bluuebunny/crossref_metadata_2025 huggingface dataset:
Fast CFD methodology for accurate prediction of wind turbine airfoil polars by means of Generalized k-omega turbulence model
Authors: Mauro Stefano , Caridi Domenico , Lanzafame Rosario , Messina Michele , Brusca Sebastian
Published: December 2024

Design and optimization of wind turbine blades still relies on 1D numerical codes such as the ones based on the Blade Element Momentum Theory (BEM). These simplified models, require accurate aerodynamics polars as input, for a wide range of Reynolds number conditions, in order to provide reliable predictions. Wind tunnel experimental measurements of lift and drag coefficients is very time consuming and expensive therefore the data available in the scientific literature are generally quite limited. Moreover, the data are often related to few limited operative conditions. On the other hand, the increase of computational power as well as the recent progresses of Computational Fluid Dynamics (CFD) in terms of accuracy and calculation speed have led to an ever wider use of CFD tools for the calculation of the airfoil polars. In this context, the authors developed a fast and accurate CFD methodology based on the use of the recently developed Generalized k-omega (GEKO) RANS turbulence model available in the Ansys Fluent solver. The new procedure to generate a suitable computational domain, the spatial discretization methods as well as Fluent solver settings and GEKO model calibration and optimization are described in detail. The methodology was validated on the basis of experimental data of the widely known S809 wind turbine airfoil, at different Reynolds numbers. Despite the use of a steady RANS turbulence modelling, the proposed CFD methodology demonstrated to accurately predict lift and drag coefficients in fully stalled conditions as well. Moreover, thanks to the automation of the entire CFD process, the methodology allowed to obtain full polars in very short times on a small HPC workstation. Thus, the results of the present work may represent an important step ahead to develop accurate databases of aerodynamic polars by means of a fast and easy but at the same time accurate approach.
The Effect of Entropy Waves Features on the Indirect Noise Generation within an Aeronautical High-Pressure Turbine stage
Authors: Pinelli L. , Giannini G. , Marconcini M. , Pacciani R. , Notaristefano A. , Gaetani P.
Published: December 2024

First stages of aeronautical high-pressure turbine are subjected to inlet distortions generated by the combustor systems. Such disturbances, characterized by velocity and temperature fluctuations, are referred to as vorticity and entropy waves and they are convected downstream by the main flow. Such disturbances interact with the turbine stages affecting the stage efficiency, heat exchange and generating noise emissions. This work summarizes a comprehensive numerical campaign on the effect of different Entropy Waves features (i.e. radial position, clocking and swirl direction) on the indirect noise generation of engine-representative high pressure turbine stage tested at the Politecnico di Milano LFM (Laboratory of Fluid Dynamics of Turbomachinery). The numerical campaign is based on URANS CFD simulations with time-varying inlet distortions, carried out with the University of Florence in-house TRAF code. A Discrete Fourier Transform (DFT) based post-processing tool has been used to extract the low frequency entropy wave content to assess indirect noise emissions. The extensive validation of the TRAF code in previous campaigns grants the fidelity of the results in terms of aerodynamic and acoustic quantities. The numerical results provide a deeper insight of the effect of entropy waves features on indirect noise generation. The outcome of the results may be used by the designer to better understand the combustor-turbine interaction, also considering the possible introduction of innovative fuels such as hydrogen or Sustainable Aviation Fuel (SAF).
Experimental characterization of a variable-pitch Wells turbine
Authors: Licheri Fabio , Ghisu Tiziano , Cambuli Francesco , Puddu Pierpaolo
Published: December 2024

Wells turbines coupled with systems based on the oscillating water column (OWC) principle represent a convenient solution for sea-wave energy conversion. The possibility to operate under the bi-directional airflow that is established within OWC systems is obtained with a symmetrical blade profile staggered at 90 degrees with respect to the turbine’s axis of rotation, resulting in a highly reliable machine. On the other hand, the limited operating range represents a relevant drawback which can be overcome introducing a variable-pitch control of the rotating blades. This control solution has been only marginally investigated with experiments, due to the complexity of devising a mechanism capable to change the stagger angle of the rotating blades.

In this work, a Wells turbine prototype with variable-pitch blades has been built and preliminary investigations are presented, with the aim of characterizing its aerodynamic behavior for different blade stagger angles. The performance of the turbine have been evaluated under regular-wave conditions, for different blade-pitch angles, in order to obtain the information required to define the pitch laws for the maximization of rotor performance.
An Automated Geometric Analysis and Characterization of an Oil-Lubricated Twin-Screw Compressor for Predictive Modeling
Authors: Giardiello Giovanni , Gimelli Alfredo , Riccio Davide , Zeppa Giuseppe
Published: December 2024

Compressor manufacturing companies are showing a growing interest in the development of validated compressor simulation models capable of predicting the behaviour of the machine if subjected to appropriate modifications. A validated predictive model of the entire compressor is useful for analysing the influence of the variation of some fundamental parameters, such as the variation of volumes or new inlet and outlet sections, on the behaviour of the machine. To create these models, geometric parameters such as the variation in volumes, the variation in inlet and outlet sections and the presence of leakage or blowhole phenomena must be provided as input. The precision of these geometric parameters is of fundamental importance for the correct operation of the model and subsequent design analyses. In this work, the authors analysed the complete geometry of an oil-lubricated twin-screw compressor. In particular, an automated calculation methodology was developed using CAD software capable of defining the characteristic curves of the volumes and areas of the compressor: starting from the geometries of the rotors and the stator case, the fluid volumes delimited at the bottom by the rotors and at the top by the case were defined through a Boolean subtraction operation. Subsequently, the volumes that complete the compression phase together were isolated and, through an automatic kinematic analysis with steps of 5° of rotation, starting from the zero reference (null volumes), the characteristic curves of the volumes and inlet and outlet areas were defined through the use of special virtual sensors. The passage sections of the leakage and blowhole leaks were defined in the same way. Particular attention was paid to the analysis of leakage and blowhole phenomena, the presence of which determines a reverse flow of part of the mass flow rate of air during the compression phase, resulting in a decrease in volumetric efficiency. Knowledge of this data is of fundamental importance for the subsequent creation of the 1D/0D model.
Design improvement of USB charger socket ripple defects
Authors: Zhao Ying , Song Shijie , Hu Jianyu , Yang Rentong , Zhang Hongji
Published: December 2024

USB smart sockets have been widely used in modern society. This product directly converts the AC level of the power grid into a DC low-voltage level, making the charging of electronic and electrical products more convenient and safer. It is worth exploring that during the quality inspection of USB smart sockets, testers often find that the DC output voltage of USB smart sockets has large ripples and poor output voltage stability. In response to this finding, this article aims to analyze the causes and types of ripples in USB smart sockets and design a device that can filter out high-frequency ripples and low-frequency ripples at the same time.
A design of series-parallel switching type electrical cabinet for energy storage system
Authors: Hao Wang , Li Haozhan , Hua Sicong , Xu Jianhong
Published: December 2024

Based on the application requirements of multi-load scenarios in the field of specific energy storage, we propose a design of a series-parallel switching type electrical cabinet through the analysis of the energy storage system. The scheme operates the on-off combination of the bus bar switch inside the electrical cabinet through the electronic control system to adjust the series and parallel configurations of the battery racks inside the energy storage container, thus changing the output voltage, current, and power to meet the electricity needs under different load conditions.
Optical fiber liquefied petroleum gas sensor based on the fluorescence quenching of CdSe quantum dot
Authors: Yu Wenwei , Yang Qiaoyun , Chen Yujing , Li Hongjie , Huang Yantang
Published: December 2024

A flameproof optical fiber liquefied petroleum gas sensor was designed and fabricated. Bare-core CdSe and CdSe@ZnSeS@ZnS@CdZnS were modified with pentafluorobenzoic acid (FBA) and encapsulated in PMMA as fluorescent probes for liquefied petroleum gas, enhancing the quantum dots’ response to liquefied petroleum gas detection. Both materials exhibited significant fluorescence quenching in the presence of liquefied petroleum gas, with the Stern-Volmer plot showing a good linear relationship with the gas. A detection optical path was designed, and the relationship between fluorescence intensity and gas concentration was fitted with an expression with a sensitivity of 0.081 (I0/I100-1), enabling real-time detection of liquefied petroleum gas.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Quantitative evaluation of power grid voltage stability level of power system with high proportional power electronics device
Authors: Ding Haoyin , Zhang Yijing , Liu Jiachen , Sun Jinwen , Hao Yi
Published: December 2024

There is an increasing need for long-distance electricity transportation using high voltage direct current (HVDC). By incorporating numerous renewable energy grid integration technologies at the receiving end, an advanced power grid network will be built. The occurrence of short-circuit faults at the receiving end has the potential to trigger commutation failure and voltage dip. Consequently, the exploration of transient voltage stability assessment is pivotal in enhancing the secure and reliable functioning of the receiving electricity grid. To address this challenge, this paper initially delves into the quantitative assessment of the voltage stability status when subjected to a power system with high proportional power electronics device. It also clarifies the dynamic interplay between reactive power and voltage. Subsequently, a suitable quantitative assessment metric for transient voltage stability is formulated. Finally, the transient voltage stability evaluation of a power system with high proportional power electronics device is realized.
An integrated hybrid dual-frequency wireless charging system with anti-offset performance via phase-shift control for consumer electronics
Authors: Xie Ronghuan , Liu Qiliang , Lin Gaoyu , Tang Ruihua , Zhang Yiming
Published: December 2024

Wireless charging systems for consumer electronics suffer from the offset issue, which leads to drastic changes in the mutual inductance and thus produces unstable outputs. To solve the issue, this paper proposes an integrated hybrid dual-frequency wireless charging system with an anti-offset performance. The proposed topology can be equivalent to the CLC-S and S-S topologies at fundamental and third harmonic frequencies, respectively. Combining the opposite trends of the output voltages in the CLC-S and S-S topologies, the anti-offset performance can be realized. To further enhance the anti-offset performance, the phase-shift control is adopted to adjust the ratio of fundamental and third harmonic, thus extending the anti-offset range. The mathematical and experimental models are established. The experimental results are well aligned with the calculated ones, confirming that the proposed system can maintain stable output and high efficiency under different offsets.
Research on dynamic aggregation of virtual power plant based on evolutionary game
Authors: Li Chunyu , Zhao Wenfei , Wang Jialei , Lu Mengxian , Yang Xiaoran
Published: December 2024

With the rapid development of Chinese society, there is an increasing demand for electricity from users, which brings great challenges to the security of the power grid. As a new technical means, a virtual power [1] plant is an inevitable choice for the future development of power systems to participate in power grid regulation. The aggregation of virtual power plants participated by distributed energy sources is the key technology for virtual power plants to mobilize distributed energy sources to participate in power grid regulation. Based on this background, this paper proposes a model of dynamic aggregation of distributed energy in virtual power plants based on evolutionary game theory [2] to realize dynamic aggregation of virtual power plants. Finally, the effectiveness of the proposed model is demonstrated by a numerical example.
A fault detection method in asymmetric networks based on the zero-sequence current
Authors: Cai Jiamin , Liu Baowen , Yu Baoqi , Liu Delin
Published: December 2024

There is always zero-sequence current in the MV network because of its asymmetric parameters of transmission lines. Therefore, studying the distribution characteristics of natural unbalanced current through the feeder of an asymmetric distribution network is conducive to the feeder selection. In this paper, the expression of the current of each feeder is derived and its composition and distribution characteristics are analyzed. Based on this, the steady-state model of the feeder is constructed. To eliminate the effects of natural unbalanced current, it is proposed to subtract the current before and after a fault occurrence, and subsequently identify the faulted line according to phase-angle characteristics of the resulting zero-sequence current increment. Finally, a MATLAB/Simulink simulation system is constructed and employed for verification purposes.
Application and platform decoupling strategy of substation integrated monitoring system
Authors: Li Zhaoguo , Zheng Jingrong , Diao Dongyu , Zhou Hualiang , Zhang Ting , Ju Lu , Liu Zechen , Zhang Xin
Published: December 2024

Aiming at the problems of poor data opening and sharing ability and closed application ecology of substation integrated monitoring system, the open monitoring system of a substation based on platform + APP architecture is proposed, which realizes the decoupling of APP and the monitoring system vendor platform through the security reinforced container technology, unified data interaction technology, and data service-oriented middleware technology. The decoupling of APP and platform promotes the construction of an open and shared industrial ecosystem for substation-integrated monitoring systems, which in turn improves the level of substation intelligence.
Reliability assessment of distribution networks with distributed generation considering different types of user loads
Authors: Jia Keyin , Shi Lei , Wang Xiyue , Chai Fangsen , Cui Yu
Published: December 2024

This paper meticulously evaluates the reliability of distribution networks integrated with distributed generation (DG), considering various user classifications. Initially, we delve into the daily load profiles of residential, commercial, and industrial sectors, establishing a nuanced time-series load model that encapsulates the distinct characteristics of these customer segments. Subsequently, we formulate a model for the harmonious operation of energy storage systems, tailored to the interplay between DG output and load requirements. Utilizing the sequential Monte Carlo simulation algorithm, we compute the reliability metrics for the IEEE RBTSBUS6 feeder system, revealing that DG integration significantly enhances distribution network reliability. Notably, identical DG output yields varied impacts on the reliability of distinct customer sectors, underscoring the need for tailored strategies in DG deployment.
Power grid accident inversion and risk identification based on adaptive fuzzy inference algorithm
Authors: Zhang Lu , Nan Dongliang , Zhao Qi , Tan Jinlong , Wang Chang , Liu Yongqiang
Published: December 2024

Based on power grid accident inversion and risk identification, a self-adaptive fuzzy inference algorithm has been designed. Using spatial reconstruction method to extract feature information from multi-channel risk data of power grid accidents, introducing the concept of risk accident hazard assessment value, and using it as a variable in the risk data perception model, a basic inversion model based on the selection and transformation of power grid accident risk variables is established. Fuzzy logic units are combined with neural networks to propose an adaptive neuro-fuzzy inference algorithm. Applying adaptive fuzzy reasoning algorithm to the integrated design of power grid accident inversion and risk identification, achieving risk identification. The experimental results show that the experimental data accuracy of this design method is the highest, reaching 0.9907, with an accuracy of 0.9336, a recall rate of 0.9214, a recognition time of 2.84 seconds, and a significant advantage in processing speed.
Virtual 3D modeling of thermal power plant turbines based on bilateral filtering algorithm for point cloud data
Authors: Zhai Weihao , Zhang Yifei , Jia Yun , He Jiahao
Published: December 2024

Point cloud segmentation and recognition for virtual 3D wind turbine modeling in thermal plants are disjointed, limiting coverage and elevating mean square error. A design leveraging bilateral filtering of point cloud data for virtual 3D wind turbine modeling is proposed to address this. This approach encompasses data acquisition, preprocessing, and multi-level segmentation to enhance coverage. Bilateral filtering is then applied to calculate the 3D model, with edge correction and verification. Tests reveal that compared to traditional k-means clustering, reduced project space, and hybrid filtering methods, the proposed bilateral filtering algorithm significantly reduces mean square error, maintaining it below 10, demonstrating improved stability, safety, and practical value for thermal plant fan modeling.
Selection of an oxygen injector for waste oxygen enrichment incineration with dual carbon targets
Authors: Wang Ke
Published: December 2024

Waste incineration is one of the most effective means of achieving harmlessness, minimization, and resource utilization. As environmental problems become more and more prominent, the more stringent environmental standards will greatly limit the development of conventional waste incineration technology. Therefore, the development of a new type of clean and efficient waste incineration technology will be the inevitable development trend of the waste incineration industry. Oxygen-enriched injector sizing study was carried out based on Computational Fluid Dynamics (CFD) techniques for the oxygen-enriched incineration technology used in waste grate incinerators. The application of CFD method is applied to different types of oxygen injection device velocity fields, temperature fields, concentration fields, etc. The results show that the ring-type oxygen injector can achieve a good mixing effect, and the resistance pressure drop is minimized, which is better than the nozzle-type oxygen injector to meet the actual needs of the project.
Analysis of energy storage optimization configuration based on the new rural energy system
Authors: Fan Zijian , Wei Xiao
Published: December 2024

Due to the influence of weather factors, distributed photovoltaics exhibit a characteristic of high output in the afternoon and low output in the evening, resulting in an imbalanced supply and demand of rural electricity, a low utilization rate of distributed photovoltaics, and slow progress in poverty alleviation in rural areas. This paper proposes a distributed energy storage optimization configuration model that takes into account various factors such as rural household electricity consumption, aquaculture load power supply characteristics, distributed photovoltaic output characteristics, and the lease period of distributed energy storage box battery modules. The optimization objective is to maximize the benefits of distributed photovoltaic systems in rural areas and minimize the investment costs of farmers, with distributed photovoltaic capacity and the lease period of distributed energy storage as decision variables. Further, considering the problem of the Slap Swarm Algorithm being prone to falling into local optima when solving the model, a set of optimal points is introduced to initialize the population and improve the algorithm’s global search ability. Finally, the effectiveness of the proposed model was verified through a comprehensive example of rural distributed photovoltaics.
Particle swarm optimisation of variable offset dual cross-limit combustion control strategies for the industrial boiler combustion
Authors: Liu Bao , Wang Guanrong , Wang Zaiying
Published: December 2024

This paper proposes an optimal control method for industrial boilers using particle swarm optimisation (PSO) algorithms to enhance the variable offset control technique. The method targets challenges such as suboptimal combustion air coefficients and prolonged system steam pressure stabilisation time during boiler production processes. By optimising the offset function and parameters in the variable offset dual cross-limit combustion control scheme, this paper aims to refine the combustion control model. Experimental results indicate that this improved PSO-based approach successfully maintains flue gas oxygen content within optimal combustion ranges under variable load conditions. These results ensure higher combustion efficiency and contribute to the reduction of environmental pollution, thus verifying the effectiveness of the optimised control system in practical applications.
Research on power flow calculation methods applicable to new energy generating station area
Authors: Liu Zhong , Yang Chuan , Xia Lei , Wu Jing , Zhou Guozheng
Published: December 2024

With the construction and development of new distribution networks, more and more new components such as distributed power supply and active loads have been connected to the new energy generation station area, posing new requirements for operational control in the new energy generation station area. However, the power supply and load coexist in the new energy generation station area, with both single-phase and three-phase, significant power fluctuations, inaccurate calculation parameters, and incomplete measurement information. Therefore, achieving accurate and efficient calculations plays an important role. This article proposes a power flow calculation method suitable for new energy generation station areas. Firstly, the three parts of this method are explained: overall scheme, stationary correction link, and generalized scene mapping; Secondly, the correctness and applicability of this method were verified through simulation.
A proximal bundle technique for solving equilibrium problems with inexact information
Authors: Feng Yongxiu , Huang Ming , Zhang Siqi , Chao Xiaodan , Lin Sida
Published: December 2024

In this study, we describe a bundle approach used to solve the nonsmooth equilibrium problems was described. Firstly, a general algorithm was considered, and the algorithm’s validity was demonstrated by showing how it can provide a sequence that converges to an approximate solution for the equilibrium problem. Moreover, in this strategy, using the inaccurate value of the objective function and the approximate gradient of the adjacent points, a proximal bundle algorithm is created to solve equilibrium problems, and the inaccurate value makes the subproblem easy to solve and maintains convergence. So, we find a simpler way to solve the equilibrium problem.
The inverse problem of time-fractional order and volatility for European option under time-fractional Black-Scholes model
Authors: Liu Siwen , Xu Zuoliang
Published: December 2024

In this paper, we discuss the inverse problem of time-fractional order and volatility for European option pricing under the time-fractional Black-Scholes model. This inverse problem is an optimization problem, which is proposed by the Tikhonov regularization method, and the alternate direction multiplier method (ADMM) is used to identify the time-fractional order and volatility of the pricing model. Numerical simulation and empirical analysis are given to demonstrate the effectiveness of the proposed method.