bluuebunny/crossref_metadata_2025_split huggingface dataset:
An overvoltage adjustment strategy Based on Integrated Voltage Sensitivity in the Active Distribution Network
Authors: Chang Yuan , Hou Wei , Xu Guangfu , Hong Haohao , Zeng Xianfeng 
Published: December 2023

Abstract
With the growing proportion of renewable energy resources represented by distributed photovoltaics in the distribution network, the overvoltage problem is becoming more and more serious owing to the photovoltaic reverse power flow in the active distribution network. This paper begins with the quantification and analysis of node voltage sensitivity, and the influence calculation formula of photovoltaic injection on the voltage sensitivity of the target node and other nodes is deduced; next, a general conclusion on the change of voltage sensitivity caused by active and reactive power of Photovoltaic is given. To solve the possible low control efficiency of control strategy simply depends on voltage sensitivity, a definition of so-called Integrated voltage sensitivity is proposed, and an optimized overvoltage control scheme is utilized under the distribution network structure. The effectiveness of the proposed control strategy is validated by a simulation example, namely, on the premise of not affecting the active power output of the photovoltaic as much as possible, the same overvoltage regulation effect can be obtained with smaller adjustments in reactive power output, further, rapid overvoltage regulation is achieved.
Real-time Strategy for Voltage Regulation of Distribution Networks Considering the Incomplete Response of Distributed Resources
Authors: Tan Wenzhuo , Yi Xingyu 
Published: December 2023

Abstract
To solve the problem of nodes whose voltage exceeds the security constraint in the scenario of massive distributed generation access, a real-time strategy for voltage regulation of the distribution network considering the incomplete response of distributed resources is proposed. The strategy includes two parts: a real-time strategy for voltage regulation of the distribution network and the improvement of the strategy to deal with the incomplete response of distributed resources. Firstly, the real-time voltage autonomy of the distribution network is realized through the distributed resource strategy for voltage regulation; Then, aiming at the incomplete response of distributed resources, the strategy for voltage regulation is improved, and the margin iteration and feedback regulation are added; Finally, based on the IEEE33 node model, we verify the strategy for voltage regulation which is proposed in this paper. The results of the experiment show that the strategy for voltage regulation can effectively solve the problem of distribution network voltage out of limit, and has good anti-interference ability for the incomplete response of distributed resources.
The Analysis of Capacitance Current Testing Method
Authors: Sui Dongpeng , Huang Fucun , Liu Quanying , Tang Hong , Wang Yanan , Wang Nan 
Published: December 2023

Abstract
The accuracy of capacitance current measurement is directly related to the choice of arc suppression coil. In this paper, the capacitance current test of 10 kV and 66 kV substations in Shenyang and Dalian is carried out by using a capacitance current tester produced by different manufacturers. The measurement mainly uses the 10 kV PT open triangle signal injection method (for 3PT), 10 kV 4PT secondary signal injection method (for 4PT), and 66/10 kV transformer neutral signal injection method. The test data are compared with the test data of laboratory standard equipment. Through comparative analysis, it can be seen that some manufacturers failed to measure capacitive current or the deviation of test results is large, and the test results of each test method of other manufacturers are closer to the measured values in the laboratory.
Control and Fault Protection Strategy in DC Distribution Network
Authors: Tan Kuo , Wu Hai , Tang Fei , Zhou Cheng , Li Hui 
Published: December 2023

Abstract
This article provides a detailed analysis of the dynamic characteristics of DC pole-to-pole faults in modular multilevel converters and proposes a protection and control strategy based on the current rate of change. By detecting the current rate of change, a resistance current limiter is put into operation to achieve DC bipolar short circuit non-blocking fault crossing. The fault current is cut off by the DC circuit breaker. This method is an active current limiting control method that adds current limiting devices before fault current identification, leading to fault identification. Finally, a single-ended flexible DC distribution network model was built by using a real-time simulation system. The simulation results verified the effectiveness and accuracy of the protection and control strategy in the case of pole-to-pole faults.
Research on the accurate fault location of AC substations based on switching action logic
Authors: Xiao Xing , Meng Chenxu , Zou Youfeng , Zeng Lingcheng 
Published: December 2023

Abstract
AC substation systems contain multiple devices and components, which affect each other, resulting in increased system complexity and difficulty in locating power faults. However, the complexity of the system increases the difficulty of analysis and judgment, and puts forward higher requirements for the accuracy of algorithms and logic. Therefore, an accurate fault location method for AC substations based on switching action logic is proposed. Taking the YnD11 connection mode as an example, the operation model of the phase selection switch of the isolation switch is constructed, and the operation logic of the phase selection switch of the isolation switch is obtained. The phase amplitude and phase relationship of the three-phase isolation switch are analysed, and the appropriate switching strategy is selected to reduce the influence of electromagnetic coupling. Based on this, the time-frequency and frequency-domain information of the power signal is obtained by the wavelet decomposition method, and the local characteristic value of voltage signal is quantified. The fault location of the filtered the voltage signal is completed by the wavelet transform mode maximum method. The experimental results show that the no-load current has good linearity and almost no harmonics, and the results of the disturbance location are closer to the actual situation.
Unsupervised photovoltaic installation date detection method based on interpretable characteristics
Authors: Xiong Xuejun , Zhang Yajun , Feng Yuyao , Yang Xiu , Jin Yu , Li Wenhao 
Published: December 2023

Abstract
With the vigorous development of new energy, more and more distributed household photovoltaics are connected to the distribution network. However, the high proportion of household photovoltaics can have many impacts on the operation of the distribution network. The power department needs to register users who have installed photovoltaics. However, due to the large user base and the high resource consumption of existing investigative methods, this article proposes an unsupervised photovoltaic installation date detection method based on interpretable characteristics. Firstly, a set of interpretable characteristics is proposed and characteristic extraction is performed on user electricity data. Then, based on interpretable characteristics, user electricity consumption patterns are clustered. Finally, the date when the user’s electricity consumption patterns change is captured to determine the user’s photovoltaic installation date. The simulation results show that the proposed method can correctly identify all photovoltaic installation dates within 10 days, which is in line with practical applications and policy requirements. It has certain practical significance and theoretical value for the development of photovoltaic installation date detection applications.
A Dual Function Coating Thickness Measuring Device
Authors: Chen Xiaoke , Li Xin , Zhang Liping , Li Yida , Li Jining 
Published: December 2023

Abstract
In engineering applications, the harsh external environment leads to the erosion of the surface of the tower. The existence of the coating layer provides better protection for the tower, but the uniformity of the thinness and thickness of the coating layer has a great impact on the performance of the material. In this paper, a dual-function tower coating thickness measurement device is designed. The device can automatically measure the thickness of the coating layer by magnetic induction or eddy current thickness measurement principle for the towers with magnetic or non-magnetic materials. The small prototype experiments show that the average error of the measurement of the non-magnetic substrate is only 0.74%, and the average error of the measurement of the magnetic substrate is only 6%, and it has the characteristics of low cost and long service life.
Research on Phase-to-phase Fault Location Method of Active Distribution Network
Authors: Guo Shaofei , Li Tiecheng , Wei Yingli , Luan Shiyan , Wang Xianzhi , Wang Yonghui , Chen Tianying 
Published: December 2023

Abstract
Due to the weak feed characteristic of active distribution networks, the sensitivity of the original fault phase selection method and the accuracy of fault location are decreased. Most of the existing fault section location methods focus on the phase selection and location of the earth fault, and there is a lack of relevant research on the location of the fault section to the phase-to-phase fault. Moreover, most of the methods need to install the signal measuring device in the system as large as possible, which is not economical. The operation condition of active distribution networks is complicated, the fault characteristics are weak, and the location result is easily affected by non-fault disturbances such as DG access capacity and location. In this paper, the phase-to-phase fault of active distribution networks is taken as the research object. Firstly, phase current and zero-sequence current combined criterion is used for fault phase selection, and then combined with the phase-frequency characteristics of mode impedance of distribution line and photovoltaic power line, a fault section location method based on transient characteristic frequency band is proposed, which is not affected by non-fault disturbance such as photovoltaic control strategy and DG access capacity and location. Simulation results show that the method is effective.
Application research in multi-objective constrained optimization of composite wing structure
Authors: He Yan , Cheng Jialin , Mao Sen , Zhang Weijing 
Published: December 2023

Abstract
In order to explore the optimization of composite wing structure, this paper reviews the past research works of optimization of composite wing structures worldwide and in China. It concludes that most optimization methods and calculations are useful but only applied with specified theoretic model. Then relevant composite optimization theories and strength constraints with composite lamination failure criteria, stability, aeroelasticity are sorts out. Optimizing algorithm and applied solution rounding strategy are also listed. Concentrating on weight-reduction analysis on the thickness, sequence, and angle of the composite stacking under multiple constraints, a practical model of a composite wing structure example with a large aspect ratio is completed, and the calculation is based on the finite element analysis tool Nastran and the genetic algorithm Matlab program. Two optimal solutions are given with detailed differences and the comparison of the composite structure parameters before and after calculation is also recorded. The application example indicates the feasibility of the optimization method and quadratic rounding process, the detail structure performance improvements with skin lamination change under the calculation are illustrated at the same time.
Mental health and wellbeing outcomes of climate change mitigation and adaptation strategies: a systematic review
Authors: Flores Elaine C , Brown Laura J , Kakuma Ritsuko , Eaton Julian , Dangour Alan D 
Published: December 2023

Abstract
Climate change has already impacted the health and wellbeing of ∼5 billion people globally. However, the potential influence of climate change mitigation and adaptation strategies on mental health and wellbeing outcomes in low-and-middle-income countries (LMICs) remains insufficiently understood. We aimed to determine the effect of these strategies on mental health and wellbeing outcomes among LMIC beneficiaries. We carried out a systematic review to identify intervention and case studies published from 2013 to 2022, searching OVID Medline, Embase, PsycINFO, Global Health, Cochrane Library, GreenFile, Web of Science, and a subset of studies from the ‘Global Adaptation Mapping Initiative’ database. We included controlled, quasi-experimental, pilot, and focussed case studies reporting mental health or wellbeing outcomes assessments of climate change mitigation and adaptation strategies. We categorised studies by design, geographic region, target population, setting, environmental hazard, strategy type and primary outcomes. PROSPERO registry: CRD42021262711. A total of 9532 studies were initially retrieved, and 15 studies involving 12 255 participants met the inclusion criteria. Among these, twelve studies described evidence from single-adaptation strategies in nine LMICs, while three reported mitigation programmes. Only two randomised evaluations assessed common mental disorders such as depression, trauma or anxiety using validated scales. Most studies evaluated broader wellbeing at the community and individual levels. Nine studies (53.3%) reported significant beneficial changes in mental health or wellbeing outcomes among beneficiaries, while six (46.7%) obtained mixed results linked to local and sociocultural factors. The interventions ‘practical significance and overall impact remained unclear due to the heterogeneous reporting in program effectiveness, gaps in effect size assessments or qualitative insights. Our review highlights the scarcity and limited nature of the current evidence, underscoring the need for further equitable research. The ongoing global climate and mental health crises press us to fully understand and address these strategies’ psychosocial impacts and translate these findings into effective policy and transdisciplinary action as an opportunity to prevent and ameliorate significant, long-term problems in the population’s mental health and wellbeing.
Building spatial symmetries into parameterized quantum circuits for faster training
Authors: Sauvage Frédéric , Larocca Martín , Coles Patrick J , Cerezo M 
Published: December 2023

Abstract
Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates employed and by the correlations of their parameters. While much research has been devoted to devising adequate gate-sets, typically respecting some symmetries of the problem, very little is known about how their parameters should be structured. In this work, we show that an ideal parameter structure naturally emerges when carefully considering spatial symmetries (i.e. the symmetries that are permutations of parts of the system under study). Namely, we consider the automorphism group of the problem Hamiltonian, leading us to develop a circuit construction that is equivariant under this symmetry group. The benefits of our novel circuitstructure, called ORB, are numerically probed in several ground-state problems. We find a consistent improvement (in terms of circuit depth, number of parameters required, and gradient magnitudes) compared to literature circuit constructions.
Preparation of MXene-based hybrids and their application in neuromorphic devices
Authors: Xiao Zhuohao , Xiao Xiaodong , Kong Ling Bing , Dong Hongbo , Li Xiuying , He Bin , Ruan Shuangchen , Zhai Jianpang , Zhou Kun , Huang Qin , Chu Liang 
Published: December 2023

Abstract
The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption, making it difficult to meet the computing needs of artificial intelligence (AI). Neuromorphic computing systems, with massively parallel computing capability and low power consumption, have been considered as an ideal option for data storage and AI computing in the future. Memristor, as the fourth basic electronic component besides resistance, capacitance and inductance, is one of the most competitive candidates for neuromorphic computing systems benefiting from the simple structure, continuously adjustable conductivity state, ultra-low power consumption, high switching speed and compatibility with existing CMOS technology. The memristors with applying MXene-based hybrids have attracted significant attention in recent years. Here, we introduce the latest progress in the synthesis of MXene-based hybrids and summarize their potential applications in memristor devices and neuromorphological intelligence. We explore the development trend of memristors constructed by combining MXenes with other functional materials and emphatically discuss the potential mechanism of MXenes-based memristor devices. Finally, the future prospects and directions of MXene-based memristors are briefly described.
Current source converter-based optimal power extraction and power control of a doubly fed induction generator (DFIG) using backstepping control
Authors: Kumar Ram Krishan , Choudhary Jayanti 
Published: December 2023

Abstract
An improved backstepping control technique for the operation of a doubly fed induction generator (DFIG) powered by a wind turbine (WT) was developed and discussed. It is derived from a current source converter (CSC). The optimal backstepping control (BSC) settings are determined using the CSC approach, which can improve the drive by providing a more rapid dynamic response, greater precision, and stable performance. The CSC approach control function was constructed using the index criterion integral time square error (ITSE) and integral time absolute error (ITAE). With the simulation technique in the MATLAB/Simulink environment, the proposed BSC-CSC effectiveness is confirmed, and analyse the performance by using the PI and SMC control. The results indicate the goals of this work have been achieved in the form of resilience, improved dynamic system efficiency, lowering the harmonic distortion, optimization of stator power, and successfully addressing the problem of model parameter uncertainty.
Robust control of a single-ended primary inductor converter using adrc technique
Authors: Kumar Priyanshu , Ajmeri Moina 
Published: December 2023

Abstract
Control of a single-ended primary inductor converter (SEPIC) is a challenging task as it is a fourth-order non-linear system. In this paper, an active disturbance rejection control (ADRC) scheme is proposed for the SEPIC converter with a high switching frequency. Selection of the observers and controller bandwidth is done according to the maximum sensitivity requirement to achieve robust performance towards varying input voltage and load. The effectiveness of the suggested ADRC is shown by calculating power and efficiencies for different loading conditions.
Exploring explainable AI: category theory insights into machine learning algorithms
Authors: Fabregat-Hernández Ares , Palanca Javier , Botti Vicent 
Published: December 2023

Abstract
Explainable artificial intelligence (XAI) is a growing field that aims to increase the transparency and interpretability of machine learning (ML) models. The aim of this work is to use the categorical properties of learning algorithms in conjunction with the categorical perspective of the information in the datasets to give a framework for explainability. In order to achieve this, we are going to define the enriched categories, with decorated morphisms,

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of learners, parameterized functions, and neural networks over metric spaces respectively. The main idea is to encode information from the dataset via categorical methods, see how it propagates, and lastly, interpret the results thanks again to categorical (metric) information. This means that we can attach numerical (computable) information via enrichment to the structural information of the category. With this, we can translate theoretical information into parameters that are easily understandable. We will make use of different categories of enrichment to keep track of different kinds of information. That is, to see how differences in attributes of the data are modified by the algorithm to result in differences in the output to achieve better separation. In that way, the categorical framework gives us an algorithm to interpret what the learning algorithm is doing. Furthermore, since it is designed with generality in mind, it should be applicable in various different contexts. There are three main properties of category theory that help with the interpretability of ML models: formality, the existence of universal properties, and compositionality. The last property offers a way to combine smaller, simpler models that are easily understood to build larger ones. This is achieved by formally representing the structure of ML algorithms and information contained in the model. Finally, universal properties are a cornerstone of category theory. They help us characterize an object, not by its attributes, but by how it interacts with other objects. Thus, we can formally characterize an algorithm by how it interacts with the data. The main advantage of the framework is that it can unify under the same language different techniques used in XAI. Thus, using the same language and concepts we can describe a myriad of techniques and properties of ML algorithms, streamlining their explanation and making them easier to generalize and extrapolate.
Proteinuria screening and risk of bone fracture: a retrospective cohort study using a nationwide population-based database
Authors: Okada Akira , Honda Akira , Watanabe Hideaki , Sasabuchi Yusuke , Aso Shotaro , Kurakawa Kayo Ikeda , Nangaku Masaomi , Yamauchi Toshimasa , Yasunaga Hideo , Chikuda Hirotaka , Kadowaki Takashi , Yamaguchi Satoko 
Published: December 2023

ABSTRACT

Background and hypothesis
Proteinuria is associated with an increased risk of kidney function deterioration, cardiovascular disease, or cancer. Previous reports suggesting an association between kidney dysfunction and bone fracture may be confounded by concomitant proteinuria and were inconsistent regarding the association between proteinuria and bone fracture. Therefore, we aimed to evaluate the association using a large administrative claims database in Japan.

Methods
Using the DeSC database, we retrospectively identified individuals with laboratory data including urine dipstick test between August 2014 and February 2021. We evaluated the association between proteinuria and vertebral or hip fracture using multivariable Cox regression analyses adjusted for various background factors including kidney function. We also performed subgroup analyses stratified by sex and kidney function and sensitivity analyses with Fine & Gray models considering death as a competing risk.

Results
We identified 603 766 individuals and observed 21 195 fractures. With reference to the negative proteinuria group, the hazard ratio for hip or vertebral fracture was 1.10 [95% confidence interval (CI), 1.05–1.14] and 1.16 (95%CI, 1.11–1.22) in the trace and positive proteinuria group, respectively, in the Cox regression analysis. The subgroup analyses showed similar trends. The Fine & Gray model showed a subdistribution hazard ratio of 1.09 (95%CI, 1.05–1.14) in the trace proteinuria group and 1.15 (95% CI, 1.10–1.20) in the positive proteinuria group.

Conclusions
Proteinuria was associated with an increased risk of developing hip or vertebral fractures after adjustment for kidney function. Our results highlight the clinical importance of checking proteinuria for predicting bone fractures.