bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of On-line Monitoring and Fault Diagnosis Platform for Oil-immersed Shunt Reactor
Authors: Wang Yanhong , Su Mingran , Hu Yiru , Yang Wei 
Published: July 2024

Abstract
With the development of society and technology, the demand for power supply and reliability is increasing. In the power system, the reactor plays an important role as a key equipment. However, due to long-term operation, the reactor is affected by factors such as loss, aging and hidden faults, and its reliability gradually decreases. The operation state of the reactor is directly related to the operation effect of the power grid and the quality of the user’s electricity. Therefore, the application of reactor online monitoring and fault diagnosis in real life is very important. This paper considers the actual operation of the coastal power grid. Based on Qt software development platform, C + + language and MySQL database, an online monitoring and fault diagnosis platform for oil-immersed shunt reactors is developed. The application of the platform takes the online monitoring and fault diagnosis method of dissolved gas in oil as the core. The platform realizes the basic information management, online monitoring, fault diagnosis and suggestions for operation and maintenance of the reactor.
A Short-Term Power Load Forecasting Approach Based on EMD-SSA-LSTM
Authors: Su Zhiyin , Zeng Jinhui , Liu Jie , Wang Jing , Li Changxue 
Published: July 2024

Abstract
Accurate power load forecasting is the foundation of the power system. However, economic, weather, and geographic factors can affect short-term electric load forecasting and can make it difficult to enhance load forecasting accuracy. To solve the problem, a novel load forecasting method based on empirical mode decomposition (EMD) and long short-term memory (LSTM) neural networks has been proposed. Firstly, the EMD algorithm is utilized to decompose the original signal into elementary components and a series of intrinsic mode functions (IMF). Subsequently, all components are input into the LSTM neural networks for load prediction. Moreover, to further improve the prediction accuracy, the sparrow search algorithm (SSA) is introduced to optimize the parameters of LSTM for the prediction of basic components. Integrating SSA and EMD-LSTM, the EMD-SSA-LSTM model is further proposed for short-term power load forecasting. Finally, the proposed model is validated by several experiments, and the results show that the coefficient of determination (R2) under the proposed model can reach 0.98, the mean absolute error (MAE) is 0.015, and the root mean square error (RMSE) is 0.02. It is verified that the proposed model can improve the accuracy of load forecasting.
Distributed Photovoltaic Output Prediction Using Machine Learning and Numerical Model Combined Method
Authors: Wang Lei , Kang Wenjun , Feng Tao , Li Li 
Published: July 2024

Abstract
In recent years, distributed photostatic (PV) has developed rapidly. Accurate prediction of distributed PV output can improve the reliable, efficient and economical grid-connected operation of distributed PV, and enhance the reliability of power supply. In this study, we first collected the datasets of distributed PV output and centralized PV station output in Dongan County, Yongzhou City, Hunan Province. Then, we analyzed the relationship between distributed PV station output and centralized PV output under different weather conditions, and constructed the centralized PV output prediction curve by combining machine learning and numerical prediction model, and finally obtained the distributed PV output prediction of the whole county. The results show that the correlation coefficient changes between the output of distributed PV stations and that of centralized PV stations under different weather scenarios (sunny, cloudy, etc.), but they all show a good consistency (the correlation coefficient of more than 0.6 accounts for more than 95% of the PV stations), indicating that the output change of centralized PV stations in one county can be used to characterize the output change of distributed stations. The distributed PV output prediction model built by LSTM and numerical model combined method has achieved a prediction accuracy of 90.4% for a single station and 94.31% for the whole county within 24 hours, both of which are 4%˜5% higher than the requirements of national standards. The model can be further extended to forecast the distributed PV output of the whole province.
Dynamic Identification Method of Load Adjustable Capacity Based on Load Dynamic Characteristic Generation
Authors: Zhang Jie , Lin Ruiyu , Shen Nuoqing , Du Jingmei , Liao Siyang 
Published: July 2024

Abstract
With the continuous establishment of the energy internet, there are already regions carrying out demand response pilots by installing smart meters. In the case that complete communication has not yet been established, how to mine the potential value of collected smart meter data based on the existing pilot areas to obtain the real response potential of regional temperature control equipment at each moment is a common focus of attention in both academia and industry. In this paper, we propose a method for dynamically identifying load adjustable capacity based on generated load dynamic characteristics, which enhances the load feature library and dynamically identifies load adjustable capacity based on generated load dynamic characteristics. The method only installs a smart energy measuring device at the customer’s entrance line to obtain the customer’s total load power information and dynamically recognizes the adjustable capacity of the load based on the generation of load dynamic characteristics.
Distributed Photovoltaic Site Selection and Capacity Planning Considering Power Mutual Support in Multiple Power Distribution Areas
Authors: Gu Taiyu , Tian Ye , Zhu Yidong , Du Wei , Liu Zhaochen , Shang Lei 
Published: July 2024

Abstract
With the rapid development of local economy, the form of independent power supply from a single power supply in the power distribution area is difficult to meet the demand for high reliability. The interconnection and power mutual assistance planning of power distribution areas under large-scale distributed photovoltaic access have attracted widespread attention. Due to the randomness and volatility of distributed photovoltaics, its different access methods and capacities will affect the safe and stable operation and power mutual assistance capabilities of the distribution area. In order to study the site selection and capacity planning problem of distributed photovoltaics, this paper builds a site selection and capacity model based on the three-phase power flow equation with the maximum external power of the station area as the optimization goal and the safe operation of the distribution area as constraints. Furthermore, an optimization method for distributed photovoltaic site selection and capacity based on improved particle swarm algorithm is proposed. Finally, this paper verified the effectiveness of the optimization method through simulation analysis on the IEEE33 three-phase grid model. The simulation results show that this method of site selection and capacity determination can achieve maximum power transmission in the power distribution area while ensuring the safe and stable operation of the area.
A Three-Dimensional Substation Reconstruction Method Based on Scenery Point Clouds
Authors: Chen Xuan , Zheng Xin , Xu Gang , Shu Zhihai , Ji Yamin , Xu Changfu 
Published: July 2024

Abstract
3D modeling is the technical foundation for 3D visualization of substations, and accurate 3D panoramic modeling of substations is the key to achieving intelligent operation and maintenance of substations. However, the reuse rate of the model is low, resulting in low system modeling efficiency and bad model accuracy, which cannot fully leverage the advantages of 3D real scenery reconstruction technology. To address the aforementioned problems, this paper proposes a basic process and method for constructing a 3D model of a substation based on point cloud data, and provides an example of 3D reconstruction of the substation, which is then validated from three aspects. The experiment shows that the proposed process and method can achieve complete and accurate 3D reconstruction of substations, with a detail completeness of 98% and an outer diameter error of only 0.13 mm for transformer bushing leads.
Controller-in-the-Loop Validation for Virtual Impedance-based Fault Detection Method in Grid-Connected Inverters
Authors: Deng Caibo , Xu Baihe , Xiong Yongkang , Yuan Le , Xu Zaide , Song Guanhong , Xia Yonghong 
Published: July 2024

Abstract
The high penetration of distributed energy resources and grid-connected inverters creates operation and protection challenges in distribution networks which have led to a need for advanced fault protection methods. Therefore, a virtual impedance-based short-circuit fault detection algorithm is proposed in this paper for grid-connected inverters in distribution networks which determines a short-circuit fault through measurement of the spectral change in system impedance at each harmonic frequency. Moreover, a controller-in-the-loop platform is also developed in this paper for verifying the proposed detection algorithm without jeopardizing the operation of distribution networks. By using the developed platform, real-time simulation on both grid-connected inverter and distribution network is carried out. The results have proven the effectiveness of the proposed method and theoretical analysis.
Temporal Scenarios-based Assessment of Maximum Hosting Capacity of Renewable Generation in Distribution Systems Considering ANM Techniques
Authors: Wu Zhize , Zhou Zhiwen , Zhang Gang , Zhang Xiaoyu , Dong Wenjie , Liu Yanle 
Published: July 2024

Abstract
Incorporating large-scale renewable distributed generation (RDG) into the power grid introduces significant challenges associated with the indeterminacy of the power system, significantly impacting the comprehensive integration and efficient utilization of the distribution network. This underscores the imperative to investigate scientific evaluation methods and mechanisms for enhancing the RDG absorptive capacity within the distribution network. In light of the stochastic and temporal characteristics inherent to RDG, a novel approach for assessing the RDG hosting capacity within distribution networks is proposed, taking temporal scenarios into account. This method optimizes the RDG output probability distribution model based on its temporal fluctuation change rule, and constructs RDG temporal scenario model by using scenario reduction technology. Subsequently, a temporal scenarios-based RDG hosting capacity assessment model is established, taking into account active network management (ANM) techniques. The effectiveness of the proposed methodology is substantiated through simulation results conducted on a modified IEEE 33-bus system.
The Uncertainty Evaluation of Determining the Trace H2S in SF6/N2 Insulating Gas Mixture by Cavity Ring-Down Spectroscopy
Authors: Gao Ruifeng , Zhu Min , Zhang Chaohai 
Published: July 2024

Abstract
H2S is one of the most important characteristic components of sulfur hexafluoride gas insulated electrical equipment. The detection of trace H2S is of great significance for early fault diagnosis of such equipment. Based on the 1578 nm wavelength distributed feedback diode laser (DFB-DL), cavity ring-down spectroscopy (CRDS) can effectively measure the concentration of trace H2S in SF6/N2 gas mixtures. Starting from the principle of methodology, this article establishes a mathematical model for evaluating the measurement uncertainty of CRDS and analyzes its sources of uncertainty: gas constant R1
, temperature T, Avogadro constant NA
, gas pressure p in the optical cavity, volume V of the optical cavity, speed c of light, the absorption cross-section parameter σ(ν)
of H2S molecules in the laser frequency ν, the ring-down time τ with absorption sample H2S and the ring-down time τ0
without absorption sample. After evaluating the uncertainty components, it was found that the main component was σ(ν)
and the secondary components were τ and τ0. The H2S content in the 80% SF6-20% N2 mixed gas measured is (7.64 ± 0.12) × 10−6 mol/mol.
An Optimized Operation Method for Integrated Energy System Based on Variable Operating Condition Characteristics
Authors: Pan Yi , Leng Xina , Wang Tao 
Published: July 2024

Abstract
An integrated energy system (IES), functioning as a multi-input, multi-output apparatus, incorporates diverse power conversion and storage device. Typically, the energy hub visually demonstrates its multi-energy coupling. However, traditional energy hub models tend to ignore the variable operating condition characteristics of conversion device, that is, the actual operation efficiency fluctuates with the load rate. Consequently, it results in constant coupling coefficients in the energy hub coupling matrix, causing discrepancies between the IES operating state and actual conditions, affecting the operation scheme’s economy. Therefore, this study focuses on IES optimized operation method, which is based on variable operating condition characteristics, incorporating conversion device operating efficiency and load rate relationships into EH models. Development of a dynamic energy hub (DEH) model that takes into account the variable operating condition characteristics of the conversion device, operational efficiency of the device is corrected in real time, and an optimal scheduling model is built with the objective of optimal IES economy, designing different scenarios for simulation and verification with an IES as an example. The study demonstrates that incorporating device operating condition dynamics into scheduling scheme effectively minimizes system expenses and enhances the system’s integrity and feasibility.
A Construction Method for Three-Dimensional Digital Model of Substation Equipments Integrating Grid Information Model
Authors: Chen Xuan , Zheng Xin , Xu Gang , Shu Zhihai , Ji Yamin , Xu Changfu 
Published: July 2024

Abstract
Building a three-dimensional digital model of a substation is a key technology for achieving intelligent operation and maintenance of substations. However, the widely used GIM technology currently has obvious shortcomings in spatial attribute correlation, and cannot meet the application requirements. This paper proposes a three-dimensional digital model modeling method for substation equipments, which improves the general standardization transplantation and spatial attribute association ability based on GIM. This method integrates the GIM data standard, establishes a mapping relationship between GIM model data and JSON instances, fully preserves the attribute information of the model, and can easily obtain a three-dimensional slice model of substation equipments. The feasibility of the proposed method was verified through experiments.
Intelligent Fault Diagnosis Method for Transformer Driven by Multiple Vibration Data
Authors: Luo Xinyue , Chen Fei , Lu Yuhong , Zhao Zhigao , Peng Yumin , Pang Jingwen 
Published: July 2024

Abstract
Early monitoring and early warning of transformer failure are crucial to ensure the safe and dependable functioning of the power system, as transformers are essential equipment within the system. On multivariate vibration data, this work presents a sophisticated approach for diagnosing faults in transformers which integrates entropy model and machine learning. Firstly, a new coarse-grained method is introduced into the improved multiscale fuzzy entropy to improve the disadvantage of the traditional entropy model with large entropy fluctuation under high-scale factors. Furthermore, the features of transformer multivariate vibration signals are extracted by the entropy model. Finally, the extreme learning machine is utilized to achieve efficient detection of transformer defects. This method provides a useful method reference for fault diagnosis of power equipment with multi-vibration signal, and has good reference value.
Safety Helmet Detection in Electrical Power Scenes based on Improved Lightweight YOLOv8
Authors: Li Zuhe , Huang Zhenwei , Chen Hongyang , Deng Lujuan , Wang Fengqin 
Published: July 2024

Abstract
To tackle the challenges of diverse targets, complex scenes, and partial occlusion in safety management during electrical field operations, the YOLO series algorithm, recognized for its exceptional accuracy and swift processing capabilities, has been applied to various scene detection tasks. To ascertain if workers have donned safety helmets and ensure the safety of electrical field operations, we propose a lightweight algorithm based on the improved YOLOv8 for constructing a digital safety helmet detection system. By incorporating the VoV-GSCSP module, we reduced model complexity, decreased computational load, and improved detection accuracy. Simultaneously, by combining the GSConv module, we enhanced the network’s feature extraction capability, enabling the network to adapt more rapidly and accurately to various complex electrical scenes, thereby strengthening the network’s robustness in safety helmet detection. Finally, we validated the effectiveness of the proposed model using the pre-existing dataset for safety helmet detection.
Research on Transformer Fault Diagnosis Based on Voiceprint Signal
Authors: Liu Guofeng , Gao Lingtao , Yu Lu , Yang Wei 
Published: July 2024

Abstract
As an important part of the power system, the operating status of the transformer will have a direct impact on the stability and reliability of the power system. In view of the problems of high diagnosis cost and low accuracy of diagnosis results in existing fault diagnosis technology, this paper takes advantage of the obvious difference between the voiceprint signal of the transformer under normal and fault operating conditions and applies it to transformer fault diagnosis, which can effectively reflect its internal working status and fault conditions, helping operation and maintenance personnel promptly discover equipment defects and locate fault causes. In order to accurately realize transformer fault diagnosis, this paper uses the improved hybrid frog leaping algorithm to optimize the fault diagnosis algorithm of support vector machine parameters for fault diagnosis, which further improves the accuracy of fault diagnosis and is of great significance for accurately identifying transformer fault states.
Simulation Study on Mechanical Vibration Characteristics of GIS Bus Misalignment
Authors: Wang Min , Zhao Shengnan , Zhang Qingjun , Jiao Hailong 
Published: July 2024

Abstract
Gas-insulated switchgear (GIS) has been widely used in power grid systems. The busbar is a crucial element within a GIS system, responsible for facilitating electrical connections between different GIS components. Any defects during manufacturing or improper installation procedures can lead to busbar misalignment, resulting in abnormal vibrations within the GIS. This study focuses on analyzing the mechanical vibration characteristics of GIS bus misalignment using a simulation model based on the finite element method for a 252kV GIS. When the busbar is misaligned and the conductor experiences an unbalanced force, it undergoes Coulomb forces at double the voltage frequency. Under normal or misaligned conditions, the conductor exhibits forced vibrations at the power frequency of 100Hz. However, when misalignment occurs, there is a coupling effect between electrodynamics and vibrations, causing the shell to display integer frequency vibration components.
Research on the Aging Judgment Method of Electrical Performance of C4F7N/CO2 Mixed Gases
Authors: Qi Geqing , Yuan Feng , Zheng Yue , Li Gang , Yang Songli , Zhang Miao 
Published: July 2024

Abstract
Perfluoroisobutyronitrile (C4F7N) gas exhibits favorable environmental characteristics and excellent electrical performance, which renders it a prominent subject of research in the development of new environmentally friendly insulation materials as a substitute for SF6 gas. In this study, the decomposition products of a gas mixture comprising 15% C4F7N and 85% CO2 were investigated within a 12kV ring main unit. A total of 140 arc tests were conducted on the gas mixture, allowing for the identification of the relationship between the generation of decomposition products and the number of arc tests. Through the determination of the proportional relationship between the different types and quantities of products, a novel method for assessing the extent of electrical performance aging in C4F7N/CO2 gas was developed, thus serving as a valuable reference for gas aging assessment. This study is of great significance for promoting the application of C4F7N/CO2 mixed gas in power systems.
Research on Evaluation Method of Water-blocking Powder Distribution Uniformity of Buffer Layer in HV Cable Based on Image Processing
Authors: Wu Zhaoguo , Li Yong , Wang Qian , Huang Huixian , Cai Zixiong , You Yang 
Published: July 2024

Abstract
High voltage (HV) cables are widely used in urban power transmission networks. The key component in HV cable, the buffer layer, has attracted lots of attention and more and more researchers are devoting to its material property evaluation and quality test techniques. A longitude water-blocking property evaluation method of buffer layer based on image processing is proposed in this paper. The microscopic images of the buffer layer are enhanced and morphologically processed. The area fraction method, entropy method, and fractal dimension method are used to evaluate the distribution characteristics of buffer layer water-blocking powder in binary images. This article innovatively proposes a more economical and convenient simulation method that can efficiently and accurately evaluate the waterproof performance of the buffer layer of high-voltage cables. With the proper use of the buffer layer, it can effectively protect the cables and improve the stability of power system operation.
Shared Energy Storage Scheme for Photovoltaic Energy Storage Power Supply System
Authors: Li Qiying , Xie Yumeng , Yang Lei , Yang Kun , Xu Dongsheng , Qiao Dongwei , Wang Shuyuan 
Published: July 2024

Abstract
While PV-battery hybrid grid integration has been recognized as an effective approach to enhance transient stability, the high operational and maintenance costs of energy storage systems constrain their reliability across various operating scenarios. To overcome this challenge, this paper takes the application of PV-battery subsystems in a distribution grid supply scenario as an example and conducts an in-depth analysis of the transient stability of PV systems during fault conditions. In cases where system demands are not adequately met, this research proposes a solution based on shared energy storage to enhance system supportability while maintaining economic feasibility. By appropriately allocating and sharing energy storage capacity, the system can better respond to sudden load fluctuations and fault conditions, ensuring a stable power supply. Simulation results demonstrate that the proposed shared energy storage solution significantly enhances the transient stability of PV systems during fault conditions, effectively mitigating the destabilizing effects of PV grid integration. Furthermore, from an economic perspective, this solution can reduce energy storage operation and maintenance costs, enhancing overall system sustainability. In conclusion, this study presents a feasible and effective support solution for the application of PV systems in distribution grids, providing valuable insights to advance the reliability and sustainability of clean energy sources.