bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digitalization of the urban communities: energy harvesting from water pipeline to enhance leakage monitoring and detection
Authors: Tiwari Ravi Nath , Pais Tatiana , Reggio Federico , Pascenti Matteo , Traverso Alberto , Silvestri Paolo 
Published: December 2024

Abstract
The SMART PIPING project aims to design, create and test innovative solutions for resilience of water and energy networks, aiming to develop technologies for the energy autonomy of stations monitoring of infrastructures suitable for the transport of fluids (natural gas and biogas, hydrocarbons, water). In this paper, an innovative bladeless turbine is designed and manufactured in relevant environment to demonstrate the feasibility of harvesting significant amount of energy in the urban pipeline distribution network to power local devices to monitor the piping health and enable leakage early-warning. Results show that bladeless turbine can constitute a cost-effective and highly reliable device to enable the digital and energy-efficient transition of urban communities.
Design of a six-axis integrated drive-control industrial robot controller
Authors: Yao Lingfeng , Feng Tingping , Li Junmin 
Published: December 2024

Abstract
This paper designs a six-axis industrial robot servo system based on Advanced RISC Machine (ARM) + Field Programmable Gate Array (FPGA), which combines the advantages of small size, smooth speed, high precision and low jitter, and the parallel processing of FPGA realizes the multiple Field-Oriented Control (FOC) algorithms to collect the output data efficiently. The FPGA parallel processing implements the multi-way Field-Oriented Control (FOC) algorithm to efficiently collect the output data, and the ARM reads the FPGA data through the Flexible Memory Controller (FMC) bus to configure the FPGA to drive the robot, which enhances the flexibility and real-time performance, and the system is able to control the six servo motors synchronously. Through experiments, we found that the industrial robot exhibited no significant changes in current feedback under both no-load and full-load conditions. Additionally, the proposed controller demonstrated higher precision in joint movement control compared to conventional controllers, achieving a maximum improvement of 0.088° and maintaining an error margin within 0.099°. The experiments validate its high-precision control, effectively addressing issues of speed smoothness and jitter, thereby providing substantial practical application value.
Study on the morphology of silicone rubber water tree under AC voltage
Authors: Yuan Xinqi , Zhou Yuanxiang , Jiang Guimin , Li Yuhang 
Published: December 2024

Abstract
Water tree is the deterioration phenomenon of insulating materials due to the ingress of water during operation. In order to reveal the change characteristics of the water tree morphology during the aging process of the silicone rubber water tree, an experimental platform for silicone rubber water branch aging was built, the morphology of the water tree in silicone rubber was observed, and a possible explanation was proposed. Silicone rubber samples were subjected to an accelerated water tree aging test in sodium chloride solution with a concentration of 1.8 mol/L for 2 weeks. The morphology of the water tree was observed, and the results were analyzed. The results showed that the morphology of the water tree in silicone rubber was inconsistent with that in XLPE, which was manifested in the following aspects. The average length of the water tree in silicone rubber was 50 μm, and the water tree grew along the wall of the pinhole. After dyeing, the morphology of the branches could be observed, but branches at the pinhole wall gradually disappeared within 15 minutes. After reheating, the water branches reappeared. In silicone rubber, the birthrate of the water tree in the silicone rubber water branch aging test was low, only 1/8 in this experiment. Its growth rate was 0.18 μm/h.
Research on the calculation model of energy consumption and carbon emissions in the service industry based on time series prediction model
Authors: Peng Haoyue , Jiang Jinyang , Peng Wenxin , Kuang Pengyan , Xiang Fei , Yang Li , Peng Shun , Cai Yuhang , Zhong Yuanhong 
Published: December 2024

Abstract
As an important component of the modern economy, the service industry’s energy consumption and carbon emissions increasingly impact the environment. This article constructs an application system for a large amount of electricity data analysis, aiming to accurately calculate and predict energy consumption and carbon emissions in the service industry through a calculation model of electricity carbon. This article combines big data, machine learning, and optimization algorithms. It compares the use of models such as ARIMAX, BP neural network, and LSTM to improve the accuracy and reliability of energy consumption and carbon emission prediction. Through actual data and case verification, the calculation model of electricity-carbon has shown good applicability and prediction accuracy in different scenarios.
Equivalent measurement method for ice thickness of transmission lines
Authors: Zhong Jianwei , Jiang Xingliang , Zhang Zhijin , Zhu Zhongfeng , Wu Zhouchang , Liu Xintong 
Published: December 2024

Abstract
Icing on power transmission lines poses a serious threat to the safety and stability of power grids. Traditional manual patrols are inefficient and unable to effectively monitor ice conditions on the lines. This paper presents a novel ice-monitoring device that employs cylindrical arrays to predict and monitor icing parameters. By measuring the increase in ice weight on multiple cylindrical surfaces, environmental conditions such as wind speed, temperature, and water droplet content are determined. The device combines advanced modeling techniques to provide real-time and precise data, facilitating timely warnings and preventive actions. Experimental results have confirmed the accuracy of this method, offering a reliable solution for managing ice-related risks in power transmission systems.
A balanced optimization method for energy storage capacity allocation in new power systems under the background of carbon reduction
Authors: Qian Xiao , Wang Xiran , Pan Li , Zou Bo , Li Yang 
Published: December 2024

Abstract
Under the combined effects of national carbon reduction requirements, strong volatility and randomness at the new energy end, and a large amount of source load at the load end, the planning and operation characteristics of the new power system will also undergo fundamental changes, and energy storage planning and configuration will become the most suitable solution. This paper explores the optimal configuration of energy storage capacity within emerging power systems. Using a distribution network heavily reliant on renewable energy as a case study, we employed an echo state network to optimize the balance of energy storage capacity. Numerical experiments and case studies were conducted to validate the efficacy of this approach. Our research aims to furnish a robust and practical methodology for optimizing energy storage capacity in power systems, thereby offering substantial support towards achieving carbon reduction targets and fostering sustainable development within the energy sector.
Optimisation of photovoltaic storage and charging system operation based on photovoltaic power generation prediction analysis
Authors: Yu Changle , Li Wenwen , Liu Jun , Zhang Su , Zhao Xiaona , Li Qiwen 
Published: December 2024

Abstract
The power generation of photovoltaic power generation systems has more influencing factors. Accurate PV power prediction data is needed in order to maintain the stable operation of the power system. In this paper, a photovoltaic power generation prediction scheme combining the integrated weather-similar day clustering method is designed with reference to the weather characteristics in the northeast region. The training samples are classified into three generalized weather small sample datasets. The radial basis (RBF) neural network prediction optimized with the Fruit Fly Algorithm (FOA) is chosen as the main body of the prediction system model. The prediction model is established. The power prediction is compared with the power prediction using the BP neural network model. The simulated prediction results are analyzed. The results indicate that the FOA-RBF neural network model exhibits superior performance in approximating nonlinear functions compared to the BP neural network. Ultimately, the forecasted data is utilized for operational enhancement, aiming for peak economic efficiency as the goal of optimization. This process considers limitations like energy equilibrium to enhance the financial viability of the optical storage charging facility, which improves the economic efficiency of the photovoltaic storage charging station, enhances the reliability of the system, and verifies the usability of the small-sample PV power prediction strategy based on FOA-RBF neural network designed in this paper for the northeastern region.
Research on capacity configuration method of optical storage charging integrated charging station considering economic and environmental benefits
Authors: Yu Changle , Yang Lin , Zhang Su , Liu Jun , Wan Jia , Song Mingda 
Published: December 2024

Abstract
In order to deeply explore the economic and environmental benefits of the photovoltaic-storage-charging integrated power station and optimize the capacity allocation of the power station, a capacity allocation strategy integrating economic considerations and environmental benefits is proposed. Firstly, based on the summary analysis of the typical architecture and operation characteristics of photovoltaic microgrid, the dual objectives of maximizing system economy and efficient utilization of photovoltaic energy are set, and multiple constraints such as power balance, installed capacity, charging and discharging efficiency and energy storage status are incorporated. A multi-objective optimization framework of microgrid adapted to time-of-use electricity price is constructed. In view of the complex model, multi-objective conflict and nonlinear characteristics, this paper introduces the improved weed algorithm as a solution tool. Finally, the examples of typical daily operation scenarios are simulated and verified by the Matlab platform. The results show that the method effectively realizes the capacity configuration of the integrated power station of optical storage and charging, provides a reference for the construction of a green energy system, and ensures the rationality and effectiveness of the construction of charging stations.
Improved state transition algorithm for hybrid power system reactive power optimization using filter techniques
Authors: Song Tao , Pu Yu , Mo Jun , Tang Hao , Zhong Jiyou 
Published: December 2024

Abstract
Reactive power optimization in power systems is crucial for their normal and efficient operation. However, with the increasing prevalence of active current and direct current (AC-DC) hybrid power grids, existing optimization methods face issues such as poor adaptability, low efficiency, insufficient accuracy, and inadequate convergence. To address these problems, this paper proposes a reactive power optimization model based on an improved state transition algorithm (STA) utilizing filter techniques. The optimization model considers operational costs associated with adjusting transformer tap positions, switching reactive power compensation capacitors, regulating generator terminal voltages, and adjusting active power in DC transmission. Simulation results demonstrate that this method can reduce system losses and voltage control costs while ensuring stable system operation, thus improving the reactive power distribution in AC-DC hybrid transmission systems.
Research on photovoltaic power generation prediction based on EPSO-ELM model
Authors: Zhang Peng , Yao Hongyang , Chen Ting , Sun Qiuya , Qian Xingxu 
Published: December 2024

Abstract
To increase the precision of photovoltaic power prediction (PPG), we propose a PPG algorithm based on variational mode decomposition (VMD) and enhanced particle swarm optimization (EPSO) to optimize the extreme learning machine (ELM). Firstly, VMD is employed to decompose photovoltaic power data into multiple intrinsic modal components of different frequencies, to decrease the non-stationarity of the original sequence. Secondly, an improved EPSO algorithm is constructed using Latin hypercube sampling and quadratic interpolation strategy. Then, the EPSO is used to globally optimize the ELM parameters and establish EPSO-ELM combination models for different modal sequence components. Finally, the trained combination model is used to perform multidimensional prediction on the modal feature components of each decomposed subsequence, and the modal prediction sequences of each layer are superimposed and combined to form the final output result. The simulation results prove that the constructed VMD-EPSO-ELM model has stronger robustness and higher prediction accuracy compared to conventional short-term PPG prediction models.
Research on adaptive control strategies for grid-friendly photovoltaic storage network systems
Authors: Tong Menghao , Niu Shengsuo 
Published: December 2024

Abstract
To address the critical issue of frequency stability in grid-connected photovoltaic energy storage systems, a control strategy based on grid-friendly technologies is proposed. This strategy integrates the principles and topology of traditional Virtual Synchronous Generator (VSG) with adaptive VSG control and pre-synchronization techniques. Firstly, based on the traditional VSG control strategy, the frequency operation curve of the VSG is divided into three intervals corresponding to different control modes: islanding mode, PV-storage frequency stabilization mode, and adaptive regulation mode. Each mode is analysed to understand the impact of different inertia and damping coefficients on system output characteristics. A control strategy that allows for adaptive adjustment of inertia and damping coefficients is designed, referencing the power angle characteristic curve and frequency oscillation curve of a synchronous generator (SG). By introducing inertia elements, the robustness of the system against disturbances is enhanced. Utilizing the operational characteristics of energy storage, the combined PV and energy storage system can better interact with and support the grid. Finally, simulation experiments are conducted by using MATLAB/Simulink. The proposed strategy is compared with traditional adaptive control and VSG control strategies. The results demonstrate that the proposed grid-friendly VSG control strategy offers superior frequency stability, robustness, and response speed.
Calculation method for the resistive current of metal-oxide surge arresters using model parameters solved by the Levenberg-Marquardt algorithm
Authors: Zhao Haidong , Xiao Xinyan , Yang Lanjun 
Published: December 2024

Abstract
Resistive current analysis is a crucial technique for evaluating the operational status of metal-oxide surge arresters (MOSAs). Various precise methods for calculating resistive current have been proposed. However, most existing approaches focus on simulation rather than experimental validation, and comprehensive studies on the performance of these methods in site tests are lacking. This paper introduces a resistive current calculation method based on the Levenberg-Marquardt (LM) algorithm. This method determines the fitting parameters of the MOSA equivalent model using the LM algorithm and then calculates the resistive current based on the solved parameters and voltage waveforms. Experimental validation on a 10 kV arrester demonstrates that the LM algorithm has significant accuracy and speed advantages over heuristic algorithms in parameter estimation. Additionally, while the fundamental components of the resistive current calculated by the LM method are consistent with those obtained by existing current orthogonality and harmonic analysis methods, significant differences are observed in the harmonic components. The resistive current calculated by the proposed method aligns more closely with theoretical expectations, rendering the results more reliable.
Investigation of the impact of different PWM frequencies on DC motor control based on 8051 series microcontroller
Authors: Li Ying 
Published: December 2024

Abstract
Pulse Width Modulation (PWM) is widely used for controlling electronic signal transmission, particularly in direct current (DC) motor control. This paper focuses on the application of PWM in controlling DC motors. The operating principle of PWM reveals that the frequency plays a crucial role in determining the precision of DC motor control. Generally, higher PWM frequencies result in smoother and more accurate motor operation. However, simulation experiments in this paper indicate that excessively high PWM frequencies can disrupt the linear relationship between control precision and frequency, leading to a significant decrease in motor speed. Based on the experiment conducted under Proteus simulation software and using an 8051 series microcontroller to generate PWM signals at various frequencies, this paper explores the impact of PWM frequency on DC motor speed. The analysis of simulated components demonstrates that at high PWM frequencies, the integrated circuit L293D experiences increased switching losses, and higher power dissipated. Additionally, the self-inductance phenomenon in DC motors becomes more pronounced with high-frequency signal transitions. Both the switching losses and self-inductance ultimately contribute to the reduction in motor speed, compromising control precision.
Simulation research on vehicle suspension control system
Authors: Yang Yuping , Shui Guang , Liu Yuting , Bu Kejian 
Published: December 2024

Abstract
The suspension system functions to transmit forces and moments between the wheels and the chassis, absorb impact forces, and ensure vehicle smoothness. In this study, the nonlinear suspension system was simplified to a two-freedom model. State-space equations for the control system were derived. A simulation model was created using Simulink. Both robust and fuzzy control strategies were applied to assess the control system's performance. Simulation results show that as the vehicle's speed increases, both the sprung and unsprung displacement and velocity responses also increase. The suspension system with fuzzy control shows marginally higher vertical displacement and velocity of the vehicle body compared to the robust control system, indicating that fuzzy control is less effective. In contrast, the state-feedback robust controller exhibits greater robustness.
Research on the sustainable assessment of ecological low-carbon buildings with integrated sandstone energy storage systems
Authors: Zhang Junxue , Asutosh Ashish T. 
Published: December 2024

Abstract
Addressing resource scarcity and climate change, enhancing building energy efficiency, and safeguarding ecological environments can effectively promote socially sustainable development. This paper employs an eco-low-carbon energy method and explores the sustainability analysis of a building system through the integration of modular energy storage units. The research results indicate that the integration of emergy storage units has significantly improved the sustainability of the building system by 56.9%, preliminarily verifying the feasibility of coupling energy storage units with the building system. The preliminary validation of sensitivity also ensures the accuracy of the analysis, providing a new model for the design of novel green building projects.
An innovative model based on the WOA-MLP approach for estimating the state of health of lithium-ion batteries
Authors: Li Zhuohao , Du Baoshuai , Mi Chunxu , Shi Qionglin , Li Haomiao , Wang Kangli , Jiang Kai 
Published: December 2024

Abstract
Lithium-ion batteries typically experience performance degradation during use, which can lead to failures or accidents when aging is severe. Assessing the state of health (SOH) of a battery is essential for refining battery stewardship and upkeep protocols, ensuring its secure and steadfast long-term functionality. Data-driven approaches for estimating the SOH of lithium-ion batteries are advancing swiftly, leading to the development of a novel model, WOA-MLP, for SOH estimation in lithium-ion batteries. Firstly, by analyzing the aging mechanism of lithium-ion batteries and the changing law of the charging voltage curve during the aging process, three health features (HFs), including constant-current (CC) charging time, constant-voltage (CV) charging time and the charging energy of the CC charging process are extracted. Then extracted HFs are inputted into the Multilayer Perceptron (MLP) for the estimation of SOH. The hyper-parameters of the MLP are optimized by using the Whale Optimization Algorithm (WOA), which comprises the hybrid data-driven model WOA-MLP. Finally, the efficacy of the method introduced is corroborated using a public battery dataset.
Sensorless control of the motor in full-speed range with improved weighted switching strategy
Authors: Fu Hongjun , Lu Dacheng , Zhang Jianzhong , Wu Shuai , Yang Kang , Zhang Zhiqiang 
Published: December 2024

Abstract
To achieve sensorless control of the interior permanent magnet synchronous motor (IPMSM) across the full-speed range, it is necessary to alternate between different rotor position observation methods. In order to smooth the speed-switching process, an improved weighted switching strategy is proposed. This strategy integrates the high-frequency square-wave voltage injection (SWVI) method and the super-twisting sliding mode observation (STSMO) method. By setting up a motor speed transition zone related to the rotor position error and adopting a nonlinear weighting coefficient function, this strategy optimizes the control effect. Simulation results demonstrate that the improved weighted switching strategy enables the smooth transition between SWVI and STSMO methods and minimizes the torque fluctuations generated by high-frequency injected voltage.
Optimization of dissociation efficiency in small linear ion trap mass spectrometer
Authors: Kuang Lvying , Wen Luhong , Chen La 
Published: December 2024

Abstract
Small linear ion trap mass spectrometers are often combined with direct ionization mass spectrometry (DIMS) techniques for detection and analysis. However, DIMS is susceptible to the working environment and matrix effects, resulting in reduced sensitivity, which places high demands on the performance of tandem mass spectrometry of small linear ion trap mass spectrometers. Therefore, the effects of vacuum pressure, collision q-value, collision voltage, and collision time on the dissociation efficiency of the ion trap are experimentally explored in this paper to enhance the performance of tandem mass spectrometry for small linear ion trap mass spectrometers. The findings indicate that as the vacuum pressure increases, dissociation efficiency first increases rapidly and then remains stable, and the peak dissociation efficiency of 56% is attained at a vacuum pressure of 0.31 mTorr. Increasing the collision q value, dissociation efficiency first increased slowly, then increased rapidly, and finally remained almost unchanged. When the collision q value was increased from 0.26 to 0.35, dissociation efficiency increased from 9% to 56%. Meanwhile, the maximum collision voltage corresponding to the optimal dissociation efficiency gradually decreases as the collision q value increases. With the increase in collision time, dissociation efficiency increases rapidly and then remains stable. Therefore, studying the factors affecting dissociation efficiency points the way to enhancing the efficacy of tandem mass spectrometry with small linear ion trap mass spectrometers.