bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stability analysis of small signals under different ratios of grid-following to grid-forming converters sent via flexible DC delivery
Authors: Li Feng , Li Zhijie , Yang Shuangmian , Wang Jinyu , Zhang Yibo 
Published: December 2024

Abstract
Under the “dual carbon” strategy, frequent wide-band grid oscillations caused by grid-following converters as the interface for renewable energy integration have become common. Existing studies suggest that the addition of grid-forming converters can address this issue, but few explain the reasons from a stability mechanism perspective. Additionally, most studies overlook the interaction between converters and sending-end converter stations. This paper first conducts sequential impedance modeling. Then, based on impedance network analysis, it examines the impact of sending-end converter stations on stability assessment. Finally, by adjusting the grid-following/grid-forming ratio, Nyquist curves under different ratios are generated to analyze the mechanism by which grid-forming converters enhance grid-following small-signal stability. Corresponding simulation models are also developed for validation.
Parameter estimation of photovoltaic power generation system and digital twin model construction
Authors: Li Han , Jiang Zhihong , Wu Han , Yang Hao , Gao Jiaqi , Zhu Yangyang 
Published: December 2024

Abstract
Photovoltaic power generation system is an essential part of modern power system, but it has intermittency and limitations and will also significantly reduce the stability of power system operation. For this reason, this paper proposes a new method for estimating the parameters of a photovoltaic power generation system. It constructs a digital twin model of photovoltaic panels for accurate real-time system monitoring. In order to construct a more realistic twin model, an improved iterative method is proposed to analyze and calculate the unknown parameters of the relationship between the internal characteristics of the PV panels. A comparative analysis with the adaptive war strategy is carried out, which verifies the effectiveness of the proposed method, reduces the operating errors of the real object and the twin, makes an essential contribution to the application of the digital twin technology in the photovoltaic power generation system, and lays a smart PV power system foundation.
A load sequence scenario generation method based on a dual-layer Markov chain Monte Carlo model
Authors: Zhang Xuhan , Wu Xiong , Zhou Junji , Liu Dong , Guo Guodong , Xue Yawei 
Published: December 2024

Abstract
Generating a large number of representative load sequence scenarios is crucial for evaluating the supply-demand balance, economic efficiency, and reliability of power systems, which have a high penetration rate of new energy. Current scenario generation methods have certain limitations, such as the need for extensive historical data and the inability to reflect the relatively fixed patterns of load sequences over the medium to long term. To address these issues, this paper proposes a load sequence scenario generation method according to a dual-layer Markov chain Monte Carlo (MCMC) model. The outer MCMC model captures the probabilistic characteristics of day-type transitions within a month, while the inner MCMC model models the state transition probabilities of the load sequences. Through optimization techniques, the daily load sequences are generated. Case study analysis shows that the statistical characteristics of the scenarios generated by this method are highly similar to the original scenarios and better than the scenarios generated without considering the outer MCMC, providing diverse scenario support for the evaluation of power systems connected to the high proportion of new energy.
Non-invasive load identification method in smart grid based on graph convolutional neural network
Authors: Wei Jie , He Xing 
Published: December 2024

Abstract
Traditional charge discerners have problems such as low discerner rates and insufficient info utilization. Therefore, this paper proposes a non-aggressive charge discerner means in smart grids based on chart convolutional neural networks. This paper divides user charges into different groups based on their similarity in electricity prolapse behavior, constructs charge space-time chart info, and uses a self-adaption space-time synchronization chart convolutional neural network to achieve non-aggressive charge discerner in smart grids. Chart convolutional neural networks can practically procedure chart structured info, transforming complex relationships between charges into feature info transmission between network points. Combined with chart convolutional neural networks for feature extraction and classification, non-aggressive charge accurate discerner of various electrical equipment in the electrified wire netting is achieved. According to the testing verification, it is known that the proposed means improve the real-time and accuracy of the charge discerner, providing strong support for achieving fine management and efficient scheduling of smart grids.
Multi-charging Station access distribution network grid planning and optimization method based on MRFO algorithm
Authors: Zhang Bin , Guo Qian , Zhang Shihao , Ma Junxian 
Published: December 2024

Abstract
The traditional multi-charging station integration into the distribution network planning and optimization model is a regional optimization process with low overall efficiency, increasing the total optimization cost. Therefore, a multi-charging station access distribution network based on the MRFO (Manta Ray Foraging Optimization) algorithm is proposed. The MRFO calculation method was adopted to improve overall efficiency, and the design of the MRFO calculation multi-charging station access distribution network planning optimization model was completed. Optimization processing and multi-objective validation are used to achieve grid optimization. The test results show that the MRFO calculation and distribution network planning optimization method designed in this study ultimately achieve a relatively low total optimization cost, better optimization effect, and practical application value.
A multi-stage scheduling optimization method for distribution networks based on extreme learning algorithms
Authors: Sun Jie , Zhu Wei , Jiang Yi 
Published: December 2024

Abstract
As the prevalence of distributed power sources grows, the distinctions between transmission and distribution networks will blur further. In addition to conventional and renewable energy sources, electric vehicles and energy storage are also regarded as dynamic resources for system operation adjustments. This article establishes response models based on the response strategies, constraints, and costs of controllable loads and energy storage resources, and proposes a multi-stage active distribution network optimization and scheduling optimization method for “source load storage” collaborative interaction. Secondly, a novel optimization scheduling method for distribution networks is formulated, aiming to enhance both reliability and cost-effectiveness while adhering to constraints such as network balance, distributed power generation, and energy storage charging and discharging. Finally, the feasibility and correctness of the optimized scheduling model and algorithm were verified using the IEEE-24 node system as an example.
Closed-loop identification technology for the whole life cycle of the fixed defective state of relay protection equipment
Authors: Cao Defa , Qiu Shufeng , Liu Yuwen , Wu Qilin , Liu Xinmin 
Published: December 2024

Abstract
Due to the multiplicity of fixed-value defect states of relay protection equipment, it is difficult to guarantee the effect of identification directly through a single indicator. Therefore, this paper proposes research on closed-loop identification technology for the full life cycle of fixed-value defect states of relay protection equipment. The generalized transformation ratio of the relay protection circuit is analyzed based on the CT basic transformation ratio, cable voltage drop correction coefficient, filter attenuation correction coefficient, and ADC quantization error correction coefficient. The relationship between the transformation ratio and the fixed value state of the relay protection equipment enables the identification of defective states. In the test results, the design technology can effectively identify fixed-value defects at different stages and to varying degrees.
A zoning-based distributed control method for medium-low voltage AC/DC distribution networks
Authors: Wang Yang , Xia Kunpeng , He Molin , Wen Xiankui , Chen Xuehan 
Published: December 2024

Abstract
With the increasing percentage of renewable energy sources and new types of power equipment, the medium-low voltage AC/DC distribution network structure improves the source-load carrying capacity and is considered a prevailing trend. Traditional centralized voltage optimization and control methods face challenges due to computational inefficiencies arising from the numerous control devices involved. In this paper, a distributed optimal control strategy based on distribution network zoning is proposed. Firstly, a network decoupling and multi-agent deployment method based on zoning is introduced. Secondly, a distributed optimal control model for medium-low voltage AC/DC distribution networks is developed to minimize network losses within the region. Subsequently, the optimization model is transformed into a Markov process, and the multi-agent proximal policy optimization (MAPPO) algorithm is used to solve this Markov process, facilitating the distributed control of medium-low voltage AC/DC distribution network based on zoning. Simulation examples are analyzed to prove the effectiveness of the proposed method.
Dynamic construction strategy of the virtual power plant based on day-ahead-intraday joint scheduling
Authors: Wang Su , Chen Peng , Fu Chen , Ran Lü , Lan Li 
Published: December 2024

Abstract
As a new technological form, the virtual power plant can aggregate the distributed resources of each plant to explore the potential of distributed energy scheduling deeply. How to coordinate the scheduling of each power-generating body to improve the overall profitability and low-carbon operation has become an important issue that needs to be solved urgently. Therefore, in this paper, considering the non-renewability of traditional energy sources, the uncertainty of renewable energy output power, and the limitations of energy storage equipment within the virtual power plant, we design a day-ahead-intraday operation strategy. This strategy is facilitated by demand response guidance and the rapid response of electric vehicles to participate in the intraday optimal scheduling of the virtual power plant and to balance the internal output error. It also uses the improved multi-objective particle swarm algorithm to obtain the virtual power plant for each part of the output power. The simulation results show that the EV aggregator combined with energy storage equipment reduces the impact of renewable energy output uncertainty on the virtual power plant and reduces the large-scale investment in energy storage equipment in the virtual power plant in a short period. At the same time, it brings economic benefits to the EV aggregator, making the EV aggregator’s participation in the scheduling of the virtual power plant more valuable for practical applications.
The relationship between drill pipe torque and mining induced stress under constant rotation and drilling speed
Authors: Tan Jian , Geng Yanfeng , Wang Yakun 
Published: December 2024

Abstract
Mining-induced stress strongly influences coal mining safety. The magnitude of coal seamstress is one of the factors for assessing rockburst risk. This study aims to explore the relationship between drill pipe torque and mining-induced stress under the conditions of different constant rotation rates and drilling speed combinations. Through theoretical analysis and numerical simulations, the variation of mining-induced stress and drill pipe torque was systematically recorded in the drilling process, assuming that it maintains a consistent drilling speed and rotation rate. The results indicate that there is a positive correlation between the mining-induced stress and the torque. Thus, the measurement of the stress can be achieved by monitoring the torque.
Optimal design of communication scheme for auxiliary control terminal of test instrument without network coverage
Authors: Wan Yilun , Wu Xinye , Tang Lei , Song Renjie , Wu Shuyun 
Published: December 2024

Abstract
To meet the diverse application needs of users, an optimization method for the communication scheme of auxiliary control terminals for testing instruments without network coverage has been designed. Signal transmission characteristics are extracted and the correlation matching degree of monitoring data is analyzed; a reliability distribution objective function is constructed and feature constraint points of monitoring data are extracted from the auxiliary control terminal of the testing instrument; dimensionality is reduced and a localization optimization fitness function under the condition of no network coverage is constructed; an error model based on delay accuracy is established and combined with positioning optimization results to obtain accurate interference position information, and the optimization effect of the communication scheme for the auxiliary control terminal of the testing instrument is improved. The experimental results show that this method has a small optimization error for channel interference location, high compatibility for communication scheme optimization data compression, and a good optimization effect.
Research on radioactive source localization algorithm based on array detector
Authors: Li Yan , Gao Weichen , Yang Bin , Li Gang , Yang Dazhan , Zhou Dongliang , Zhang Pengda 
Published: December 2024

Abstract
In this paper, a ring array radiation detector based on a point kernel integral algorithm is designed for the current situation of potential radiation damage to the public after patients undergo internal radiotherapy. The detector calculates the distribution and activity of radionuclides in the body by measuring the radioactive dose of the patient’s body surface, so as to improve the scientific nature of the diagnosis and treatment effect evaluation and radiation protection strategy. Experimental verification shows that the method has high accuracy in the calculation of radioactive source activity, and can provide effective data support while simplifying the calculation amount. The work of this paper provides new technical means and a theoretical basis for precise treatment and radiation protection of nuclear medicine.
Multi-objective path planning for mobile robots in nuclear radiation environment
Authors: Yu Yuhui , Yao Huilu , Mo Wenfeng , Qiu Yihai , Zhou Lin 
Published: December 2024

Abstract
To minimize the harm of nuclear radiation in hazardous environments, employing mobile robots as substitutes for human workers is of significant practical importance. To plan an optimal path in a nuclear radiation environment, we developed a multi-objective path planning (MOPP) model to facilitate the integration of algorithmic improvements and experimental validation. An improved ant colony optimization algorithm (IACO-A*-M) is proposed, considering path length, cumulative radiation dose, and energy consumption during the movement process to address the path planning challenge for mobile robots in such environments. The approach begins with optimizing the initial pheromone distribution and heuristic function. Next, an improved A* algorithm is employed to estimate the MOPP path, enhancing pheromone levels along this path. Additionally, an elite ant strategy is applied in each iteration to accelerate convergence and improve search quality. Finally, a secondary path planning step is performed in each iteration to further shorten the path and minimize the number of turns. Simulation experiments conducted in MATLAB R2023a demonstrate that the IACO-A*-M algorithm significantly reduces the accumulated radiation dose, energy consumption, and multi-objective function values compared to ACO, IACO and IACO-A*. These results highlight the effectiveness of the IACO-A*-M algorithm in planning more efficient and rational paths in radiation environments.
Characterization and simulation study of rGO@epoxy strain sensor for implantation into CFRP composites
Authors: Wang Shengjie , Yang Heng 
Published: December 2024

Abstract
Epoxy-based strain sensors are considered an ideal choice for online monitoring due to their excellent compatibility with carbon fiber-reinforced polymer (CFRP) composites. However, the existing preparation processes for sensors are usually quite complex and have high requirements for experimental equipment and process conditions. This study proposes a novel and simple preparation method for sensors, which integrates laser reduction of graphene oxide and pre-cured epoxy film technology. By precisely controlling the pre-curing time of the epoxy film, the integrity of the sensing layer is effectively protected during the sensor curing process. In uniaxial tensile tests, the sensitivity of the sensor increases with the increase in scanning speed. The sensors fabricated at a laser scanning speed of 120 mm/s have a sensitivity of 11.6, which is approximately 2.01 times higher than the sensitivity at a 24 mm/s laser scanning speed. The sensor’s cyclic response was further tested to characterize its online monitoring capability. Finally, simulation analysis was conducted to investigate the impact of implanting sensors of different thicknesses on the stress concentration of CFRP laminates. This study provides a reference basis for the design of epoxy-based strain sensors with less invasion and better compatibility with CFRP in the future.
Segmented remote plasma source output current tracking based on LQR improved single-neuron PID
Authors: Xian Jianwei , Ma Caiyu , Xue Jiaxiang 
Published: December 2024

Abstract
The current plasma concentration control algorithm of plasma power supply has some problems, such as being easily disturbed by the outside world, low tracking control speed, and difficulty in adjusting the appropriate control parameters. In this paper, an improved single-neuron fuzzy PID control algorithm based on LQR is proposed. The learning process of neurons is optimized by introducing a quadratic performance index, and the scale coefficient K is adjusted by fuzzy control. The feasibility of the control algorithm and its advantages compared with other PID controllers are verified by MATLAB discrete simulation and real machine experiments. The results show that the PID control algorithm designed in this paper can reduce the steady-state deviation and overshoot, shorten the adjustment time, and eliminate the adjustment shock, thus significantly improving the processing effect.
Research on weak appearance defect detection method for engine piston rings based on 3D vision
Authors: Chen Yongbin , Zhang Zhijun , Yu Long , Huang Guojian , Huang Jiasi 
Published: December 2024

Abstract
As a critical engine component, the appearance quality of piston rings directly affects engine performance and lifespan. Subtle defects like minor chipping, cracks, and scratches can significantly impact engine safety and durability. Traditional 2D image processing and AI-based methods struggle to detect these defects, being prone to interference and resulting in high false positive and negative rates. This paper introduces a 3D vision-based detection method, which captures detailed surface morphology to improve defect detection accuracy. Experimental results demonstrate that this approach effectively reduces false positives and negatives, significantly enhancing detection accuracy.
Identification of Position-independent Geometric Errors for two rotary axis of five-axis machine tool using a double ball-bar
Authors: Peng Weichao , Huang Jianfeng , Li Xun , Huang Ruiwei 
Published: December 2024

Abstract
Inaccuracy geometric precision of the rotary axis causes serious dimensional errors in a workpiece in precision machining. A quick identification method is proposed for the identification of the rotary axes position-independent geometric errors (PIGEs) using a double ball bar (DBB). For the B-axis test, translational errors and rotational errors are measured by singular-axis and multi-axis controlled movements respectively. Path generation for the C-axis test with three simultaneous control movements is implemented to identify the PIGEs. The quick identification method is simulated and performed on a multi-axis configuration. The result shows that the method is suitable for PIGEs identification on multi-axis configuration.
Modeling and forecasting air conditioning loads using BP neural network
Authors: Liu Bin , Zhu Zizhe , Tan Zhukui , Hu Caixia 
Published: December 2024

Abstract
Power load forecasting plays an extremely important role in the safety and stability of the power system. Due to the immediate use of the power system, grid workers need to develop power generation plans based on load forecasting report data to maintain a balance between power generation output and load. Accurate prediction reports can assist power companies in planning and managing the production and distribution of electricity, further improving energy utilization efficiency and reducing costs, as well as improving the reliability and stability of the power system. Regarding the modeling and prediction of wide-area air conditioning loads, this article uses the method of regional decomposition to establish air conditioning prediction models for different cities. Finally, the prediction results of the models for different cities are added together to obtain the air conditioning load prediction results. The BP prediction model developed in this paper demonstrates relatively accurate results, offering a method for forecasting air conditioning load.
Research on the construction of electric power robot standards
Authors: Wang Kai , Kong Xuecheng , Yu Yang , Jiao Bo , Miao Pengchao , Li Dongjie 
Published: December 2024

Abstract
This article aims to explore the current development status and trends of power robot technology, as well as the current status and needs of standard construction for power robots. Through in-depth analysis of relevant technologies and standards, combined with application needs, references and suggestions are provided for the further construction and improvement of standards for electric power robot technology. The future layout direction of standard construction is analyzed, providing theoretical support and practical guidance for promoting the healthy development of electric power robot technology.
Research on the reliability evaluation model and application of power systems based on artificial intelligence algorithms
Authors: Jia Keyin , Li Lei , Han Dongxu , Zheng Shiyang , Ji Shi 
Published: December 2024

Abstract
A reliability evaluation model of a deep belief network based on the PSO algorithm is proposed to improve the accuracy of automatic evaluation of reliable power systems. Through a deep belief network, the learning depth of the model is improved. After introducing the PSO algorithm to optimize the parameters, the reliability evaluation quality of the model on the power system is further improved. Experiments show that compared with the traditional DBN algorithm, the proposed PSO-DBN algorithm has a faster training convergence speed and better sample reconstruction effect.