bluuebunny/crossref_metadata_2025_split huggingface dataset:
Defect detection for polyethylene pipelines based on ultrasonic-guided waves
Authors: Liu Yang , Mai Huaming , Cheng Mengfei , Li Hongzhao , Zhang Weiwei , Lin Rong 
Published: December 2024

Abstract
The identification of small non-penetrating defects in polyethylene (PE) pipes, utilizing ultrasonic-guided waves, serves as the cornerstone for ensuring the safe operation of these pipes. However, owing to the PE pipe material characteristics, the guided wave has high attenuation in PE pipe, which seriously limits the detection range and accuracy of the guided wave. To address this problem, the dispersion and dissipation characteristics of ultrasonic-guided waves in PE pipes were derived, and the results indicated that the excitation frequency was the important parameter affecting the propagation distance. Then, an experimental platform for PE pipe testing was built using macro fiber composites. The acoustic attenuation coefficient and dispersion were calculated. After considering the effects of dissipation and dispersion on the guided wave, the optimal excitation frequency was selected to extend the guided wave detection distance to 4 m. Finally, an experimental study on ultrasonic-guided wave detection of defects in PE pipes was conducted. The experimental results showed that non-penetrating small defects with a section loss rate of 8% could be effectively identified and located using ultrasonic-guided waves.
Wind turbine gearbox bearings fault detection based on TCN-Attention-MTBO
Authors: Zhang Tianjie , Cheng Xiangheng 
Published: December 2024

Abstract
In recent years, the new wind turbine gearbox, as the key to promoting wind power generation technology, is regarded as the future direction to alleviate the energy crisis, so the detection of gearbox-bearing faults has become a research hotspot. Based on this, this paper proposes a new wind turbine gearbox-bearing fault detection method based on the improved TCN-Attention-MTBO model. First, a temporal convolutional network (TCN) is used to effectively capture and extract the key features in the gearbox-bearing signal. Secondly, the key features are given weights by combining the self-attention mechanism, to increase the weight proportion of effective features. Finally, the MTBO algorithm is used to automatically search the three key parameters in the TCN network. The results show that the proposed model achieves a classification accuracy of 98.75% on the public data test set. Meanwhile, compared with other classical classification models, the accuracy is improved by up to 12.92%, and the Recall, Precision, and F1 values are also improved, which realizes the accurate detection of wind turbine gearbox bearing faults.
Flexible arc suppression control method for single-phase grounding fault current in distribution network
Authors: Liu Delin , Liu Baowen , Cai Jiamin , Yu Baoqi 
Published: December 2024

Abstract
This research presents a closed-loop control technique grounded in multi-proportional (MPR) resonance to improve the stability of the output waveform of the arc suppression device and achieve precise arc suppression, in response to the problem of incomplete arc suppression caused by external interference in the distribution network grounding current. Firstly, we calculate the optimal injection current required for arc suppression before the fault occurs and use it as the reference current for closed-loop control. Secondly, after a fault occurs, the injected current is monitored in real-time, the reference current is subtracted from the actual injected current, and the difference is input into the current closed loop as a feedback parameter to adjust the injected current to approach the reference current and improve its controllability. After simulation verification, the arc suppression control method described in this piece can achieve a current control accuracy of 97%, effectively regulating injection as a reference value and suppressing fault currents.
Study on induced voltage of DC transmission lines and its influencing factors
Authors: Wen Junyi , Yu Haisheng , Liu Yang , Jin Guoliang 
Published: December 2024

Abstract
To address the issue of induced voltage that may occur during the maintenance and repair of intersecting lines below ultra-high voltage ±800 kV DC transmission lines, which poses a threat to the safety of maintenance personnel, this paper analyzes the formation mechanism of induced voltage based on the principles of electrostatic fields. By modeling an actual ±800 kV transmission line and using EMTP simulation software, the variations in induced voltage were simulated under various factors such as voltage level, parallel line length, and soil resistivity. The simulation analysis results indicate that the electrostatic-induced voltage of DC lines increases with the voltage level, parallel running length, sag, soil resistivity, and the number of split conductors; it decreases with the increase of the distance from the center and the crossing angle; and it is essentially unaffected by the operating current. Therefore, the findings of this paper have significant practical value for the protection against induced voltage in future DC high-voltage transmission lines.
Maximum power point tracking control and power distribution of marine current power system based on hybrid energy storage
Authors: Wang Ruiwang 
Published: December 2024

Abstract
With the development of industry in modern society leading to the energy crisis and the problem of the earth’s environmental pollution, it is especially important to generate electricity from renewable and clean energy sources. Marine current power generation, as a form of marine energy use, is also the object of widespread concern at home and abroad. A system consisting of supercapacitors and batteries can smooth out the fluctuations of the generating power. Power distribution is a necessary step to achieve smooth grid-connected power fluctuation by using the hybrid energy storage system (HESS). In the article, low-pass filter (LPF) and empirical mode decomposition (EMD) algorithms are used to distribute the marine current power generation, and the simulation experimental model of the present system is constructed in the Matlab/Simulink environment. Results of the simulation experiments show that the power curve of the generator side is obtained, and the generation power distribution is realized. Power signals of different frequencies are separated through the power distribution, so as to obtain the reference power of supercapacitors and the batteries.
Design and implementation of terminal power information collection and control system based on low-code platform
Authors: Qin Weiming , Guo Wenjing , Lin Jingwen , Chen Xuerui , Li Jiahao , Ming Meng 
Published: December 2024

Abstract
As the number of power consumption collection terminals continues to increase, the business needs for remote signaling and telemetry data collection through interface services continue to expand. Different professional collection front-end openness levels and open standards are not unified, and each professional terminal can only be managed in a decentralized manner. As a result, the current power consumption information data collection capabilities are scattered. In general, active data collection and sensing in enterprises must rely on various collection services to carry out applications. The lack of unified operation and management mechanisms and tools leads to difficulties in the development of interfaces and the management of terminal equipment. A set of power consumption information collection service integration systems developed based on a low-code platform is proposed. In view of the different communication protocols of power terminals and the need to develop different interfaces, etc., the command issuance and message encapsulation of different power collection protocols is carried out, to provide customized collection services and improve unified access and management capabilities of terminals.
Transformers partial discharge pattern recognition based on Vision Transform
Authors: He Shifang 
Published: December 2024

Abstract
The secure and stable operation of modern electricity systems depends on the reliable performance of transformers. Partial discharge (PD), as a common fault phenomenon in transformers, has a significant impact on its performance and lifetime. Traditional PD identification methods rely on manual feature extraction and lack generalization capability. In addition, this paper proposes a partial discharge recognition method based on Vision Transformer (ViT), which utilizes the self-attention mechanism of the visual transformer to accurately recognize partial discharges in transformers. Through the training and testing of the transformer partial discharge mapping dataset, the results show that the method in the present paper outperforms the traditional method in terms of recognition accuracy and stability, and the accuracy is maintained between 96.24% and 97.08%, which has a good prospect for engineering applications.
Optimization control of distribution network voltage based on voltage level and power factor segmentation
Authors: Ma Xiang , Shen Xi , Fang Xuan , Lv Leiyan , Song Xin , Mao Huibao 
Published: December 2024

Abstract
This article deeply analyses the model and calculation method of distributed energy in reactive power generation and absorption operation modes. It proposes an optimized control model and optimization method based on the segmented power factor of the distribution network voltage level. Simulation calculations were conducted. The results showed the effectiveness, economy, and balance of the reactive power optimization model and algorithm considering voltage level and power factor segmentation.
Risk assessment of belt conveyor belt in thermal power plant based on Bow-tie model and uncertainty Fuzzy Dynamic Bayesian Network
Authors: Liu Xin , Zhou Chongbo , Jiang Wen , He Hui , Liu Hao 
Published: December 2024

Abstract
In thermal power plants, coal conveyor belts pose significant risks that jeopardize the stability of the energy supply, underscoring the need for effective risk management. To address the complexity, uncertainty, and polymorphism issues in belt conveyor systems, we introduce a BT-UFDBN risk analysis method specifically for coal conveyor belts. This method develops a typical Bow-tie model, identifies potential risk factors for unplanned stoppages, and utilizes fuzzy evaluation methods and an improved SAM method to determine prior probabilities. The Bow-tie (BT) model is then mapped into a Dynamic Bayesian Network (DBN). To manage uncertainties within the DBN, the Leaky Noisy-OR gate model, stationarity, and first-order Markov assumptions are employed to ensure the model’s validity and practical relevance. This paper uses a belt conveyor system from a thermal power plant as a case study to validate the model’s effectiveness in predicting accident consequences, diagnosing fault causes, and proposing targeted preventive measures for identified weak points. The study provides theoretical guidance for risk management of coal conveyor belts in power plants.
Polynomial fitting calibration method for K-type thermo-couples based on the least squares method
Authors: Li Xingyu 
Published: December 2024

Abstract
Thin-film thermocouples, a crucial technology for accurate temperature measurement, have attracted considerable attention in energy utilization, environmental monitoring, and advanced manufacturing. This paper provides an overview of the evolution of thin-film thermocouples, with a focus on the calibration methods and experimental performance of K-type thermocouples. Utilizing a five-layer film structure that includes a polyimide substrate, aluminum oxide insulation layer, nickel-chromium and nickel-silicon metal films, and a silicon oxide protective layer, we have designed and implemented a new thin-film thermocouple temperature measurement system, which has been experimentally validated for stability and repeatability under high-temperature conditions. The results indicate that the system demonstrates excellent thermoelectric potential output and good repeatability in temperature measurement within the range of 50°C to 250°C. Although the long-term stability in extremely high-temperature environments requires further study, this research provides theoretical and experimental support for the future development of thin-film thermocouple technology.
Fault Detection selection method for distribution network based on regional amplification of grounding current and double closed-loop feedback control
Authors: Yu Baoqi , Liu Baowen , Liu Delin , Cai Jiamin 
Published: December 2024

Abstract
The susceptibility of high-resistivity ground faults in power systems, coupled with systemic imbalances, can result in the distortion of fault characteristics, leading to erroneous fault line selection. In addition, the full compensation of fault arc suppression conceals the fault characteristics, which brings difficulties to fault detection. In order to take into account the needs between fault arc suppression and fault detection, and tackle the issue of high-impedance fault line discrimination with a comprehensive and innovative approach, this paper studies the approach to line selection based on double closed-loop feedback control and amplification of the grounding current region. It enables adaptable grounding of the neutral point when a transient ground fault arises, and priority is given to ensuring fast and reliable arc suppression. In the event of a persistent grounding fault, the flexible grounding of the neutral point through the current source serves to mitigate the deleterious effects of fault deterioration, and the fault current is flexibly modulated within a relatively safe range until the requirements of fault arc suppression and fault detection are met.
Numerical simulation of the effect of fixation type on random vibration of fuel cell stacks in various vehicles
Authors: Yao Junqi , Yin Yan , Zhang Fan , Qin Zhikun , Guo Ting 
Published: December 2024

Abstract
During vehicle operation, the fuel cell stack onboard inevitably experiences vibration loads due to uneven road surfaces and internal system vibrations. Therefore, this paper conducts a mechanical response analysis of metal plate fuel cell stacks under random vibration loads for various vehicles (passenger vehicles, buses, trailers, and logistics vehicles) and different fixation types (fixing with 14 bolts, fixing with four corner bolts, fixing blind end plates, fixing short sides of both end plates, fixing long sides of both end plates). The stress distribution of the membrane electrode assembly (MEA) and sealing rings is obtained. The results indicate that fixing with 14 bolts is the optimal fixation type for various vehicles, resulting in the minimum stress on both the MEA and sealing rings. However, under the condition of fixing the short sides of both end plates, the stress on the MEA is maximum for various vehicles, and under the condition of fixing blind end plates, the stress on the sealing ring is maximum for various vehicles. Hence, during stack installation, it is advisable to avoid these two fixation types to the greatest extent possible. Among various types of vehicles, passenger vehicles experience the least impact from random vibrations, whereas logistics vehicles are the most susceptible to such vibrations. Additionally, stress on the MEA and sealing rings is higher around the corners, making them susceptible to damage.
Optimization scheduling of power flow pre allocation for flexible interconnection of distribution networks using improved Non-dominated Sorting Genetic Algorithm III
Authors: Xin Shijin , Hao Guojie , Zhang Pan , Fan Changhui , Niu Haitao , Zhang Zhaoxu 
Published: December 2024

Abstract
In order to stabilize and effectively control the operation status of the distribution network, we can take a series of measures to ensure the stable operation of the system. An improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) algorithm is proposed to optimize the pre-distribution of flexible interconnected power flow in the distribution network. Based on the improved NSGA-III algorithm, an optimal regulation framework of flexible interconnected power flow in the distribution network is constructed. According to this framework, the minimum loss objective function and peak load shedding objective function are calculated, and the constraint conditions are set according to the constraint conditions of these two functions. By using the improved NSGA-III algorithm, the objective function of optimal regulation of flexible interconnected power flow in the distribution network is solved. The experimental results show that this method is remarkable in frequency stability, with the smallest fluctuation amplitude, and can quickly keep the frequency stable. After applying this method, the voltage amplitude of phase A is effectively adjusted.
Frequency response control strategy of wind farm based on wind turbine state
Authors: Fu Guoyong , Wang Shilong , Zhao Hanqiu , Zeng Ting , Fan Ziwei , Jian Wenqing 
Published: December 2024

Abstract
With the development of new power systems, the inertia of the power system generated by connecting the air power generation to the high power network is gradually decreasing. First, the proposed piecewise element is to determine the production frequency of the wind electric field classification based on the operating state and average velocity of the wind electric field. On the other hand, the inertia and frequency during the parameter operation decrease as a function of the rotational air dynamic ability, while monitoring the rotational air distribution dynamics closely and monitoring the change in the reference power. This allows power to be used to convey unit rotor energy at higher frequencies to avoid the lower unit speed involved in frequency control. Simulation results show that the strategy can fix the active effects of power based on the fan, the state of the wind electric field, and the possible kinetic energy during rotation.
Energy storage optimization strategy for photovoltaic-storage-charging microgrid at highway service areas
Authors: Tao Yongjin , Shi Yunchun , Liu Bin , Mao Yong , Chen Xiaoqi 
Published: December 2024

Abstract
To enhance the utilization rate of photovoltaic (PV) systems in highway service areas and reduce energy costs, this paper proposes an optimization model for the configuration and scheduling of energy storage capacity in a PV-storage-charging microgrid for highway service areas. A novel solving algorithm is employed for the simulation analysis. First, a mathematical model of the highway service area microgrid is established, and the electric vehicle charging load in the service area is analyzed using Monte Carlo simulations. Based on this analysis, a microgrid load model for the service area is developed. Secondly, from the economic perspective of the highway microgrid, a bilevel optimization model is proposed to achieve comprehensive optimization of both the configuration and scheduling of the energy storage system. The model is solved using the EDO-MILP (Exponential Distribution Optimizer-Mixed-Integer Linear Programming) algorithm. Finally, the Pan-Da Highway (within Sichuan) distributed PV-storage demonstration project is taken as a case study for simulation and optimization. The simulation results underscore the significance of deploying 2856 kWh/714 kW of energy storage equipment and optimizing its scheduling. Compared to the no-storage scenario, this configuration leads to a substantial reduction of 36.25% in annual costs. Furthermore, when compared to an empirical scenario, the cost savings reach 30.74%. These findings serve as invaluable references for configuring photovoltaic storage systems in newly developed highway service areas, guiding the design and execution of strategies aimed at enhancing the self-sufficiency of green energy. This research not only establishes a theoretical foundation but also presents a practical solution framework for promoting broader energy integration across the entire highway network.
Short-term photovoltaic power prediction model based on VMD-DBO-KELM
Authors: Liu Xiong 
Published: December 2024

Abstract
Aiming at the problem that the current photovoltaic power prediction model has poor generalization performance and cannot achieve accurate prediction under different environmental conditions, in this paper, a combinatorial prediction model of a kernel extreme learning machine is optimized by a dung beetle optimization algorithm after variational mode decomposition. A number of different time scales of photovoltaic power data are selected for the experiment. From the experimental results, compared with the traditional model, the proposed model has strong generalization performance and outstanding prediction results in all data sets, and has broad application prospects.
Interval vertex coloring of cartesian products and strong products of paths
Authors: Lai Jinyu , Tian Shuangliang , Jin Meiqin 
Published: December 2024

Abstract
For the graph’s vertex coloring, it is required that for every vertex in a graph, the colors used in its open neighborhood or closed neighborhood must be able to form a continuous integer interval. A coloring is called an open neighborhood interval vertex coloring or a closed neighborhood interval vertex coloring of a graph if the neighborhood satisfying the condition is open or closed. In this paper, the interval vertex coloring of cartesian products and strong products of two paths is studied, and the low bound of the interval chromatic number is given.
An mhADMM-PDAS method for sparse optimal control problems
Authors: Wang Tongtong , Chen Xiaotong , Shi Yurong 
Published: December 2024

Abstract
We consider sparse optimal control problems with elliptic PDE constraints and L
1 -control cost in this paper. Inspired by the outstanding performance of the multi-level heterogeneous alternating direction method of multipliers (mhADMM) and the primal-dual active set method (PDAS), we integrate both approaches’ advantages and propose a two-phase mhADMM-PDAS method. In Phase I, we apply the mhADMM algorithm, which is known for its capability of attaining solutions with moderate accuracy. Then, in Phase II, the solutions obtained from Phase I are further refined to achieve higher precision through the employment of the PDAS method as a post-processor. Additionally, numerical results illustrate the efficiency of the mhADMM-PDAS method.
Parameters estimation for ornstein-uhlenbeck processes with time periodic modulated drift
Authors: Pan Yurong , Jia Chaoyong , Ma Cheng 
Published: December 2024

Abstract
This work mainly delves into the parameter estimation problem concerning Ornstein-Uhlenbeck processes with time-periodic modulated drift based on continuous observations. A least squares estimator is constructed by minimizing a contrast function. It is demonstrated that as the number of observed periods approaches infinity, the least squares estimator exhibits asymptotic unbiasedness, strong consistency, and asymptotic normality. The important technique in the asymptotic study relies on the central limit theorem for multiple Wiener integrals. Finally, numerical simulations of the least squares estimator are presented.
Exploring the impact of chiral phase transition on axion adopting the three flavour Nambu–Jona-Lasinio model
Authors: Jia Xueqing , Zhao Wenhao 
Published: December 2024

Abstract
We employed the three flavour Nambu–Jona-Lasinio (NJL) model as a low-energy effective theory of QCD to systematically study how axion properties change with temperature and density. By calculating and illustrating axion mass, its self-coupling and quark condensation parameters as functions of chemical potential and temperature, we provide insights into the impact of QCD phase transitions on the properties of the axion. This research contributes to a deeper understanding of how QCD phase transitions affect axion behavior.