bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of Assembler and C Programming Languages on PIC16F877 Microcontroller
Authors: Woo‑Garcia Rosa M. , Argüelles-Lucho Primavera , Montes de Oca‑Mora Natanael J. , Salas-Rodriguez Silvestre , Sanchez-Vidal Adrián , Ceron-Alvarez Carlos A. , Osorio-de la Rosa Edith 
Published: February 2024

Abstract
The programming of microcontrollers is essential for their correct operation, high computational efficiency, low-power solutions, and it is also key to get the most out of their capabilities with the hardware features that each of the microcontroller families has. There are different programming languages that can be used in microcontrollers, in this work we will focus on C and Assembler. C is one of the most widely used due to its efficiency and its ability to control the hardware directly. Assembly is a low-level language that allows more control over the instructions executed in the microcontroller, two songs have been implemented in assembler and C languages and the resources used for their execution on a Microchip PIC16F877 have been analyzed. The research results allow the developer to obtain results for a selection of the optimal hardware platform as well as the programming language according to the required memory utilization. Coding performed in MPLAB software with the asm and XC8 compilers, indicate that the C-code programs cover a larger amount of memory close to 4 times greater or higher.
Impact of Rhodamine B solution on charge carrier generation and photocatalytic performance in CdS/TiO2 heterostructures synthesized by CBD.
Authors: Cruz González N. , Noguez del Águila O. , Roque J. , Silva-Galindo G. , Torres Frausto C. , Zapata-Torres M. 
Published: February 2024

Abstract
This article presents the study of Rhodamine B solution effects on the generation of charge carriers and the photocatalytic performance of CdS/TiO2 heterostructure films. CdS/TiO2 heterostructure films were synthesized using the chemical bath deposition technique (CBD). The structure and morphology of the heterostructures were studied by X-ray diffraction (XRD), and Scanning Electron Microscope (SEM), respectively. The optical properties of the CdS/TiO2 heterostructures were characterized using a UV–vis spectrophotometer. The results showed that the CdS/TiO2 optical band gap energy (Eg) was 2.75 eV, between the TiO2 band gap energy (3.38 eV) and the CdS band gap energy (2.57 eV). The electrochemical characterization indicated that the photocurrent of the CdS/TiO2 heterostructure decreased from 29 mA to 12.4 mA when the UV-vis light source was turned off. The density of charge carriers generated on the surface of the CdS/TiO2 was simulated employing the WXAMPS software. The results suggest that the thickness of the TiO2 layer should be optimized to allow the activation of CdS. Finally, this study concludes that the presence of rhodamine B in the solution attenuates light transmission and affects the generated electron-hole pairs, resulting in poor photocatalytic behavior.
Control strategy and research on energy storage unit participation in power system frequency regulation based on VSG technology
Authors: Lv Zhengqiang , Xu Jia , Pang Yuanqi , Tan Litao , Zheng Han 
Published: February 2024

Abstract
When large-scale photovoltaic power plants are integrated into the grid, it will cause the grid-connected capacity to gradually expand, causing greater harm to the safe and stable operation of the power system, which will affect the FM characteristics of the power system. This requires the PV power plant to actively participate in power system frequency control. Through the PV virtual synchronous generator frequency control technology, coupled with the virtual synchronous PV power plant modeling, the PV new energy units can have the same frequency control characteristics as synchronous generator sets. In the virtual synchronous technology, due to the existence of damping windings, electromagnetic damping will be generated, and it is also due to virtual damping that the virtual synchronous technology has the ability to damp power oscillations. It is also because of the excellent virtual inertia and damping characteristics of the virtual synchronous generator in the grid that this FM technology helps to regulate the frequency fluctuations of the new energy grid, meet the requirements of the power system to maintain stable operation, and then realize the large-scale PV grid connection.
Optimization model of new energy consumption based on load demand-side response
Authors: Guan Yan , Cai Yinong , Gao Xiying , Lu Xinyi 
Published: February 2024

Abstract
As a technically and economically feasible and potentially resource-rich regulation tool, demand-side response can effectively strengthen the guarantee of new energy consumption, and is expected to play an enhanced key role in the construction of new power systems. This paper analyzes the factors affecting the linkage between demand-side and new energy generation side, the promotion of new energy consumption by demand-side response, and proposes a method to respond to the change of new energy output from the customer side, using interruptible load, transferable load, shiftable load, and curtailable load strategies to respond to the peak regulation problem brought by new energy access to the grid. The optimal demand-side response strategy is obtained by constructing a new energy consumption optimization model and solving it with the direct non-inferiority solution method, taking into account the economic and new energy consumption objectives. Finally, the effectiveness of the method is verified by simulating the IEEE 37-node system with multiple new energy configurations.
Passivity and higher-order sliding mode control for doubly-fed induction generation wind power system
Authors: Li Xiaolei , Shao Mingyan , Xu Jiadong , Zhu Yu , Zhang Hongzhe , Li Chen , Dong Xu , Li Siyuan 
Published: February 2024

Abstract
To simplify the model of DGIG and improve the robustness, the variable speed and constant frequency (VSCF) DFIG wind power generation systems from the perspective of energy and robustness were studied. It was based on passivity and sliding mode control (SMC) theory. Firstly, the model of DFIG based on Euler-Lagrange (EL) equations was established, so the original DFIG wind turbine system is divided into electrical and mechanical subsystems. For two subsystems, the electrical subsystem was designed. In contrast, the mechanical subsystem is an energy-consuming system and is stable through selecting suitable design parameters. Hence, the system control algorithm is simplified. A high-order terminal SMC was designed to get a faster speed and better robustness for the speed loop. Simulation results show that the proposed controller is simple and effective. It can guarantee the wind power system outputs constant frequency. The motor speed can also track the reference speed quickly, so the performance of the wind turbine system is improved.
Research on energy storage charging piles based on improved genetic algorithm
Authors: Sun Ningbo 
Published: February 2024

Abstract
Aiming at the charging demand of electric vehicles, an improved genetic algorithm is proposed to optimize the energy storage charging piles optimization scheme. Firstly, the characteristics of electric load are analyzed, the model of energy storage charging piles is established, the charging volume, power and charging/discharging timing constraints in the charging process are considered, and the optimization is carried out by the improved genetic algorithm, and the profit of charging piles and user charging electricity charges are calculated, so as to obtain the possible optimal parameters, as well as the impact of the energy storage system on the stability of the power grid. Secondly, the analysis of the results shows that the energy storage charging piles can not only improve the profit to reduce the user’s electricity cost, but also reduce the impact of electric vehicle charging on the power grid load.
The influence of training datasets on LSTM-based irradiance prediction
Authors: Li Lei , Zhang Lei , Li Dengxuan , Zhang Weitao , Yue Han 
Published: February 2024

Abstract
In solar photovoltaic power generation, the prediction of solar irradiance is essential for minimizing energy costs and ensuring the provision of high-quality electricity. Deep learning models have recently gained popularity in the field, as numerous scholars have successfully employed them to predict solar irradiance. In line with this, this paper proposes three distinct methods for dividing the training dataset. Subsequently, these methods are employed in predicting solar irradiance using the LSTM-based model. Furthermore, an error analysis of the prediction results is conducted for each of the three models. The optimal training dataset division method is determined and proposed based on a comparison of the sizes of the three models using six error evaluation indexes.
Optimal power flow calculation method for regional integrated energy system based on double-layer optimization
Authors: Zhang Tianshuo , Zhao Yan , Liu Renshuo , Chi Huazhi 
Published: February 2024

Abstract
In this paper, a two-layer optimization calculation method is proposed to improve the practical application efficiency of the integrated energy system and to improve the different problems of power flow calculation. Firstly, the power flow model is constructed and the optimization function is established based on indicators such as economy and environmental protection. The two-layer optimization method is proposed by combining the quadratic programming method and the particle swarm algorithm. Then, the obtained operating conditions of each energy equipment are used as the input of the power flow. The processing of each device and the energy flow of the power flow network are calculated and the effectiveness of the algorithm is verified by MATLAB power analysis.
Using the modified one-dimensional model to predict the mixed oil tailing of refined oil pipeline
Authors: Xiao Fan , Wang Yubin , Yang Qi , Xiao Mengsi 
Published: February 2024

Abstract
The tailing phenomenon in refined oil pipelines leads to an increase in the amount of mixed oil, resulting in significant economic losses. Therefore, it is of great significance to identify the factors that affect tailing, clarify the development rules of tailing oil, and manage the operation of refined oil pipelines. This article proposes a method to modify a one-dimensional (1D) oil mixing model, which takes into account the influence of wall-attached oil detachment rate and trailing oil concentration on tailing. A theoretical model that can describe the asymmetric development of the mixing interface is presented, and the development of the mixing interface under different detachment rates is discussed based on this model. The results show that this model can describe the tailing phenomenon in oil mixing. As the wall-attached oil detachment rate decreases, the length of the mixing zone gradually increases, the amount of mixed oil increases, and the asymmetry of the mixing interface increases.
Optimal operation of electric-heat-gas-hydrogen integrated energy system considering source-load uncertainty
Authors: Zu Weican , Wu Zhi , Wang Haolin , Zheng Shu , Wu Yuanxi , Hu Pengcheng 
Published: February 2024

Abstract
Faced with the threat of global climate change crisis, increasing the use of hydrogen-enriched compressed natural gas (HCNG) and the utilization rate of renewable energy can both reduce the carbon emissions of the integrated energy system (IES). This paper establishes an electric-heat-gas-hydrogen integrated energy system (EHGH-IES) framework considering source-load uncertainty, and the natural gas pipe network in the traditional IES is transformed into a HCNG pipe network with a certain hydrogen blending ratio. An optimal operation model of the EHGH-IES considering source-load uncertainty is proposed in this paper. The chance constrained programming method is adopted to deal with source-load uncertainty. We pay attention to comparing different confidence coefficient and hydrogen blending ratio in terms of the changes in the cost of the IES. The results show that the total cost of the EHGH-IES will increase by considering source-load uncertainty and using HCNG pipeline, but will bring some stability and environmental benefits to the IES by them.
Study on the influence of expansion section parameters on the performance of compressed air energy storage system
Authors: Li Fengyu , Shu Yue , Liu Xiaoming , Xiao Jun 
Published: February 2024

Abstract
Compressed air energy storage (CAES) gains attention because of its promising future application. In this paper, the parameters related to the expansion section, such as the heat exchanger efficiency, the high-pressure storage tank outlet air temperature and the high-pressure storage tank outlet air pressure, are selected to study their effects on the performance and setting of the CAES system. The results show that with the change in heat exchanger efficiency, air temperature and pressure at the tank outlet, the compression and expansion stage numbers are significantly affected. Because of the influence of the change of compression and expansion stage numbers, the hot water residual temperature, the efficiency of the first cycle, the efficiency after multiple cycles, and the area of the heat exchanger have changed. It can be seen that the change of expansion section parameters mainly affects the system performance by affecting the setting of compressor and expander stages.
Optimization of energy storage capacity using distributionally robust optimization for converter-based grids
Authors: Liu Jinjun , Han Minxiao , Tang Xiaojun , Sun Tiankui , Xie Wenqiang 
Published: February 2024

Abstract
As the penetration of converters into the grid continues to increase, converter-based power sources are replacing synchronous machine-based power sources, leading to the establishment of a converter-based grid (CBG) for regulating grid frequency and voltage. At this stage, energy storage becomes a necessity to support the operation of the CBG. The grid-interfacing inverters are transitioning from the conventional grid-following (GFL) control to the grid-forming (GFM) control. within the context of this research paper, considering the premise that energy storage capacity must meet CBG stability constraints and accommodate the integration of renewable energy sources, a capacity optimization model is constructed based on distributional robust optimization. The primary objective of this model is to minimize the overall cost with the allocation of energy storage capacity as the central focus. The study includes case analyses conducted within an IEEE-14 node CBG. The results of the case analyses confirm that the determined energy storage capacity can ensure the stable operation of the CBG and meet the demands for integrating renewable energy sources.
Flexible resource optimal allocation strategy for distribution system resilience improvement under failure uncertainty
Authors: Gu Jingda , Li Ying , Ju Li , Zuo Xianghong , Zhao Yuheng 
Published: February 2024

Abstract
Extreme disasters have led to huge economical losses to the power system and posed a huge threat to the stable supply of electric energy. Especially for traditional passive distribution network systems, failures of transmission lines, towers, and other equipment will cause large-scale power outages and huge economic losses. In order to improve the ability of the power system to deal with extreme weather disasters, this paper proposes an optimal configuration strategy for the distribution network backup power resource oriented to resilience improvement, considering the uncertainty of equipment failure caused by disasters in the case of a known disaster range. The location and capacity of backup resources can be properly configured to reduce the load shedding of the power distribution network during the disaster process, thereby improving the resilience of the distribution network. The method proposed in this paper is modeled as a two-stage stochastic programming, which includes mix-integer constraints. In the first stage, the optimization problem confirms the capacity and location of the flexible resources. In the second stage, the operational constraints and costs of load shedding are considered. The resilience enhancement method is verified on the improved IEEE-33 node system, and the result shows that the method proposed in this paper can effectively enhance the resilience of the urban power network.
Research on ultra-short-term load forecasting method based on PCA-MIC-LSTM
Authors: Pan Lianrong , Lv Jia , Li Peikai , Fu Yuan , Chen Xinling 
Published: February 2024

Abstract
This research presents a PCA-MIC-LSTM-based ultra-short-term electric load forecasting approach. This method uses Principal Component Analysis (PCA) technology to denoise power load data, uses the Maximal Information Coefficient (MIC) method to perform feature screening and correlation analysis on the processed data and finally selects some features with high correlation to input Long-Short-Term Memory Neural Network (LSTM) for training and modeling. Through actual case analysis, this method reduces the prediction error MAPE by 0.29% compared to the basic LSTM model. Compared with LightGBM, Xgboost, and SVR models, the prediction error MAPE is reduced by 0.16%, 0.11%, and 0.32%, respectively. It demonstrates the validity of the approach established in this investigation and provides technical support and a theoretical basis for scientific decision-making to optimize the precision of ultra-short-term electric load prediction in power systems.
Frequency regulation scheme for islanded microgrid
Authors: Shao Mingyan , Li Xiaolei , Li Chen , Zhang Hongzhe , Zhu Yu , Dong Xu , Xu Jiadong , Chen Bin 
Published: February 2024

Abstract
When the main grid of the power grid malfunctions undergoes emergency repairs, the microgrid is in off-grid operation. At this time, the microgrid in an isolated state must have self-frequency regulation capability. This article proposes a frequency control strategy for isolated microgrids, which can improve their ability to resist frequency interference. The proposed control strategy considers the different characteristics and capabilities of various micro-sources for frequency regulation. It classifies the frequency modulation micro-sources in the microgrid. Judgment conditions and parameters are set to determine whether each micro-source participates in frequency modulation and the participating components. The isolated microgrid has an economical and fast frequency modulation strategy. Finally, a microgrid simulation model is established using Matlab/Simulink simulation software to verify the effectiveness of the proposed frequency modulation strategy.
The influence of external conditions on the physical characteristics of Press-Pack IGBTs
Authors: Lu Yujia , Jiao Chaoqun 
Published: February 2024

Abstract
In order to solve the problem of unclear factors affecting the physical characteristics of Press-Pack IGBT devices, this paper explores the influence characteristics of the two main external conditions of fixture pressure and heat dissipation capacity on the physical parameters of Press-Pack IGBTs by building IGBT models with different chip structure layout considering various physical characteristics of materials. The results show that the effect of fixture pressure on the stress characteristics of the device is more obvious than the heat dissipation ability of the device, and the water-cooling heat flux has more influence on the electrical-thermal characteristics of the device. The research results provide a reference for the external conditions and structure selection of Press-Pack IGBTs.
Research and application of target detection algorithm for live operation in substation
Authors: Yang Fayu , Duan Xuejiao , Wang Jia , Wang Yueming , Zhang Hong-an 
Published: February 2024

Abstract
The substation is an important node connecting the backbone network and distribution network and is the core hub of the smart grid. With the continuous increase of electrical equipment, the substation plays a vital role in the whole power system. The structural principle of deep learning convolutional neural network is deeply studied, which lays a theoretical foundation for substation fault detection and recognition based on the object detection algorithm. This paper focuses on fault detection algorithms based on deep learning. First, two mainstream target detection algorithms, Faster R-CNN and YOLO V5, are applied to the self-made substation fault data set. The experimental comparison shows that while the YOLO V5 algorithm has a slightly higher recognition speed, the accuracy of fault target recognition and similar background image recognition is higher than that of the Faster R-CNN algorithm. Then, for the benchmark network YOLO V5, an improved method is proposed. The innovative idea is to delete large branches from the network detection part for the detection of small faults in the station, so as to reduce the number of model parameters and improve the speed of model detection.
Ultra-short term prediction of wind power based on multi-resolution closed-loop method
Authors: Zhang Yuhang , Liu Wenhao 
Published: February 2024

Abstract
In order to improve the accuracy and stability of wind power prediction, a multi-resolution closed-loop wind power ultra-short term prediction method is proposed. Using historical data with different resolutions, the differential data is generated by the pre-predictor with low, medium and high resolutions, and then the differential data is combined with the post-prediction data into the post-predictor with three resolutions for training. Since the differential data is obtained from the prediction data with different resolutions, the post-prediction data containing the differential data contains inconsistent information between different resolutions, so that the post-predictor receives the inconsistency between different resolutions and minimizes the inconsistency after several closed-loop iterations, and finally outputs wind power prediction with different resolutions. At the end of the paper, simulation experiments are carried out using the data of the 2022 KDD Cup competition, and comparison is made with some mainstream wind power prediction models. It is found that the proposed model has high accuracy and stability.
Convergence criterion of power flow calculation based on graph neural network
Authors: Li Shenhao , Pan Zhichon , Li Hongyi , Xiao Yue , Liu Ming , Wang Xiaorui 
Published: February 2024

Abstract
In order to solve the problem of current data-driven power flow calculation methods rarely consider the divergence of power flow, which always maps a false system power flow when a divergence power flow case was given, a data-driven power flow convergence method based on DGAT-GPPool graph neural network classifier is proposed. Firstly, to solve the problem that the classical graph convolution method does not consider the edge attribute, a double-view graph attention convolution layer is constructed based on line admittance. Secondly, to solve the existing pooling method also does not consider the edge attribute and the loss of physical meaning of the coarse graph obtained from pooling, a grid partition pooling layer is constructed based on the electrical distance between nodes. Finally, 10000 system samples containing different network topologies are generated based on the IEEE 14-node system and its extended system, the accuracy reaches 99.3% in the testing set after training, and the effectiveness of the improvements in graph convolution and graph pooling is verified by comparative experiments.