bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparing Explicit and Implicit Methods for Estimating the Region of Attraction
Authors: Yuan Guoqiang , Li Yinghui , Xu Haojun , Xu Jihui , Gao Jianguo 
Published: February 2023

Abstract
The region of attraction (ROA) is an important tool in investigating control and dynamic systems. Numerous computational methods have been proposed for estimating the ROA within the Mathematics, Power systems, and Control community. These methods employ different methods such as invariant manifolds, linear differential inclusion, level set methods, the sum of squares decomposition, and many others. This paper provides new insights into these methods and divides them into two classes: explicit and implicit. The explicit methods discretize the ROA (or the boundary of the ROA) into a finite union of geometric elements and store them directly. The essential idea of the implicit methods is to present the ROA implicitly, meaning that it is feasible to find the ROA as the level set of some function of the state space variables. The key point is the iteration and optimization of this implicit function. The explicit and implicit methods are compared and discussed by investigating some numerical examples. In particular, we consider the determination of the boundary of the ROA using the stable manifolds, and the estimation of the ROA as backward reachable sets using a Hamilton-Jacobi-Isaacs partial differential equation.
Study on SOC Estimation of Li-ion Battery Based on the Comparison of UKF Algorithm and AUKF Algorithm
Authors: Guo Yi , Chen Yuhang 
Published: February 2023

Abstract
In this study, a state of charge (SOC) estimate technique for lithium-ion batteries is presented using an adaptive traceless Kalman filter (AUKF). First, the battery’s second-order RC equivalent circuit model is created, and its parameters are identified. Next, in contrast to the traceless Kalman filter (UKF) algorithm, which ignores the time-varying characteristics of the system noise when estimating the lithium-ion battery’s state of charge, the AUKF-based SOC estimation method is formed from the perspective of adaptive noise adjustment (SOC). The results of testing the AUKF algorithm in real-world settings demonstrate that it has great estimate accuracy and stability and that its estimation outcomes outperform those of the UKF method.
Short-term Wind Speed Prediction Based on Improved Auto Encoder
Authors: Yin Yikun , Shang Zengyi , Li Fengxia , Shao Hailin 
Published: February 2023

Abstract
In order to improve the wind power consumption level of the new power system with new energy as the main body, it is necessary to accurately predict the wind speed. The key is to refine the dynamic trend of the wind power system and the potential physical structure in the wind speed sequence. Firstly, on the basis of the library Koopman dynamics theory and the encoder structures, a physically constrained spatio-temporal neural network is built, which generates the linear evolution moment of the nonlinear variables of the wind farm. Secondly, the system trend is approximated by the linear evolution matrix, and the forward and backward dynamics are fully considered in the prediction process. Then, the bidirectional correlation prediction mechanism and the cost function adapted to different objects are set to reduce the requirements for the reversibility and stability of the prediction sequence. Meanwhile, the hidden vector of feature space is visualized to show the feature interval dependency in the system. Finally, the effectiveness of the proposed method is verified by the wind speed measurement data of Prince Mountain in Beipiao. The results show that the proposed method has high prediction accuracy, strong generalization ability, and high interpretability for strong random and strong fluctuation wind speed series.
Open capacity calculation method of distribution network
Authors: Li Jianyu , Wang Jie , Lin Yujie , Yang Binhao , Wu Chengjian 
Published: February 2023

Abstract
At present, there are few methods for quantitative calculation of installed load or new energy for distribution network equipment. Based on this, this paper proposes a calculation method for the open capacity of the distribution network. Firstly, the calculation methods of the open capacity of a 10 kV distribution transformer, 10 kV lines, and high-voltage substation three-level distribution network based on the conventional situation and new power system are studied, respectively. Then the calculation methods of the open capacity of the distribution system based on the conventional case and the new power system are studied, respectively. Finally, the empirical region is selected, and the research results are used for case analysis to verify the effectiveness and operability of the research results.
Research on fault reconstruction of ship regional distribution System based on improved genetic algorithm
Authors: Long Qiannan , Shi Weifeng 
Published: February 2023

Abstract
To ensure that the power supply can be restored quickly when the ship’s power system fails, the fault reconstruction method of the system is studied. Taking the number of switching operations and load loss as the objective function, the traditional genetic algorithm is improved by introducing a Tent population initialization, adaptive crossover probability, and mutation probability to realize the fault reconstruction of the ship regional distribution system. Two reconstruction examples show that the improved genetic algorithm can effectively solve the reconstruction problem of the ship’s regional distribution power system, and has good convergence speed and accuracy, which can ensure the stable operation of the ship power system.
Non-intrusive Load Identification Algorithm Based on Stacking Modeling
Authors: Miao Weiwei , Zeng Zeng , Teng Changzhi , Zhang Rui , Li Shihao , Bi Sibo , Wang Jijun , Zhou Haocheng , Li Fubao , Zhang Yongze 
Published: February 2023

Abstract
The non-intrusive power load identification relies on the smart meter’s voltage, current and power signals, which can realize the classification and monitoring of the domestic electric load. The load identification algorithm is the core of the system. Non-intrusive power load intelligent identification systems are generally based on a single model for aggregation, and commonly used models include hidden Markov models, graphical models and deep learning models. Usually, a single model is only effective in identifying certain types of electrical loads, and its universality and generality need to be improved. Improving the decomposition accuracy of the model is the main purpose of implementing non-intrusive powerload monitoring. The stacking integration algorithm is used to optimize the non-intrusive powerload monitoring model. The stacking integration model is divided into two layers. The base model initially decomposes the load and realizes the load accuracy through the meta-model. Experiment results show that the non-intrusive load identification algorithm based on Stacking modeling proposed in this paper can accurately decompose the minute-level sampled data. Compared to a single model, the decomposition accuracy of this method is improved by 8.2%.
Prediction for Tokamak High-power Pulse Load based on Elman Neural Network
Authors: Wang Junjia , Chen Hui , Hu Hejun , Huang Yiyun 
Published: February 2023

Abstract
A dynamic load prediction model of the Elman neural network is suggested to increase the reliability of the tokamak high-power pulse prediction. The method enhances the network’s capacity to process dynamic input by employing a dynamic back-propagation algorithm. A comparison of the simulation prediction results and measured data, depending on the measured data of the EAST (the experimental advanced superconducting tokamak) power bus, shows that the prediction results of Elman are more accurate than the traditional BP neural network, which can describe the impact of the fusion device’s pulsed load on the power grid better and increase the precision of stability analysis.
Comparison of SAGA and GA in Wind Turbine Layout Optimization Based on Modified Two-dimensional Wake Model or Jensen Model
Authors: Yang Wanqing 
Published: February 2023

Abstract
In this paper, Simulated Annealing Genetic Algorithm (SAGA) and a new two-dimensional wake model called 2D_k Jensen model are adopted for optimal wind turbine layout (WTL) in the wind farms and are compared with Genetic Algorithm (GA) and Jensen model, respectively, aiming to minimize the investment cost and maximize the wind power generation as much as possible. The influence of the radial distribution of wake on the equivalent wind speed in the wake superposition region is considered. In the case of single wind direction and single speed, total output power and energy extraction efficiency are both improved when SAGA is applied to the two model conditions respectively, especially for the WTL using the 2D_k Jensen model, these two aspects are significantly improved by 13.75% and 24.10%, respectively, and the objective function is reduced by 19.05%. The results demonstrate that SAGA is more conducive to solving the practical configuration optimization of wind turbines, compared with the original GA.
Research on adaptive active power coordinated control strategy of Zhangbei Flexible DC Grid project
Authors: Song Yantao , Zhong Qidi , Xiao Kun , Gao Zijian , Wu Qingfan , Song Wei 
Published: February 2023

Abstract
Zhangbei Flexible DC Grid project with grid characteristics is different from the previous multi-terminal flexible DC project. Zhangbei Flexible DC Grid project uses a DC breaker (DCB) to isolate DC line faults. Limited by the current domestic research and development level of high-voltage DCB, it does not have overload operation capacity. Against this problem that the overload of DCB endangers the safety of equipment and system after a permanent fault of the DC overhead line, the adaptive power coordination control strategy is proposed when the sending end converter station is connected to the new energy station and the AC grid respectively, The feasibility and rationality of this strategy are verified by the RTDS real-time simulation system of Zhangbei Flexible DC Grid project and provides technical reference for the long-term upgrading and transformation of Zhangbei Flexible DC Grid project.
Optimizing direct normal irradiance prediction of BP neural network based on Logistic Sparrow search algorithm
Authors: Lu Jun , Bai Wangwang , Feng Zhihui , Cai Ping , Chen Baoqi 
Published: February 2023

Abstract
With the wide application of photothermal power stations in power systems, Direct Normal Irradiance(DNI) prediction is very important to improve the economic benefits of photothermal power stations and the utilization of solar energy. The short-term prediction of wind power by traditional Sparrow search algorithm (SSA) optimized BP(SSA-BP) neural network is prone to fall into local optimum, slow convergence rate, and low prediction accuracy. A short-term DNI prediction method based on the Logistic Sparrow search algorithm (Logistic-SSA) optimized BP(Logistic-SSA-BP) neural network is proposed. This paper takes a photothermal power station in Northwest China as the research object. Firstly, the Pearson correlation coefficient is introduced to analyze the environmental data set with a strong correlation with the direct illumination radiation index as the input of the model, so as to avoid redundant data affecting the prediction of the direct illumination radiation index. Secondly, the SSA algorithm and Logistic-SSA algorithm are used to improve BP neural network. Finally, the measured historical data of the photothermal power station is used to simulate the prediction models. The simulation results show that the prediction model based on Logistic-SSA-BP has better prediction accuracy than SSA-BP, and is more in line with the production and operation requirements of the photothermal power station.
A Substation Live Detection Method on Communication Protocol Data Parsing and Data-level Fusion of Neural Networks
Authors: Zhang Yanyan , Ning Xuefeng , Li Long , Chen Yunan , Xi He , Wang Shaorong 
Published: February 2023

Abstract
In this paper, a data parsing method based on database communication protocol technology and a data fusion method based on the neural network are proposed, which solves the shortcomings in the data communication process of the substation, and provides an intelligent method for data analysis and fusion. The data parsing, transforming method, process, and implementation scheme based on the communication protocol is described in this paper. Firstly, the digital expression method of the data message is proposed. Secondly, the message recognition method based on the feature field is introduced. Through the analysis and heuristic of the feature fields, the protocols of the messages are determined. then the data interface relationships in the system are stored in a separate database. When the parsing process is completed, we use data pre-processing, data cleaning, data integration, and other methods to fuse the data of each source. Finally, the unstructured data fusion method based on the VGG-19 model is used to fuse the data. At the same time, this paper also describes the scheme of some principles step by step and introduces the relevant technical details.
Research on Power Grid Short-term Load Forecasting Method Based on Data Mining
Authors: Bi Yanbing , Su Yanlong , Yang Jun , Wang Yuling , Hu Yu , Pan Shitong 
Published: February 2023

Abstract
Because the abnormal location of the power grid load is mismarked, the average relative error increases in the process of forecasting. This paper proposes to study and analyze the method of short-term load forecasting based on data mining. It preprocesses the load characteristic clustering and related data, marks the abnormal position of a power grid within a calibrated mining range according to the load fluctuation state, and defines the short-term load forecasting range. It also constructs a data mining power load forecasting model through the acquired real-time forecasting data and information, designs layered and staged load forecasting links, and corrects and calculates by adopting data mining. The prediction residual error is obtained. The stage prediction standard is further determined, and the prediction processing is realized. The final test results show that the data mining short-term load forecasting test group designed in this paper has a small fluctuation range, indicating that the prediction error has been greatly controlled in the practical application process. The prediction error is small, the accuracy is improved, and it has practical application value.
A State-of-Charge estimation method of Lithium battery based on BP neural network
Authors: Wei Wang , Xiangdong Meng , Han Liu , Gang Li 
Published: February 2023

Abstract
In the proposal of Dual-Carbon plan and the rapid development of clean energy generation, the energy-storage has become an important development direction in the power field. In this paper, the key parameter of the lithium battery-based energy-storage system—SOC estimation method are studied, the second-order resistance-capacitance model of the lithium battery is analysed. The equivalent circuit model of the lithium battery is established. The calculation basis of SOC in lithium battery is determined. The SOC estimation method of the battery is realized by BP neural network. The availability and performance of the advanced method is proved by the simulation analysis results based on the public lithium battery data.
Optimization of a salt gradient solar pond for air heating application
Authors: Verma Sunirmit , Das Ranjan 
Published: February 2023

Abstract
In this study, it is demonstrated that when a solar pond is used for an air heating application like crop drying, domestic space heating, greenhouse heating etc., it is important to account for pumping power in the calculations. When air is the working fluid, the magnitudes of extraction and pumping power are comparable. Therefore, a new parameter is defined in context of solar ponds used for air heating, namely, effective power (defined as power extracted minus pumping power). At first, using a steady-state analysis, a closed form solution is derived for extraction power. The pertinent expression for pumping power is taken from the literature. Thus, an analytical expression for effective power is obtained and thereafter, an optimization analysis has been carried with the objective of maximizing this effective power. The analysis is used to calculate optimum values of air mass flow rate and pond’s non-convective zone thickness at which maximum effective power is registered. The corresponding maximum effective power and the associated air outlet temperature are also calculated. This work thus is believed to prove useful for designing of solar ponds that are used for air heating applications in a manner to ensure maximal system performance.
Analysis of axial windage loss in air gap of high-speed motors under high Taylor number
Authors: Song Yuchen , Cao Jiwei , Zhong Chengbao , Chen Jialiang , Li Liyi 
Published: February 2023

Abstract
In the field of ultra-high-speed motors, windage loss rises rapidly with the increase of rotational speed, and even becomes the main part of motor loss. However, most researches of windage loss are based on empirical formulas, which has a narrow scope of application. These formulas could not guarantee the accuracy when calculating large Taylor number flow in high-speed motor air gap, especially under axial ventilation condition. This paper focuses on the interaction between Taylor vortices and axial flow, analyses the influence of Taylor number on the behaviour of Taylor vortices and windage loss under non-axial and axial ventilation. First, another form of friction coefficient related to rotation speed is proposed using dimension analysis. Second, the scale of Taylor vortices is estimated and how it affects windage loss is discussed. Finally, a deceleration experiment taking a 250krpm induction motor is used to verify the accuracy of the proposed windage loss model.
Safety evaluation on devices of high-pressure hydrogen refueling stations
Authors: Shi Zhuoming , He Pengfei , Wang Benjin 
Published: February 2023

Abstract
Hydrogen refuelling stations are established in various places nowadays. With the development on the hydrogen refuelling under higher pressures, the risk of hydrogen leakage on the devices, specifically the compressor and the dispenser, draws increasing concerns for these stations. In this paper, different cases of hydrogen leakage and combustion are evaluated for these devices with respect to various conditions, based the prototypes designed for a high-pressure hydrogen refuelling station in China. The results show that the limited hydrogen flow in the compressor ensures that no serious accident will occur after leakage. As for the dispenser, the cases for internal leakage on the main tube and the external leakage on the nozzle hose are studied and compared. According to the results, the harmful and lethal distances are obtained. Also, the effect of wind speed on the gas diffusion field and the temperature field are quantified by the simulation. It can be stated that the severity of ignition will increase with consideration of the wind. The results can be beneficial to the timely detection of the possible hydrogen leakage of the devices, and to the optimized layouts for the design of stations.
An auxiliary design system for industrial heat supply system considering carbon reduction benefit
Authors: Ren Jiayi , Wang Fei , Fan Zikai , Han Siwei , Zheng Jiaqi 
Published: February 2023

Abstract
Decarbonization of industrial process heating plays a crucial role in China’s carbon neutrality strategy. Traditionally, industrial users make decision on heat supply system design based solely on economy, with little regard for carbon emission. This study proposes an auxiliary design system to help the designers choosing proper industrial heating supply scheme considering carbon reduction benefit. Through traversal the existing scheme database, all feasible solutions are screen out and then evaluated by calculating an proposed comprehensive economy evaluation index. The comprehensive economy evaluation index takes carbon reduction as revenue since industry users will be able to trade their carbon emission rights at the carbon markets though CCER programs in the future. Future carbon price given by the carbon price prediction module is used to calculate the revenue user can get from carbon reduction. Grid emission factor prediction module makes prediction for changing grid emission factor, which is also involved in the evaluation to consider the benefit from continuously increasing green power in the future. A case study is given with heating demand data of a food factory to shown that the proposed method can reflect the value of carbon reduction, which helps to promote low-carbon technologies in the future.
Research Progress of Electroplated Micropore for Interconnection Technology
Authors: Sun Zhaoning , He Guanghui , Zhao Zhenbo , He Xiao , Zhao Hao 
Published: February 2023

Abstract
Electroplated micropore interconnection is one of the core technologies to realize the electronic connections of the high-density multilayer Printed circuit board (PCB). The quality of electroplated copper layer is a key factor for the reliability of PCB and electronic components. Focusing on the acid copper plating process, this paper expounds the development of the electroplating solution and additives, the mechanisms of electroplated copper deposition and hole filling mechanism, and the process control of electroplating, which will provide a reference for the in-depth study of the electroplating micropore interconnection technology.
Modification and performance evaluation of Konjac gum in drilling fluid
Authors: Shan Hou 
Published: February 2023

Abstract
Because of the rock carrying property of drilling fluid, it is usually necessary to add bentonite and polymer into the drilling fluid to improve the viscosity of the drilling fluid. However, bentonite and polymer will seriously affect the rheological property, sand carrying property, drilling speed, degassing effect, etc. of the drilling fluid. Therefore, it is necessary to develop a new environment-friendly low viscosity cutting agent for drilling fluid. In this paper, konjac gum was modified by metal complexation. Some modified konjac gum were studied. The effects of different dosages of modified konjac gum on the rheological properties of drilling fluid were analyzed through infrared spectrum characterization, thermogravimetric analysis and compatibility evaluation. The experiment proved that heavy metal cations and inorganic flocculant polyaluminum chloride/iron can coordinate with konjac gum, to form a cation konjac glucomannan chelate system, which enhances the three-dimensional network structure of the molecule; The shielding effect of metal cations will weaken the electrostatic repulsion between -O- in konjac glucomannan and compete with each other, thus affecting the rheological properties. The modified konjac gum not only improves the rheological property of its aqueous solution but also improves its water solubility. The operation process is simple and green.