality requirements, capacity limitation and static stability, and then virtual resistance values are optimized. Results: In the simulation scenario of two micro-sources and three micro-sources, the virtual resistance obtained by the method proposed in this paper has more obvious improvement on the system operation index, and is not affected by the load type. Conclusion: The method of optimizing virtual resistance based on the hybrid index can achieve the effect of restraining circulating current and improving power sharing degree on the premise of guaranteeing power quality and satisfying system stability. The optimization of virtual resistance is affected by the number of feeders. It is necessary to re-optimize the virtual resistance after changing the number of feeders, but the process of switching micro-source and adjusting load does not affect the optimized resistance value.