bluuebunny/crossref_metadata_2025_split huggingface dataset:
A two-stage scheduling method for virtual power plants considering ultra-short-term forecasting errors
Authors: Sun Wenduo , Li Yuyao , Zhang Kaiming , Zheng Xiang , Ye Congqi 
Published: January 2025

Abstract
The intermittency and randomness of distributed energy resources (DER) present challenges to power system stability. Virtual Power Plants (VPPs) address this by coordinating internal resources, reducing imbalance risks, and improving energy and economic efficiency. This paper introduces a two-stage VPPs scheduling model using a sequence-to-sequence(seq2seq) model with an Attention mechanism for rolling error correction of renewable energy output. Aggregating flexible resources such as electric vehicles (EV) and hydrogen storage systems (HSS), the model applies Model Predictive Control (MPC) for intra-day rolling optimization. Case studies show the proposed method reduces total costs by 7.59% compared to methods without error correction, 1.08% relative to those without EV, and 0.98% against those without HSS, enhancing VPPs scheduling’s economic efficiency and flexibility.
Optimized scheduling strategy of air conditioning load based on NMF hypergraph clustering algorithm
Authors: Sun Xu , Liu Xiangjun , Deng Hanjun , Yang Shuai , Yu Minqi , Meng Junxia 
Published: January 2025

Abstract
The residential air conditioning load has the characteristics of dispersed distribution and state diversity, so it is of great significance to study the cluster air conditioning load resource management and the optimization of single load usage mode. This paper uses non-negative matrix decomposition and hypergraph networks to improve clustering accuracy and adaptive clustering adjustment, and decentralized control and grouping wheel stops are realized according to tunable potential. Simulation analysis verifies the clustering model’s accuracy and the control strategy’s effectiveness.
Fault diagnosis method for power electronic switching devices based on the AdaESGMD-SPGL fusion
Authors: Zhang Ning , Zhang Yongming 
Published: January 2025

Abstract
The fault signals of power electronic devices are often accompanied by significant electromagnetic noise, making it difficult for single-feature extraction algorithms to capture periodic impact signals, while single-feature enhancement algorithms are easily disturbed by noise. To address this issue, this paper proposes an AdaESGMD-SPGL fusion method for fault diagnosis of power electronic switching devices based on the integration of feature extraction and enhancement concepts. The method first employs an improved Symplectic Geometry Mode Decomposition (SGMD) to perform an initial decomposition of the switching fault signal, then selects the mode with the highest energy as the feature signal, and finally applies Sparse Periodic Group Lasso (SPGL) to learn the periodic patterns of the feature signal’s characteristic frequency and perform envelope spectrum analysis. Experimental simulation results indicate that the improved SGMD significantly enhances the robustness of the SPGL algorithm against noise.
Combustion and coordination based on multi-intelligence technology control system
Authors: Ma Wenqi 
Published: January 2025

Abstract
In the process of carbon roasting, the temperature control of the roasting chamber is extremely important, and the uniformity and accuracy of the temperature will affect the qualification rate and product quality of the roasted products, and also indirectly affect the production capacity in the production process. In this paper, multi-intelligence technology is introduced into the heating system of carbon roasting. A combustion intelligence unit is formed by a controller, thermocouple (temperature sensor), burner, combustion chamber, etc. The coordinating intelligence is designed on the upper layer of multiple combustion intelligences to manage the coordinating combustion intelligences, and a multi-intelligence combustion system is formed in a chamber. Secondly, a linear-quadratic model was applied to the coordinating intelligences to design a coordination strategy to coordinate the temperature difference between the intelligences. Finally, the coordination strategy is simulated using MATLAB, and the results show that the intelligences can be adjusted to the same level in a short time.
Modeling and simulation of a real-time control system for a planar redundant parallel robot
Authors: Yang Jiangzhao , Wang Dan , Huang Qihao 
Published: January 2025

Abstract
We designed a real-time control system for a planar redundant parallel robot. Firstly, the hardware system was constructed, encompassing the structural design of a planar 2-DOF redundant parallel robot, the selection of servo motors, and the configuration of connecting rods. Subsequently, modeling of the parallel robot was carried out, which included the development of both forward and inverse kinematics models. Based on this foundation, a real-time control system was designed utilizing MATLAB’s Simulink and the gtrbox toolbox. Models for real-time control of both forward and inverse kinematics were established. The method and process for setting the origin position were elaborated in detail. Finally, through this integrated hardware and software platform, real-time co-simulation control of the parallel robot is achieved, providing a flexible experimental platform for research on kinematics and dynamics, including trajectory planning and velocity planning of parallel robots.
Inverter power supply control strategy research based on improved virtual synchronous generator
Authors: Zheng Weiqiang , Zheng Weiliang , Wang Hongjian , Wang Wei , Wang Chengbo , Xu Qi , Zhou Zhixin 
Published: January 2025

Abstract
In the article, the power oscillation problem of the virtual synchronous generator (VSG) is studied. To facilitate a solution for this problem, an optimized virtual synchronous generator (OVSG) control method in view of forward atonement is designed. Tunable variables for atonement segment changing realize the purpose of suppressing the steps of the operating system and inhibiting active power oscillation of VSG. Virtual simulation and experiment test the availability of the designed method.
Substation fire monitoring considering multi-information fusion algorithm
Authors: Meng Qingfeng , Wang Yuanhang , Fu Kejiang , Lv Yang , Yi Donghai 
Published: January 2025

Abstract
In order to eliminate the fire safety hazards of manual inspection in substations, reduce the workload of personnel fire maintenance, and promote the construction of a modern equipment management system, an intelligent automatic inspection, and fire extinguishing robot with data interconnection has been designed and developed. Detailed regulations have been made on the robot’s working process and information-sharing mechanism. In the process of developing intelligent fire detection algorithms, multi-sensor information fusion technology was utilized to improve detection accuracy, and D-S evidence theory was introduced for fusion reasoning, achieving the goal of significantly improving the accuracy of intelligent fire detection. Experimental data shows that the D-S fusion algorithm can quickly converge the judgment time and improve the probability of accurate judgment when determining a fire situation. The entire system can achieve unmanned inspection tasks for substation fire protection.
A fault localization method for power communication fiber optic cable lines based on brillouin frequency shift features
Authors: Zeng Yanting , Zeng Yongan , Hu Ling , Xiahou Weiming , Zhang Zhihong 
Published: January 2025

Abstract
Due to the diversity of fault states of power communication optical cable lines, a fault location method for power communication optical cable lines based on Brillouin frequency shift characteristics is proposed. With the help of wavelet transform unique multi-scale analysis ability, with the characteristics of electric communication optical cable line Brillouin scattering signal mediation, by calculating the central frequency difference of two groups of Brillouin spectral data, we determine the specific frequency shift. Using the Lorentz curve to fit the frequency shift data, we determine the corresponding frequency shift peak line position. In the test results, the design method can realize the effective mediation of the fault signal. The accuracy of the positioning results for different types of faults is up to 0.1m. The error is always within 0.5m.
Fault detection of a diesel engine through vibration source separation and deep learning
Authors: Yuan Yudao , Liu Pengpeng 
Published: January 2025

Abstract
Failures in a diesel engine will cause massive damage to the machine, so it is essential to monitor and detect unexpected faults in the diesel engine. Because the diesel engine vibration signal is a nonlinear superposition of multiple vibration sources, traditional vibration identification can no longer meet the growing requirements for monitoring accuracy. For this reason, a new method is proposed in this study based on the EMD - nonlinear ICA to separate the noise and other interference signal sources from the engine vibration signal and obtain the fault-related vibration sources; then, a deep learning model is built to identify the fault types. This test assessed the proposed method for diesel engine fault detection. The results signify that the signal separation method can select the fault vibration source from the engine vibration signal and correctly identify the engine faults.
SOC estimation method for power lithium batteries in energy storage system
Authors: Li Zewen , Fan Yuanliang , Wu Han , Li Haotian , Li Lingfei , Lin Jianli , Chen Weiming , Huang Xinghua 
Published: January 2025

Abstract
The state of charge (SOC) of the battery is an index set to measure the leftover capacity of the battery. Its scientific estimation plays a vital role in ensuring the secure operation and efficient control of the battery. To enhance the certainty of battery SOC estimation, this study proposes a SOC estimation approach that blends the unscented Kalman filter (UKF) and the crisscross optimization algorithm (CSO) based on a second-order RC network equal battery model. Building upon hybrid pulse power characteristic (HPPC) testing and parameter estimation using the least squares method, the battery SOC is estimated using the urban dynamometer driving schedule (UDDS) test data and the UKF algorithm. Different from the extended Kalman filter (EKF), the UKF algorithm avoids the loss of multi-system nonlinearity during the linearization process through the Gaussian transformation based on sigma points. The CSO algorithm is utilized to calculate the noise in the battery model estimation by considering the battery charge-discharge voltage, current, and estimation results. By excluding the estimation error from the original estimation statistics, more accurate estimation statistics are obtained. Experimental analysis is conducted by comparing the proposed CSO-UKF algorithm with the UKF algorithm and the PSO-UKF algorithm, using battery charge-discharge data collected under the UDDS operating condition with a battery charge-discharge test instrument. The experimental statistics endorse the effectiveness and trustworthiness of the proposed algorithm.
Investigating the impact of water uptake and sheath moisture on electric field distribution in internal air gaps of high voltage composite insulators
Authors: Chen Hao , Zhao Hongfeng 
Published: January 2025

Abstract
Composite insulators are widely utilized in power systems as critical insulation devices. However, internal air gaps within these insulators are susceptible to partial discharge defects due to factors such as water uptake and sheath moisture, which can lead to flashover incidents. The majority of current research on composite insulators mainly focuses on air-filled gaps, with limited studies exploring the effects of water and moist air infiltration, generally employing experimental methods to measure electric field intensity. Nonetheless, these experiments are often constrained by limited conditions and fail to accurately measure the internal moisture distribution within the gaps, thus hindering a deeper understanding of electric field patterns within these gaps. Computational methods, on the other hand, have demonstrated advantages in simulating electric field distributions in realistic environments. Finite element analysis (FEA), a powerful computational tool, can be effectively applied to the calculation of air gaps in composite insulators. To gain a deeper understanding of the performance of high voltage ends of composite insulators in complex environmental conditions, particularly the infiltration of moisture between the core rod and the sheath, and the changes caused by sheath moisture due to rainfall, this study employs advanced finite element analysis software, COMSOL. By constructing an accurate FXBW4-110/100 AC composite insulator three-dimensional air gap model, the study can closely simulate the impact of varying water vapor conditions on electric field distribution. The model is based on actual physical phenomena and structural parameters such as the core rod, sheath, and their interfaces. By setting different humidity levels and simulating the rainfall process, the study observes how the electric field at the air gap is influenced by the diffusion of water molecules during the water uptake phase. Subsequently, as the sheath becomes moist, the study further explores how moisture interacts with the air and is absorbed by the insulation materials, thereby affecting electric field intensity. Notably, since there are interactions between water uptake and sheath moisture, the study not only analyzes the impact of individual factors but also considers the combined effect when both water uptake and sheath moisture are present. The results of this combined effect reveal the dynamic changes in electric field intensity over time, providing crucial insights into the durability and reliability of insulators.
Research on photovoltaic maximum power point tracking control based on improved whale optimization algorithm
Authors: Shen Yang , Gao Changwei , Li Runsheng 
Published: January 2025

Abstract
Under uneven illumination conditions, the power characteristics of photovoltaic (PV) arrays exhibit complex multi-peak features, significantly increasing the difficulty of Maximum Power Point Tracking (MPPT). The traditional MPPT method makes it difficult to accurately and timely track the Global Maximum Power Point (GMPP) when facing multi-peak characteristics. Given this, we have conducted in-depth research on PV Global Maximum Power Tracking control strategies suitable for non-uniform illumination conditions and proposed a Maximum Power Tracking control method based on an Improved Whale Optimization Algorithm. Based on preserving the original characteristics of the whale optimization algorithm, using cubic chaotic mapping to initialize the population, the initial solutions are evenly distributed. To optimize the hunting and encirclement processes of whale populations, nonlinear attenuation coefficients, and adaptive weights are introduced separately. The research results show that the improved whale algorithm can locate the maximum power peak point more rapidly and accurately, confirming its high applicability and superiority in complex lighting environments.
Research on LSTM-based winding state prediction method
Authors: Wang Yiyang , Guo Haifeng , Li Jianan , Liu Xinen , Li Xiang , Du Zhongbo , Shen Yang 
Published: January 2025

Abstract
Mechanical and electrical equipment is widely used in various links of the manufacturing industry and is also the key to the implementation of modern industrial technology. Winding is the core component of transformers, electromagnetic valves, motors, robots, and other mechanical and electrical equipment. The safe and reliable operation of winding is the premise for the healthy operation of industrial equipment. Therefore, taking the core component of many industrial equipment, the winding, as the research object, and aiming to improve the safety and reliability of the equipment, this study focuses on the state prediction method of winding with the help of sensitive feature information. With the rapid development of industrial big data technology, data-driven technology, especially time series prediction technology, has developed rapidly. Considering the characteristics of winding degradation data, a winding state prediction method based on the Long Short-Term Memory (LSTM) approach is proposed. By using experimental data of winding, a health index reflecting their health status is constructed, and the LSTM is utilized for training the health index, thereby achieving time series-based state prediction of winding. This provides reliable support for the health status assessment of industrial mechanical and electrical equipment and enhances the reliability of the equipment.
Distributed photovoltaic maximum power point prediction based on GA-BP neural network
Authors: Shen Yulan , Yu Xiang , Zhang Chen , Xu Pengfei , Ma Yichao , Li Yaze 
Published: January 2025

Abstract
This paper introduces a novel approach to predicting the maximum power point in the Hadoop Electrical Energy mechanism, addressing the inaccuracies inherent in traditional methods. We propose a model leveraging a conventional approach - Back Propagation Neural Network (GA-BPNN) to forecast the largest load dot. The simulation results from our numerical examples indicate that the GA-BPNN-based MPPT control algorithm for the Electrical Energy mechanism efficiently tracks the largest load dot with high precision and stability, outperforming comparative methods in terms of both stability and accuracy.
Evaluation of physicochemical property changes in 3D-printed biodegradable medical devices under simulated oral physiological conditions
Authors: Lee Eungtae , Seong Yeonguk , Jeong Jihee , Ji Yongbin , Eom Joonho , Park Changwon , Kim Jinhyun , Park Sangbae , Chung Jong Hoon 
Published: January 2025

Abstract
Biodegradable medical devices undergo degradation following implantation, potentially leading to clinical failure. Consequently, it is necessary to assess the change in their properties post-implantation. However, a standardized method for the precise evaluation of the changes in their physicochemical properties is currently lacking. In this study, we aimed to establish precisely simulated oral physiological conditions (SOPCs) and investigate the physicochemical property changes to predict the performance alterations of biodegradable dental barrier membranes (BDBMs) following human implantation. We investigated changes in physicochemical properties of BDBM after exposure to SOPC for 24 weeks. When BDBM was exposed to SOPC for 24 weeks, there was a significant decrease in mass (−1.37%), molecular weight (−19.54%) and tensile load (−72.84%). Among the physicochemical properties, molecular weight decreased similarly after 24 weeks of implantation in rats (−15.78%) and after 24 weeks of exposure to SOPC (−19.54%). Changes in the physicochemical properties of BDBM in simulated in vitro oral conditions and in the in vivo environment were similar. Overall, the evaluation of physicochemical property changes after exposing BDBM to the proposed SOPC demonstrates novelty in its ability to accurately predict performance changes post-implantation. This approach may provide significant insights not only for the development of BDBM but also for various types of biodegradable medical devices.
What Is Advice?
Authors: Jonas Monique 
Published: January 2025

Abstract
Advice is a ubiquitous social practice, and one that raises complex ethical challenges in our personal and professional lives. Despite this, few contemporary philosophers have paid it serious attention. One must dig deep into the archives, and think laterally about contemporary scholarship, to find a body of ethical reflections on advising. This chapter provides a critical overview of philosophical treatments of advice from antiquity to the modern day. It explores how thinkers have approached six key questions: What is advice? Why take advice? Why give advice? How should one advise? What are the qualities of a good advisor? and What should the advice relationship be like? It identifies foundations for a theory of the ethics of advising and several questions that such a theory should resolve.
Responsibility
Authors: Jonas Monique 
Published: January 2025

Abstract
We often take received advice into consideration when we judge an agent for an act they did, but there is no established position about how we should factor it in. This chapter sets out, explicates, and defends an account of advice’s implications for moral responsibility. The core claims are that, provided that the norms of advice are adhered to, advisees remain accountable for advised acts, but that received advice can be relevant to how they are judged and responded to. Advisors are accountable for the advice they give and can be judged accordingly. Norm violations can render an advisor (partially) accountable for an advised act. The adverse consequences of the prevailing ambiguity about responsibility for advised acts are explored, and it is argued that apportioning responsibility as defended here supports the defining purpose of advice as help with practical reasoning.
Professional Advice
Authors: Jonas Monique 
Published: January 2025

Abstract
Professional advisors face particular challenges in gaining the trust of advisees, due to the possible absence of personal ties between advisor and advisee and the constraints that professional obligations can place upon advising. This chapter examines how advisors should balance professional responsibleness and responsiveness to advisees in three contexts: advice to a person in public office, law, and medical advice. In different ways in each of these contexts, professional responsibilities constrain how responsive to an advisee’s goals, preferences, and values an advisor can be. The chapter concludes with a discussion of conflicts of interest within the financial advice industry. Empirical evidence about how declarations of conflicts of interest affect advisee decision-making is presented, and it is argued that the only way to prevent conflicts of interest from compromising professional advice is to remove them.
Uncollected Writings
Authors: Wollheim Richard 
Published: January 2025

Abstract
This is the first of three planned volumes of Richard Wollheim’s work for Oxford University Press. The others are volumes on political philosophy, edited by Jonathan Wolff; and on philosophical psychology, edited by Garry Hagberg. The present volume serves two purposes. First, it contains articles published before 1994 that did not appear in any collection under his name, plus ones published since 1994, and therefore after any collection appeared of his work. Second, at the time of his death he was assembling a small book, which comprised two revised essays, the original versions of which appeared in 1994 (‘On Formalism and Its Kinds’) and 2001 (‘On Formalism and Pictorial Organisation’), plus substantial new material. The whole is reproduced here (we’ve followed Wollheim in referring to them as ‘Lectures’). Together with works of the first kind, along with his works of 1974, 1980, 1986, and 1994, we are thus provided with a complete collection of Wollheim’s works in the philosophy of art.