bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of control system on the DC impedance characteristics of low-medium frequency bands and stability of modular multilevel converter based HVDC system
Authors: Lin Xin , Guo Chunyi , Wang Yanning , Yang Shuo , Li Chunhua , Sun Xu 
Published: April 2022

Abstract
The resonance in modular multilevel converter based high voltage direct current (MMC-HVDC) system poses a potential threat to the safe and stable operation of the power grid. The mechanism of the resonance phenomenon is complicated, which is closely related to the impedance characteristics of MMC system. By establishing the DC impedance model of MMC-HVDC system at both ends, the influence of parameters of control system in converter stations on the DC impedance characteristics of MMC system was analyzed, revealing the main influencing frequency bands and effect of each control parameter. The DC impedance characteristics of low and medium frequency bands could be shaped in a targeted manner by adjusting the control parameters, the stability margin of the system will be improved as well. The correctness of the results was verified by time domain simulation. The analysis results of this article can provide reference for the selection of the parameters of control system and the improvement of stability margin of MMC-HVDC system.
Identification method of the feeder-transformer relationship in active distribution network based on power restoration information matrix
Authors: Chen Li , Lin Dongyue , Lin Yan , Chen Xue , Zhang Wei 
Published: April 2022

Abstract
In order to solve the problem of identification and verification of topological relationship between feeder and distribution transformer in distribution network, a new identification method of feeder-transformer relationship in active distribution network based on power restoration information matrix is proposed. Firstly, the identification model of feeder-transformer relationship is established through the distribution network feeder-transformer information matrix. Then, through the identification model of feeder-transformer relationship, the real-time power restoration information matrix (RRIM) and standard power restoration information matrix (SRIM) are established to describe the power restoration distribution transformer under real-time and standard conditions. Finally, the feeder-transformer relationship error information matrix is established through the difference between RRIM and SRIM to describe the name information of distribution transformer with misconnection and leakage on a feeder. The case analysis shows that this method can identify the feeder-transformer relationship of distribution network based on the topology information of DMS and real-time distribution transformer power restoration information. The method is simple and reliable, can better meet the actual needs of the site, and has good popularization value.
Design and application of distributed photovoltaic transaction consensus mechanism based on blockchain
Authors: Han Qi , Qi Yifei , Deng Hua , Liu Han 
Published: April 2022

Abstract
In recent years, distributed photovoltaic power generation has developed rapidly, but it has also brought many problems, such as poor equipment reliability, complex operation control, and many management subjects. Under the situation of the development of the electricity market, it is necessary to solve the problem of ensuring the data security of participating in the electricity market. Blockchain technology has the characteristics of traceability, non-tampering of information, openness and transparency, etc., which can satisfy massive transactions and reduce the burden of traditional power transactions. Therefore, based on the typical characteristics of blockchain, this paper analyzes the common attributes of blockchain and electricity market transactions, and adopts the DPOS (delegated proof of stake) consensus mechanism to realize distributed photovoltaic transactions. On this basis, this paper utilizes the consensus method based on node credit value and applies it to the actual microgrid distributed photovoltaic power station. The experimental results show that the transaction consensus mechanism proposed in this paper has certain advantages in transaction efficiency and transaction cost, which can provide a practical basis for blockchain to realize distributed photovoltaic transactions.
Analysis and research on breakdown cause of lightning arrester at low voltage side of 220kV transformer
Authors: Qu Limin , Li Tong , Wang Chao , Zhang Jian , Zhang Dewen , Zhang Hang , Gong Mingchen 
Published: April 2022

Abstract
This paper describes an event of zinc oxide arrester breakdown at low voltage side due to single-phase grounding at medium voltage side of 220kV transformer. Combined with the system wiring mode, equipment damage, system background and test data, the causes of fault and the transmission mechanism of overvoltage are studied in detail, It is concluded that the single-phase grounding short circuit on the medium voltage side leads to the displacement voltage of the neutral point, and the overvoltage is transmitted to the low voltage side through the capacitive coupling between the transformer windings, resulting in the breakdown of the arrester. The correctness of the conclusion is verified by calculation, and reasonable rectification measures and suggestions are put forward to effectively limit the generation of overvoltage on the low-voltage side of the transformer and ensure the safe and stable operation of the power grid.
Commutation failure detection of DC system based on S transform entropy
Authors: Fang Zhi-wen 
Published: April 2022

Abstract
The failure of commutation will cause the abrupt change of the operating parameters of the DC system. In this paper, the mechanism and physical process of commutation failure are analyzed. In view of the changing characteristics of inverter side valve current and DC current in the case of commutation failure, two kinds of S transform entropy are introduced into the detection of commutation failure of DC system in combination with S transform entropy’s excellent signal change feature recognition ability. In addition, the detection performance of the two s-transform entropies is fully considered in the case of no commutation failure, critical commutation failure and commutation failure. The simulation results based on PSCAD/EMTDC preliminarily verify the validity and accuracy of the two kinds of S transform entropy in dc system fault detection, which provides a basis for the further application of S transform entropy in dc system fault detection field.
Research and application of voltage coordinated control method for distributed photovoltaic connected to low voltage distribution network
Authors: Yin Hongxu , Zhang Bing , Song Liang , Liu Jie , Zhang Yingjie 
Published: April 2022

Abstract
Distributed photovoltaic access is likely to cause the problem of voltage limit violations, which greatly affects the consumption of distributed photovoltaics. At present, the existing methods do not consider the coordinated control of multiple voltage levels. In response to this problem, this article firstly analyzes the multi-voltage coordinated control method of distribution network. On the basis of data analysis, this paper formulates a control method for regulating the low-voltage distribution network by using the medium-voltage network.. In addition, optimized calculations were carried out on the voltage condition range of distributed photovoltaic access to the low-voltage distribution network. Finally, this paper analyzes the impact of distributed photovoltaic access on the voltage level of the distribution network, and provides a technical reference for the economic operation of the distribution network.
A High-Performance Cloud-based Remote Sensing Data Reprojection Method
Authors: Dong Yi , Xu Chen 
Published: April 2022

Abstract
Remote sensing (RS) data are the cornerstone of the digital earth. At present, the productions from Earth observation satellites are updated frequently with refined data quality. However, traditional methods are implemented mostly based on a single machine. When processing large-scale data in batches, there are limitations in computing power and storage, and the expansion is relatively cumbersome. This demands a more efficient and complex computation platform, such as cloud computation. Specifically, the reprojection procedure, unlike other data processing procedures, is both computationally intense and I/O-intense. This paper proposes a high-performance cloud-based RS data reprojection method called OCRM (optimised cloud-based reprojection method). First, the data process flow of reprojection was optimised by improving the I/O efficiency and computational efficiency. Second, with scheduling and controllers under cloud computation, high-performance massive RS data reprojection was achieved. The overall performance of the RS data reprojection module with the optimised algorithm in a single machine considerably outperformed that of the commercial software. The scheduling module can maximise the use of cloud resources to achieve high-performing reprojection calculations. The results indicate the stable performance of the proposed method, and we are working to expand the structure of OCRM to other computationally intensive remote sensing processes, such as data preprocessing and data mining with deep learning.
HBase-based spatial-temporal index model for trajectory data
Authors: Liu Hong , Yan Jining , Huang Xiaohui 
Published: April 2022

Abstract
The development of global positioning technology and the popularization of smart mobile terminals has led to a trend of rapid growth in the data volume and coverage of trajectory data. This type of data has the characteristics of fast update speed, high dimensional characteristics, and a large amount of information that can be mined. Many technology companies will use trajectory data to provide location-based services, such as vehicle scheduling and road condition estimation. However, the storage and query efficiency of massive trajectory data have increasingly become bottlenecks in these applications, especially for large-scale spatiotemporal query scenarios. This paper solves this problem by designing a trajectory data index model based on GeoSOT-ST spatiotemporal coding. Based on this model, the HBase-based trajectory data storage scheme and spatiotemporal range query technology are studied, and MapReduce is used as the calculation engine to complete the query attribute condition filtering in parallel. Comparative experiments prove that the index model proposed in this paper can achieve efficient trajectory data query management.
Study on the Expression of Spatiotemporal Patterns in Marine Ecosystems
Authors: Linchong Kang , Haiyan Huang , Xiaoyi Jiang , Guocheng Zuo , Yi Wang , Chang Liu 
Published: April 2022

Abstract
The ocean is a three-dimensional ecosystem with a wide range of temporal and spatial scales, thus making the visual expression of marine ecological characteristics extremely challenging. What types of marine ecosystems exist in a region, where are they distributed, what is their current status, and what are their changing trends? These are important questions faced by marine managers and decision makers. Based on the analysis of the relationship between biology and environment, this paper conducts ecological classification and data production for various marine elements. This paper expresses spatiotemporal marine ecological characteristics using a four-level general framework, including sea zone, bathymetry space, water space, seabed space. The bathymetry reflects the relevant marine topographic and landscape characteristics, and the water space and seabed space are composed of water environment, sediment environment and living organisms respectively. The framework’s main functions are the display, description, and statistical analysis of ecological characteristics, and display the spatiotemporal distribution pattern. These methods were combined with the construction of a database and R&D of system functions to fill the gap in this field in China and provide decision support for marine ecosystem management.
Active Contour Building Segmentation Model based on Convolution Neural Network
Authors: Liu Mengjia , Liu Peng , Song Bingze , Zhang Yuwei , Zhang Luo 
Published: April 2022

Abstract
In high-resolution remote sensing images, artificial features on the surface account for a large proportion. In artificial features, buildings, as special artificial features, buildings have different shapes. They are easily affected by light, so it takes a long time to extract using traditional image segmentation methods. It can't effectively design feature engineering to depict the high-dimensional features of the target building. We propose an active contour model based on a convolution neural network, which integrates the prior knowledge and constraints of active contour model, such as continuity of boundary, smooth edge, and geometric characteristics of buildings, into the learning process of convolution neural network to realize the close unity of ACM and CNN. According to our work, a fundamental end-to-end trainable image segmentation framework which is composed of convolution neural network (CNN) and ACM with learnable parameters is implemented, the problem of semantic segmentation of buildings in aerial images was dealt with, the model was evaluated on the publicly available dataset called Vaihingen, and some parameters were explained. In building semantics, the active contour model based on a convolution neural network has good performance.
A Standardized GIS Web Service Oriented Shared-nothing Architecture - Taking Species Distribution Modeling as an Example
Authors: Liu X Y , Yu J S D , Wei D D , Tong R J , Li D Y , Luo S 
Published: April 2022

Abstract
The rapid development of big data analytics technologies and tools, particularly the related distributed computing technologies, which divide large problems into smaller ones, provides important technical support for developing big earth data research. To realize the effective utilization of big data computing resources, it is important to overcome the potential incompatibility and inconsistency among the distributed computing nodes. Using standardized GIS web service interfaces is a promising approach to link existing heterogeneous GIS platforms through standardized protocols and establish a universal, platform-independent computing layer to improve the efficiency of computing resources for big earth data analysis. This research analyzed the requirements of distributed computing, and further proposed a standardized GIS web service-oriented shared-nothing architecture by comparing various mainstream distributed computing architectures and related web service specifications in the industry. For example, the prediction of Lophelia pertusa coral distribution by random forest is the application of the method discussed in this paper.
The impacts of trees’ canopy occupation on pedestrian thermal environment of township streets
Authors: Yamin Liu , Ding Lin , Yongbing Zhu , Chongcheng Chen 
Published: April 2022

Abstract
This paper measures the effect of trees’ canopy occupation on township street pedestrian thermal environment by ENVI-met software. The demonstration experiment was conducted in two streets with different features; street A (NE-WS) with 28% buildings coverage and 0.36 AR (street aspect ratios, H/W), street B (N-S) with 63.85% buildings coverage and 1.39 AR. The results show that trees’ canopy occupation ratio (TCR, Vtc/Vstreet) influences the average temperature at pedestrian height. The average temperature increases in the morning, whereas it decreases at noon and in the afternoon. It seems that TCR needs an offset to work on wider street, such as in street A, where the average temperature drops when TCR is greater than 0.15 (noon case, 12:00) and greater than 0.05 (afternoon case, 16:00). For every 0.1 increase of TCR (Vtc/Vstreet), the average temperature drops by 0.06°C(street A, 12:00), 0.118°C(street A, 16:00), 0.124°C(street B, 12:00), 0.1°C(street B), respectively. We can conclude that increasing the space occupation of trees’ canopy in the street can improve the pedestrian thermal environment, especially, the street with a small street aspect ratio. At the same time, we suggest that wider and taller trees shall be planted for street greening.
Optimal spatial resolution of remote-sensing imagery for monitoring cantaloupe greenhouses
Authors: Cui B , Huang W J , Ye H C , Chen Q X , Li Z C , Jiang H Y 
Published: April 2022

Abstract
Plastic greenhouses are vital agricultural facilities to protect cash crops from disease and insects, especially in the Hainan region of China, which has high temperature and high humidity. Remote-sensing technology is an efficient means to quickly determine the spatial distribution of plastic greenhouses on the regional scale. With the rapid development of remote-sensing technology, and especially the increasing types of high-spatial-resolution remote-sensing imagery, many studies have obtained good results by using remote-sensing technology to monitor plastic greenhouses. However, the best spatial resolution of images for monitoring plastic greenhouses has yet to be studied. To address this issue, we use cantaloupe greenhouses as the research object and GF-2 images with 1m spatial resolution as data source. We then use the re-sampling method to generate images from these data with spatial resolutions of 0.5, 2, 3, and 5 m. The details of the spatial distribution (texture features and shape features) and the spectral features of the plastic greenhouses were then extracted from images of varying spatial resolution, and a remote-sensing monitoring method for cantaloupe greenhouses was constructed based on the object-oriented random forest algorithm, which combines spectral, texture and shape features, and the monitoring results are compared. The results show that the use of 2 m spatial resolution provides the highest monitoring accuracy of cantaloupe greenhouses (overall accuracy = 94.85% and KIA = 0.92). This study thus provides a theoretical basis for remote-sensing monitoring of greenhouse cantaloupes that satisfies the current demands of production accuracy.
Remote-sensing monitoring of rubber plantations using object-oriented characteristics from the vigorous period
Authors: Cui B , Huang W J , Ye H C , Chen Q X , Li Z C , Jiang H Y 
Published: April 2022

Abstract
Natural rubber is one of the four major industrial raw materials in China, and the demand for it is increasing rapidly in China. However, due to geographic and climatic limitations, the rate of natural rubber production in China is significantly less than required to satisfy this demand. Therefore, to ensure the healthy development of China’s rubber industry, it is urgent to develop a method to rapidly and accurately monitor the planting and distribution of rubber forests in China. Existing studies have exploited the unique phenological characteristics of rubber plantations manifested by the spectral characteristics of the vigorous growth period and the deciduous period to identify rubber plantations through remote sensing. Unfortunately, the cloudy and rainy climate of rubber-growing regions makes it is difficult to obtain remote-sensing data in the optimal period. Therefore, the present study uses Planet images as the basic data and extracts typical spectral and textural features of rubber forests during the single vigorous growth period to construct a remote-sensing method to monitor rubber forests based on the object-oriented random forest algorithm, which uses all the spectral and texture features. The results show that the proposed random forest classification model achieves a high classification accuracy: the total classification accuracy reaches 89%, and the rubber producer’s accuracy and user’s accuracy both exceed 92%. This study can effectively solve the problem of lack image data with cloudless from deciduous period and provides a good theoretical basis for remote-sensing monitoring of China’s rubber forests, thereby facilitating the development of China’s rubber industry.
Analysis of Ecological Environment Changes and Driving Forces in Boao town of Hainan Province
Authors: Yang J Y , Wang Q J , Li Z C , Xie J J , Chang D K 
Published: April 2022

Abstract
Based on the three phases of remote sensing imageries in 2000, 2010 and 2020, taking Boao town as the study area, this paper analyzed the changes in the ecological environment and the driving forces of ecological environment. The results showed that: (1) The main land use type of Boao town was forest, followed by cultivated land. The main change of land use types in Boao town from 2000 to 2020 is from forest to other land types. (2) The average vegetation coverage of Boao town was 70.68% in 2000, 62.49% in 2010 and 71.65% in 2020, which indicated a trend of first decreasing and then increasing. (3) The changes of landscape pattern index of type level from 2000 to 2020 were as follows: the landscape shape index and landscape fragmentation of cultivated land decreased gradually, while the landscape shape index and landscape fragmentation of construction land and forest land increased. The trend of landscape pattern index of landscape level from 2000 to 2020 was as follows: landscape fragmentation continued to increase, landscape spread continued to decline, landscape separation continued to rise, and landscape diversity and evenness continued to increase. (4) In the driving force analysis of ecological environment changes in Boao Town, the results of principal component analysis showed that human factors were the leading factors of ecological environment changes in Boao Town, and economic factors such as GDP, output value of the third industry and per capita disposable income of farmers were the main driving forces of landscape pattern changes in Boao town.
Research on Disaster Acquisition Method Based on Point and Surface Combination of UAV Technology
Authors: Zhang Xuehua , Deng Duo 
Published: April 2022

Abstract
UAV (Unmanned Aerial Vehicle) remote sensing technology plays an increasingly important role in disaster acquisition. At present, after the disaster occurs, the orthophoto images of the UAV in the disaster area can be quickly obtained, but the bidimensionality of the orthophoto image limits the accuracy of disaster information extraction; it is required to improve the timeliness of the large-area 3D model established using the oblique photography technology in the disaster area. Combining the advantages of orthophoto and 3D models, this paper proposes a method for rapid acquisition of disasters based on point-surface combination. The Tibet Milin M6.9 earthquake is used as an example to analyze the advantages and disadvantages of this method in practical applications.