bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thermal Analysis and Optimization of Energy Storage Battery Box Based on Air Cooling
Authors: Wang Lulu 
Published: September 2023

Abstract
For energy storage batteries, thermal management plays an important role in effectively intervening in the safety evolution and reducing the risk of thermal runaway. Because of simple structure, low cost, and high reliability, air cooling is the preferred solution for the thermal management. Based on a 50 MW/100 MW energy storage power station, this paper carries out thermal simulation analysis and research on the problems of aggravated cell inconsistency and high energy consumption caused by the current rough air-cooling design and proposes the optimal air-cooling design scheme of the energy storage battery box, which makes the structure of battery module more reasonable and the temperature distribution between the batteries more uniform. It has a certain guiding significance for energy saving, consumption reduction, and stable operation of energy storage systems.
Research on Control Strategy of Hybrid Energy Storage System Participating in Primary Frequency Regulation of Power Grid
Authors: Wei Ning , Yan Xin , Han Xinglei , Ma Xiaolu , Cui Jian , Sun Weitao 
Published: September 2023

Abstract
With the ongoing development of China’s power system, there is a gradual increase in the proportion of new energy power generation. However, the randomness and volatility associated with new energy power generation can lead to increased frequency fluctuations in the power grid, posing a significant challenge to power system frequency regulation. In this paper, we investigate the control strategy of a hybrid energy storage system (HESS) that participates in the primary frequency modulation of the system. We analyze the advantages and disadvantages of various types of new energy storage from both technical and economic perspectives and perform an applicability analysis system to select lithium batteries and supercapacitor energy storage for forming a HESS. We propose a virtual droop control strategy to regulate the output of the HESS in the primary frequency regulation of the system. Finally, we build a simulation model that includes the HESS and power grid on the MATLAB/Simulink simulation platform and conduct a simulation analysis. The results show that the proposed strategy is superior and effective in controlling the HESS’s output when participating in the primary frequency modulation of the system.
Wide frequency band oscillation mechanism and suppression measures for grid-interfaced PV inverters based on impedance analysis
Authors: Zhao Chaofan , Chen Laijun , Su Xiaoling , Yang Jun , Kang Pengpeng 
Published: September 2023

Abstract
With the rapid development of PV and wind power, the energy structure of the power grid is changing dramatically. The dominant power supply role played by conventional power sources such as hydropower and thermal power is gradually replaced by PV and wind power. All the power electronics-based equipment makes the power system stable form more complex, and one of the most significant challenges is wide frequency band oscillation (WFBO). This paper proposes an impedance analysis method for PV inverters in high proportion to new energy-integrated power systems. First, we investigate WFBO mechanism caused by the PV inverter based on its impedance model, and then a detailed impedance model of parallel PV inverters is developed to study WFBOs in PV stations. Finally, the WFBO suppression measure based on active damping control is proposed. Simulation results prove the correctness and feasibility of the active damping control strategy.
A study of the impact of voltage sag on sensitive loads at low-voltage distribution areas
Authors: Xu Tao , Yang Junqian , Wang Wei , Chen Jiong , Zhang Wen , Jiang Yihao , Zheng Xinying , Wang Qianggang 
Published: September 2023

Abstract
Under the background of the dual carbon goal and the new power system, the power system becomes more complex and diverse, and voltage sag events caused by short-circuit faults, overloads, switching operations, large motor starting, and distributed generation access are more frequent. On the other hand, with the increasing sensitivity of low-voltage distribution network electrical equipment to power quality, especially for precision process control equipment using computers, power electronics, and automation technology, new energy generating units, and electric vehicle charging facilities, voltage sag accidents will cause difficult-to-estimate losses in equipment production and operation, and frequent voltage sag events have become one of the main power quality problems affecting reliable power supply and normal equipment operation. To address this problem, this paper analyzes the impact of voltage sag on low-voltage substation loads from a mechanism perspective, establishes a voltage sag tolerance curve for electronic appliances through experimental research, and simulates and verifies the effectiveness and correctness of the method in this paper using a 3 MW-rated induction generator connected to the distribution network as an example.
Short-Term Load Forecasting Based on Improved VMD and KELM
Authors: Liu Liyuan , Zhou Bonan , Wang Yongqian , Luo Xianjue 
Published: September 2023

Abstract
To address the problem of low accuracy of short-term load prediction in the past, this thesis uses a prediction model with neural network LSTM as the main body and constructs an adaptive and dynamically adjusted rolling load prediction model to meet our load prediction needs through VMD decomposition, kernel limit learning machine, and correlation analysis techniques. To reduce the maximum load error and the overall result error, a correction algorithm is proposed to improve the VMD to minimize the impact of load decomposition; after that, the model is used to analyze a load of a day in a region to verify the superiority of the prediction model. The example shows that the load forecasting method can help to make more accurate predictions of short-term loads.
Study on Compression Ratio Optimization of M100 Methanol Engine
Authors: Chen Chen , Han Dong , Ma Zhicheng 
Published: September 2023

Abstract
A turbocharged and intercooled diesel engine was transformed into an ignited M100 methanol engine by adding a spark plug to the cylinder head. A one-dimensional model of the methanol fuel engine was established using the bench test data as the boundary conditions, and the accuracy and accuracy of the model were verified. Methanol has better octane number and explosion resistance than traditional fuels, increasing the compression ratio of the engine will improve the power and fuel efficiency of the methanol engine. Based on the original compression ratio of 12, 8 compression ratio simulation schemes were selected, including 11.4, 11.7, 12, 12.3, 12.6, 12.9, 13.2, 13.5, etc., and the engine performance and the combustion state in the cylinder changed with the compression ratio were analyzed by simulation calculation, and the engine was optimized and improved. The results show that when the engine compression ratio reaches 12.9, the engine performance reaches the optimal value.
Key technologies research and application of green power traceability based on blockchain
Authors: Wen Tingting , Li Da , Duan Tingting , Guo Qinglei , Zhao Lihua 
Published: September 2023

Abstract
Green power traceability is a crucial link to ensure the green power transactions’ authenticity, effectiveness and credibility. It’s an essential means to promote the large-scale development of green power. However, there are many links in the whole chain of green power. The data involves multiple departments and institutions. So it is not easy to trace the authenticity. This paper first introduces the entire chain of green power traceability and critical information. Secondly, it proposes the green power traceability scheme and key technologies. Then, it designs the system and functional architecture of green power traceability based on blockchain. Finally, it analyses the application effect of the system in green power trading and the Winter Olympics. This research provides the technical reference for the authenticity and traceability of green power. It helps the green-low-carbon transformation of energy consumption structure.
Cascaded H-bridge Type Power Electronic Transformer Electromagnetic Interference Study Based on Parallel Simulation
Authors: Sun Haifeng , Liu Xiaopeng 
Published: September 2023

Abstract
Power electronic converter (Power Electronic Transformer, PET) is widely used in power systems, so the research on PET electromagnetic disturbance (Electro Magnetic Interference, EMI) cannot be ignored. PET consists of many insulated gate bipolar transistors (Insulate-Gate Bipolar Transistor, IGBT). A large number of converter levels will cause problems such as small simulation steps and long simulation times. To solve the above problems, firstly, the IGBT and its anti-parallel diodes are modeled by an 8-node model to reduce the complexity of the system; secondly, after dividing the model into fine-grained models, CUDAC coding is used to realize the parallel operation of the system on the graphics processing unit (Graphics Processing Units, GPU). Finally, the feasibility of the proposed method is compared with the PSCAD simulation results.
Influence of Large-scale Distributed Photovoltaic Access on Harmonic Characteristics of Distribution Network
Authors: Hong Xiaoai , Tian Lijun , Yin Xunpeng , Xu Minghua , Han Lei , Liu Xingjian 
Published: September 2023

Abstract
With the development of PV on a large scale, developable rooftop distributed PV becomes a high-quality resource to achieve the double carbon goal. Compared with single-node and low-permeability PV access, large-scale photovoltaic access makes the power grid present high-power electronic characteristics, which has a more complex impact on the harmonic characteristics of the distribution grid. To comprehensively reveal the influence of large-scale distributed photovoltaic access on the harmonicity of the distribution network, this paper first conducts a theoretical analysis of the harmonic generation mechanism. On this basis, build a simulation model based on Matlab/Simulink, considering the measurement indexes of low harmonics and supraharmonics, and the influence of grid-connected location, grid-connected capacity, background harmonics, access mode, and other factors on the harmonic characteristics of the distribution network is simulated and analysed. According to the simulation results, large-scale distributed PV access significantly increases the content of low and supraharmonics in the distribution network, which has a great impact on the harmonic characteristics of the distribution network.
Analysis Method for Photovoltaic Absorption Capacity of Distribution Network Based on Risk Quantification and Network Reconstruction
Authors: Xue Guiting , Zhang Bowen , Zhao Yuan , Qi Hong , Liu Kaixin , Yang Xuetao , Xiao Xiangtao 
Published: September 2023

Abstract
The random characteristics of photovoltaic (PV) power will cause changes in node voltage and line power, which is an important factor affecting the photovoltaic absorption capacity of the distribution network (DN). So as to accurately evaluate the impact of photovoltaic randomness on the absorption capacity of DN, an analysis method of photovoltaic absorption capacity of DN is proposed based on risk quantification. Firstly, a photovoltaic absorption capacity evaluation model is proposed based on risk quantification, which considers the randomness of load and PV power and can improve the accuracy of photovoltaic absorption capacity assessment. Secondly, the photovoltaic absorption capacity DN is calculated with the network topology and photovoltaic access capacity of the DN as the optimization variables. The probabilistic power flow is performed by linear power flow, which effectively improves the calculation speed. Finally, the proposed PV absorption capacity analysis method is simulated and analyzed. The simulation results show that the proposed method can effectively take into account the impact of load and photovoltaic randomness and improve the photovoltaic absorption capacity of DN.
Application of Gray Wolf Optimization Algorithm in Urban Electricity Load Forecasting Model
Authors: Tian Hailin , Wang Fang 
Published: September 2023

Abstract
A combined prediction model based on long short-term memory neural network (LSTM) and convolutional neural network (CNN) is proposed in order to increase the prediction accuracy of short-term load. To address the issue that the gray wolf optimization (GWO) search process is prone to falling into local optimum. An improved grey wolf algorithm (IGWO) is proposed to update the convergence factor using the lower incomplete gamma function to improve the global optimization performance. The Dropout technique is used to improve the generalization ability of the model; the network layers are improved by increasing the initialization of weights; the model is trained using an adaptive moment estimation (Adam) optimizer, test data are input to the trained neural network model, and finally, the optimized model is used for prediction. The high prediction accuracy of the proposed method is demonstrated experimentally.
Research on Multi-carrier PWM Technology Based on Modular Multilevel Inverter
Authors: Guo Yifeng , Shi Qingyu , Huang Limin , Guo Shicheng , Zhang Li , Fan Dakun 
Published: September 2023

Abstract
The modular multilevel inverter circuit structure is introduced, three traditional multi-carrier PWM modulation strategies for modular multilevel inverter circuits are studied and analyzed, and the inverter-side output voltage spectrum is analyzed by simulation. Based on the traditional multi-carrier PWM modulation strategy, this paper uses the PWM technology combining multi-reference wave and multi-carrier to analyze and simulate, and builds a modulation model and a circuit model of the modular multilevel inverter circuit in the Matlab/Simulink environment. Compared with the simulation results of the traditional multi-carrier PWM strategy, the multi-carrier modulation algorithm of the multi-reference wave can slightly reduce the total harmonic distortion rate. At the same time, the multi-carrier modulation algorithm of multiple reference waves can ensure that the capacitor voltage values of each submodule of the modular multilevel inverter circuit are equal so that there is no circulation phenomenon between each phase. Finally, a physical platform is built to verify the authenticity of the simulation experiment.
Topology of Wind Power DC Collection Converter Based on Double Bus Structure and Its Power Balancing Control Strategy
Authors: Lu Yajun , Zhong Qidi , Wang Shuai , Liu lin , Sun Jinping 
Published: September 2023

Abstract
Wind power collection technology is the key to grid-connected wind power generation. Compared with AC wind integration, DC wind power collection is more advantageous. Using a triple active bridge (TAB) converter as a submodule, this paper proposes a wind power DC converter topology and its control strategy for medium-voltage dc (MVDC) grid connection. The converter adopts a double bus structure, with the MVDC bus connected to the grid and the low-voltage dc (LVDC) bus handling the mismatched power. It has independent maximum power point tracking (MPPT) control, power balancing, and autonomous voltage balancing on the output side. The converter system is simulated and verified on the MATLAB/SIMULINK software platform. The simulation results show the effectiveness of the control strategy and the ability of the converter to operate stably under multiple conditions.
Automatic generation method of power grid maintenance operation ticket based on GA-ANN optimization algorithm
Authors: Chen Zhangguo , Hu Chao , Qiao Zhizhong 
Published: September 2023

Abstract
The automatic generation method of power grid maintenance operation ticket based on the GA-ANN optimization algorithm is designed. An automatic generation model of power grid maintenance operation ticket, including data communication, power grid information topology, task planning, path search, path selection, operation ticket generation and simulation of rehearsal agent, is built. This paper takes the operation sequence generated by different equipment performing operation tasks as the training sample of the GA-ANN model, obtains the change rule of operation sequence of each equipment agent, and realizes the automatic generation of maintenance operation tickets for different equipment. It is verified by experiments that the time of automatic generation of operation tickets is fast and can complete the maintenance and invoicing of various equipment.
Research on the operation control strategy of optical storage and DC microgrid
Authors: Wu Jinming , Xu Aoran , Peng Ye , Zhang Xiaotong , Li Zezheng , Feng Yuexin 
Published: September 2023

Abstract
Compared to traditional alternating current (AC) power grids, direct current (DC) microgrids have outstanding technical and economic advantages and bear great development potential and application value. In this paper, the operation control strategy of optical storage DC microgrid is studied. Firstly, the structural composition and related characteristics of the DC microgrid are systematically analyzed. Secondly, the coordinated control strategy for the DC microgrid during off-grid operation, grid connection operation, and load optimization is studied, and the mathematical model of the DC microgrid is established. Finally, the simulation analysis of the model of the DC microgrid is conducted, and the results show that the coordinated control strategy can effectively stabilize the output power, promote the power balance of the power grid, and provide fundamental guidance and reference for the efficient conversion and control of power grid, which has important practical significance.
Adopting multiple vision transformer layers for fine-grained image representation
Authors: Sun Fayou , Ngo Hea Choon , Yu Yelan , Xiao Zhengyu , Meng Zuqiang 
Published: September 2023

Abstract
Accurate discriminative regions proposal has an important effect for fine-grained image recognition. The vision transformer (ViT) brings about a striking effect in computer vision duo to its innate muti-head self-attention mechanism. However, the attention maps are gradually similar after certain layers and since ViT adds classification token for perform classification, it is unable to effectively select discriminative image patches for fine-grained image classification. To accurately detect discriminative regions, we propose a novel network AMTrans, which efficiently increases layers to learn diverse features and utilizes integrated raw attention maps to capture more salient feature. Specifically, we employ DeepViT as backbone to solve the attention collapse issue. Then, we fuse each head attention weight within each layer to produce attention weight map. After that, we alternatively use recurrent residual refinement blocks to promote salient feature detection and then utilize semantic grouping method to propose the discriminative feature region. A lot of experiments prove that AMTrans acquires the SOTA performance on three widely used fine-grained datasets under the same settings, involving Stanford-Cars, Stanford-Dogs and CUB-200-2011.
Optimization of ICANet lightweight human pose estimation based on HRNet
Authors: Hou Qing , Li Kun , Xu Zhen 
Published: September 2023

Abstract
The existing high-resolution human pose estimation models have low applicability in practical applications due to their large parameter quantity and high computational complexity. To address these issues, this paper proposes a human pose estimation network with a lightweight inverted residual coordinate attention network (ICANet) based on the high-resolution network (HRNet). With the introduction of CoordAttention mechanism and the inverted residual module, this paper proposes two lightweight network modules, namely ICAneck and ICAblock, which not only reduce model parameter quantity and computational complexity, but also achieve feature enhancement of long-range dependence and precise position information in spatial directions of the feature map. Experimental results show that compared to HRNet, the ICANet model proposed in this paper reduces its parameter quantity by 53.7% and computational complexity by 32.4% on the COCO validation set, and lowers its parameter amount by 53.7% and computational complexity by 32.6% on the MPII validation set. Practical applications prove that ICANet still achieves high-precision detection of human key points with fewer parameters and lower computational complexity, and has higher applicability and practicality compared with common human pose estimation networks such as the Stacked Hourglass Network (Hourglass), Cascaded Pyramid Network (CPN), and SimpleBaseline, and therefore has better applicability and practicality.
Comparative evaluation for alternative variable importance rankings for pedestrian injury severities
Authors: Cheng Yichi , Zhang Yongping 
Published: September 2023

Abstract
Little research is dedicated to evaluating the performance difference of various metrics in ranking predictor importance in the traffic safety field. To this end, the main objective of the current paper is to evaluate and quantify different methods for sorting the variable importance related to crash severity. A comprehensive database for pedestrian-related crashes in the state of California was developed. Four popular measurement metrics used in the past were chosen for evaluation purpose: Mean Decrease Accuracy (MDA), Mean Decrease Gini (MDG), log-likelihood ratio test associated with multinomial logit model, and Principal Component Analysis (PCA). The former two metrics come under the same umbrella of the Random Forest (RF) technique, while the latter two are methods belonging to different domains. The results show the alternative methods yield different variable importance rankings with PCA being isolated from others. The two methods under the same domain of the random forest, or MDG and MDA, have the most common results, but still reveal a 17% ranking difference. It is anticipated that the results could raise more awareness of the importance of selecting the appropriate metrics to evaluate the predictor importance from different perspectives.
Monte Carlo simulations of LUOSHU: a new 46-meter small-angle neutron scattering instrument in China
Authors: Tang Y.F. , Zhong S.Y. , Yan G.Y. , Chen J. 
Published: September 2023

Abstract
The LUOSHU small-angle neutron scattering spectrometer is one of the neutron scattering instruments under construction at China Mianyang Research Reactor. Quantitative characterization of nanoparticles is now in a higher demand for research interests in material science, physics, chemistry, biology. Therefore, as a general-purpose small-angle scattering spectrometer, LUOSHU is aimed to provide a large accessible Q range, flexible resolution and sufficient neutron flux. Monte Carlo simulations have been performed on LUOSHU from the neutron source to sample platform to optimize the parameters of the bender part, collimation system, and the velocity selector. And the instrument layout is optimized base on the simulation results. Finally, the maximum neutron intensity of the neutron beam at the sample stage can reach 1.01 × 107 N·s-1·cm-2 and its divergence is 0.63.