bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reconfiguration of distributed power distribution networks based on improved gravitational search algorithms
Authors: Lin Zefeng , Xiao Jianmei 
Published: April 2024

Abstract
In order to solve the problem of weak global search ability and premature convergence of GSA, a hybrid algorithm combining GSA and PSO is proposed. This algorithm can effectively use the advantages of PSO in global search. In addition, the update strategy of the gravitational constant is optimized, which improves the early search ability of the algorithm. Taking the reduction of network loss as the objective function, the improved algorithm is used to reconstruct the distribution network with distributed generation. The simulation results of the IEEE33 system show that this algorithm can efficiently decrease the network loss and enhance the node voltage.
A method for locating lightning strikes in distribution lines with multiple branches
Authors: Huang Zhidu , Wu Zhongyang , Cui Zhimei , Xu Wenping , Liu Yinglong , Li Shengdong , Feng Yubin , Chen Yinyao 
Published: April 2024

Abstract
A lightning strike fault location (FT) method for multi-terminal overhead lines based on 0-mode 1-mode wave velocity difference is proposed to address the shortcomings of current multi-terminal overhead line positioning methods that are difficult to identify due to multi-terminal GPS time synchronization and reflected traveling waves. Based on the wave velocity difference between 0-mode and 1-mode, single-end ranging is performed, and an FT judgment vector is formed based on the ratio of the ranging results of each monitoring terminal to the inherent length of the line. By analyzing the element features in the FT judgment vector, an FT criterion is proposed, and the FT results of the appropriate monitoring terminal are selected as the final FT. After PSCAD/EMTDC simulation verification, this method is suitable for complex multi-branch overhead lines, and can accurately locate the FT without being affected by time synchronization.
Static model of DFIG wind farms considering control strategies
Authors: Wu Xu , Wang Jingjing , Xie Dawei , Peng Wei , Mai Li 
Published: April 2024

Abstract
The static equivalent models of DFIG grid-connected currents vary under different control strategies. Traditional static equivalent algorithms fail to consider these differences, posing risks in static security analysis. In this study, based on the internal structure of DFIG, grid-connected current models for DFIG under different control strategies are established. The appropriate model is selected during the current calculation process based on the coupling relationship between active power (P) and reactive power (Q) of DFIG. Subsequently, generator nodes to be retained are selected according to the reactive power sensitivity of external network generators based on the internal network, and the node admittance matrix is modified to perform static equivalence. Results show that the proposed model in this study has higher accuracy compared to the traditional Ward equivalent model.
Distribution network risk assessment with distributed generation impact based on non-sequential Monte Carlo method
Authors: Zhang Dahua , Wang Xu , Zhang Xiaojuan , Lin Ziqing , Hu Baiji , Zhu Yayun , Cao Jingyi 
Published: April 2024

Abstract
At present, the development of renewable energy distributed generation is fast, and the distribution network is facing significant challenges. Unlike traditional fossil fuels, new energy generation is characterized by significant volatility caused by uncertain factors like environmental and meteorological conditions. This paper presents a risk assessment index system that utilizes the non-sequential Monte Carlo method to tackle the given issue. The system comprises two assessments: voltage limit risk and reliability index assessment. Through the application of the Monte Carlo method, a comparative analysis of the results with alternative calculation approaches is conducted. Subsequently, these findings are utilized to evaluate the effectiveness of two commonly employed protection strategies.
Research on AGV path planning in new energy battery workshop
Authors: Zhao Chundi , Zhang Linli , Liu Zhouyuan , Shang Xiao , Song Linsen , Zhang Yiwen , Gao Yan 
Published: April 2024

Abstract
In recent years, more and more factories have begun to use automatic guided vehicles (AGVs) instead of manual work in production, transportation, and other links, which has broad application prospects and market prospects. This paper takes the new energy battery workshop as the research object, analyzes the AGV operation plan in the workshop according to the overall workflow of the workshop material distribution, and uses AnyLogic to simulate the map to complete the algorithm test. A* global algorithm is optimized and improved, and the optimal shortest route with more smoothness from the beginning of the AGV to the end of the AGV is planned, which greatly improves the operation efficiency of the whole scheduling and has high practicability.
Transmission line defect detection based on feature fusion
Authors: Zhao Liuqi , Huang Min , Zhao Hanghang , Zhang Yuheng , Wen Xing , Zhang Xin , Wang Ning 
Published: April 2024

Abstract
Transmission lines are the lifeblood of the power system, and regular line inspections are required to ensure the operation of transmission lines. However, due to the special high-voltage environment of transmission lines, the pictures collected by UAVs are characterized by many background interferences and small defect samples, and it is difficult to directly detect line inspection methods based on deep learning. For this reason, this paper proposes a high and low-dimensional fusion framework, which divides the target detection task into four modules, and greatly reduces the technical volume of the model while retaining the high-dimensional features through the collaboration between modules. This paper also introduces the window attention mechanism based on YOLOv7, which allows the model to focus on local information and improve the model’s detection ability on small targets and also introduces the Wise-IOU loss to improve the model’s prediction accuracy and inference time. Experiments prove that the method in this paper can significantly improve the accuracy of model prediction while meeting the speed requirements of industrial scenarios.
Characterization of electric field distribution in valve-side casing of 500 kV converter transformer considering AC and DC mixed voltage
Authors: Chen Yongtao , Liu Zhi , Wang Pengbo , Yang Fan 
Published: April 2024

Abstract
The converter transformer, as one of the indispensable core devices in the ultra-high voltage direct current (UHVDC) transmission system, exhibits highly complex electric field distribution characteristics within the valve-side bushing. In-depth research into the electric field distribution characteristics and variation patterns of the valve-side bushing under both alternating current (AC) and direct current (DC) voltage conditions is crucial for ensuring the reliable operation of the transmission system. This study focuses on investigating the electric field distribution of the valve-side bushing under AC and DC voltage excitations. A full-scale finite element model of the valve-side bushing of the converter transformer is constructed using finite element simulation software for modeling and research purposes. The results indicate that the internal electric field distribution of the bushing is generally similar under both AC and DC voltage excitations. However, in the SF6-filled region, a steeper voltage drop is observed under DC excitation compared to AC. The space charges generated in the DC electric field due to field polarization have a significant impact on the distortion of the electric field on the inner and outermost layer electrode plates. These conclusions provide theoretical guidance for the insulation design of the valve-side bushing in converter transformers at the 500 kV voltage level and similar ratings.
Regional photovoltaic grid power generation output prediction based on SVM and autoencoder
Authors: Sun Mingyu , Lv Fenglei , Du Dongyan , Ren Jinggang , Guan Yongchang , Zhu Hongqing , Zhang Ranran , Ouyang Mengmeng 
Published: April 2024

Abstract
Photovoltaic power generation, which is an important component of clean energy, has been widely used around the world. However, the predictability of photovoltaic power generation systems has always been a challenge because it is affected by meteorological conditions and other external factors. Therefore, accurate photovoltaic power generation output prediction is crucial for the stability and reliability of the power system. This work aims to use two advanced machine learning technologies, supporting vector machine (SVM), and autoencoder (Autoencoder), to improve the prediction performance of regional photovoltaic grid power generation output. First, this article introduces the autoencoder and SVM network, and then we use the SVM algorithm to analyze and model these data. By using SVM, we can build a highly accurate photovoltaic power generation output prediction model that can predict on different time scales, from hourly level to daily level. Next, the principle and working mechanism of the support vector machine will be introduced in detail, and its advantages in processing complex data and high-dimensional features will be explained. Then, this article will use the grid search method to optimize the support vector machine model. A new cluster performance index based on electrical distance and regional voltage regulation capability is proposed, which divides the distribution network into multiple clusters. The voltage control strategy combines cluster autonomous optimization of time scales and distributed inter-cluster coordination optimization. Then we select evaluation indicators to evaluate the obtained model. The results show that the model can predict power generation output and provide a reference for making point arrangements. In terms of model building, this article will explain how to select appropriate features and parameters and use the collected data to train the SVM model. We also perform a detailed evaluation and analysis of the model’s performance. We use a variety of evaluation metrics to measure model accuracy and stability. Experimental results show that the SVM and autoencoder hybrid model have higher prediction accuracy and better generalization performance than traditional methods.
Failure analysis of strain clamps on a ± 800 kV transmission line
Authors: Teng Yulin , Xie Jinpeng , Chen Shanyi 
Published: April 2024

Abstract
During X-ray testing of a ± 800 kV ultra-high voltage direct current transmission line tension clamp, it was found that the internal steel core had broken. To investigate the cause of the steel core fracture, the failed clamp was dissected and analyzed, including chemical material testing, mechanical performance testing, fracture morphology inspection, and analysis of the compression process of the tension clamp. A preliminary judgment was proposed on the cause of the internal steel core fracture of the tension clamp, And verification experiments and simulation analysis were conducted to preliminarily determine the cause of the steel core fracture, and it was ultimately concluded that the main reason for the steel core fracture was the incorrect compression sequence of the large cross-section wire tension clamp.
Stability modeling of MMC-HVDC transmission system with differential flat control
Authors: Feng Zheng , Li Hui 
Published: April 2024

Abstract
AC asymmetry grid conditions put forward higher requirements for the stability control and performance of MMC-HVDC transmission systems. This thesis proposes a small-signal modeling method of an MMC-HVDC transmission system with differential flat control for AC asymmetry grid conditions, incorporating dynamic models of MMC internal characteristics, new control systems, circulation suppressors, and DC lines. To prove the accuracy of the pre-sended small-signal model, a two-terminal MMC-HVDC test system with differential flat control is constructed on the PSCAD/EMTDC platform.
Suitability of capacitor allocation methods in different kinds of distribution networks
Authors: Chen Yun , Pan Ziyan , Liu Jilei , Liu Shipeng , Zhang Jie , Ma Yongfei , Li Yadi , Ge Yimeng , Lu Binxian 
Published: April 2024

Abstract
In the context of power systems with “a high percentage of renewable energy” and “a high percentage of power electronic equipment”, reactive power capacitors are demanded to support the voltage and keep the economic operation of the distribution network. Reasonable configuration of reactive power compensation including capacitor location and compensation capacity can keep the system voltage at a reasonable level, improve the system voltage stability, and reduce the network loss. In this paper, a reactive power optimization model based on three capacitor allocation methods, namely the reactive power sensitivity method, the active power loss method, and the voltage stability method, was established. Considering the stability and economy of distribution network operation, the suitability of the different methods in different kinds of distribution networks was evaluated in terms of the indicators of minimizing the network loss, minimizing the voltage offset, and minimizing the system operation cost. The simulation results show that the active power loss method is the most suitable for reducing voltage fluctuations and network losses.
Development of a high temperature and high pressure downhole string dynamic load monitor
Authors: Yu Yang , Li Guoliang , Zhao Chuang , Cao Yinping , Liu Wangheng 
Published: April 2024

Abstract
Due to the uncertainty of the formation and the complexity of downhole tubular string loads and deformations, the existing tubular string static theory is difficult to explain and lacks support from measured data of tubular string dynamic loads. Therefore, a dynamic loads monitor for oil and gas tubular string is developed, which can synchronously measure the internal pressure, external pressure, axial force, and vibration acceleration for the string during the whole operation process. The monitor realizes simultaneous monitoring of tubular string load and vibration response parameters while meeting the requirements of various sensor placement spaces and structural strength safety, simplifying the on-site test process. By adding a power control circuit to the circuit system, the working time of the monitor is extended. The high temperature calibration test of the monitor is carried out, and the maximum calibration error is 0.10%, which meets the requirements of measurement accuracy.
Remaining useful life prediction method of lithium-ion battery Based on KPCA-IVMD-SE-DNN
Authors: Zhou Chen , Xu Hanbin , Wei Yiying , Huang Chao 
Published: April 2024

Abstract
In this paper, aiming at the problems of feature processing and capacity regeneration in the prediction of remaining useful life (RUL) of lithium-ion batteries, an RUL prediction method based on kernel principal component analysis (KPCA), improved variational mode decomposition (IVMD), sample entropy (SE), and deep neural network (DNN) are proposed. Firstly, six health indicators (HI) are extracted by analyzing the character of batteries charging and discharging process, and their correlation with capacity is calculated. Secondly, the KPCA is used to denoise and simplify the dimension of the HI set and ensure that they fully contain the degradation information. Thirdly, the battery capacity is decomposed into trend and interference components by using the improved VMD of the central frequency method (CFM), and the reconstruction is carried out according to the SE of each component to increase the efficiency and accuracy of prediction. Finally, the prediction model is constructed based on DNN. The experimental analysis of NASA battery data sets shows that the proposed method has the best prediction accuracy, efficiency, and robustness than DNN, KPCA-DNN, KPCA-EMD-DNN, KPCA-VMD-SE-DNN, and so on.
Sensitivity of 3D Convective Urca Simulations to Changes in Urca Reactions
Authors: Boyd B , Smith Clark A , Calder A C , Townsley D M , Zingale M 
Published: April 2024

Abstract
A proposed setting for thermonuclear (Type Ia) supernovae is a white dwarf that has gained mass from a companion to the point of carbon ignition in the core. There is a simmering phase in the early stages of burning that involves the formation and growth of a core convection zone. One aspect of this phase is the convective Urca process, a linking of weak nuclear reactions to convection that may alter the composition and structure of the white dwarf. Convective Urca is not well understood and requires 3D fluid simulations to realistically model. Additionally, the convection is relatively slow (Mach number less than 0.005) so a low-Mach method is needed to make simulating computationally feasible. Using the MAESTROeX low-Mach hydrodynamics code, we investigate recent changes to how the weak reactions are modeled in the convective Urca simulations. We present results that quantify the changes to the reaction rates and their impact on the evolution of the simulation.
Amplification and Dissipation of Magnetic Fields in Accreting Compact Objects
Authors: Bugli M. , Guilet J. , Del Zanna L. , Mignone A. , Mattia G. , Berta V. , Raynaud R. , Reboul-Salze A. 
Published: April 2024

Abstract
Magnetic fields play a crucial role in shaping the dynamics of accreting compact objects. Whether we consider the formation of a proto-neutron star during the gravitational collapse of a massive star or the accretion disk around a black hole after a compact binary merger, a key process that remains challenging to include in large-scale simulations is the amplification and dissipation of magnetic fields driven by turbulent fluid motions. Despite the enormous increase in computational power currently available, the large separation between all the relevant spatial and temporal scales still poses severe limits to what can be achieved with ideal fluid simulations. One way to tackle such issue is to rely on sub-grid models, which however need to be appropriately tuned in light of models probing the small-scale dynamics. In this work we present the current state-of-the-art of dynamo models in proto-neutron stars, which aim at describing the amplification of magnetar-like magnetic fields during the gravitational collapse of a massive star. We also review some of the works from the past few years that included turbulent dynamos in accretion disks around a black holes, relying on a mean-field formalism. Finally, we will present a recent study on polar jets with explicit turbulent resistivity which showcases the importance of employing highly accurate numerical schemes.
pynucastro 2.1: an update on the development of a python library for nuclear astrophysics
Authors: Clark Alexander Smith , Johnson Eric T. , Chen Zhi , Eiden Kiran , Zingale Michael , Boyd Brendan , Johnson Parker T. , DaCosta Luis Rangel 
Published: April 2024

Abstract
pynucastro1 is an open-source python library that provides visualization and analyze techniques to classify, construct, and evaluate nuclear reaction rates and networks. It provides tools that allow users to determine the importance of each rate in the network, based on a specified list of thermodynamic properties. Additionally, pynucastro can output a network in C++ or python for use in simulation codes, include the AMReX-Astrophysics simulation suite. We describe the changes in pynucastro since the last major release, including new capabilities that allow users to generate reduced networks and thermodynamic tables for conditions in nuclear statistical equilibrium.
Secular Outflows from Long-Lived Neutron Star Merger Remnants
Authors: Radice David , Bernuzzi Sebastiano 
Published: April 2024

Abstract
We study mass ejection from a binary neutron star merger producing a long-lived massive neutron star remnant with general-relativistic neutrino-radiation hydrodynamics simulations. In addition to outflows generated by shocks and tidal torques during and shortly after the merger, we observe the appearance of a wind driven by spiral density waves in the disk. This spiral-wave-driven outflow is predominantly located close to the disk orbital plane and have a broad distribution of electron fractions. At higher latitudes, a high electron-fraction wind is driven by neutrino radiation. The combined nucleosynthesis yields from all the ejecta components is in good agreement with Solar abundance measurements.
Three-dimensional Modeling of the Solar Wind and Local Interstellar Medium Interaction with Pickup Ions in the Presence of Heliospheric Current Sheet
Authors: Bera R. K. , Fraternale F. , Pogorelov N. V. 
Published: April 2024

Abstract
Our three-dimensional, time-dependent, multi-fluid model has been used to investigate the solar wind (SW)–local interstellar medium (LISM) interaction with pickup ions (PUIs) treated as a separate fluid. A non-zero, but fixed, angle between the Sun’s magnetic and rotation axis is adopted. The flow of the plasma mixture (thermal SW protons, PUIs, and electrons), is described by the system of ideal magnetohydrodynamic equations with the source terms responsible for charge exchange between ions and neutral atoms. Different populations of neutral atoms are governed by the individual sets of the Euler equations. As the standard Rankine–Hugoniot relations are not appropriate to describe the anisotropic behavior of PUIs at the termination shock, we use a kinetically-derived set of boundary conditions at it. We extend our previous work [1] and perform these new simulations on a Cartesian grid. This approach allows us to maintain a uniform grid resolution in all directions, without compromising resolution, at large distances from the Sun. The possibility of transition of the SW flow to a stochastic regime in the region between the termination shock and heliopause is further investigated.