Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (579)
  • Open Access

    ARTICLE

    Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

    Penghui Liu1,2,*, Yaning Zhang1, Yuxing Dai2, Yanzhou Sun1,3

    Energy Engineering, Vol.121, No.7, pp. 1883-1901, 2024, DOI:10.32604/ee.2024.049318

    Abstract Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points. The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index, which leads to detection failure when the arc zero-off characteristic is short. To solve this problem, this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines. Firstly, the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied. After that, the convex hull, gradient product,… More >

  • Open Access

    ARTICLE

    Research on the Icing Diagnosis of Wind Turbine Blades Based on FS–XGBoost–EWMA

    Jicai Guo1,2, Xiaowen Song1,2,*, Chang Liu1,2, Yanfeng Zhang1,2, Shijie Guo1,2, Jianxin Wu1,2, Chang Cai3, Qing’an Li3,*

    Energy Engineering, Vol.121, No.7, pp. 1739-1758, 2024, DOI:10.32604/ee.2024.048854

    Abstract In winter, wind turbines are susceptible to blade icing, which results in a series of energy losses and safe operation problems. Therefore, blade icing detection has become a top priority. Conventional methods primarily rely on sensor monitoring, which is expensive and has limited applications. Data-driven blade icing detection methods have become feasible with the development of artificial intelligence. However, the data-driven method is plagued by limited training samples and icing samples; therefore, this paper proposes an icing warning strategy based on the combination of feature selection (FS), eXtreme Gradient Boosting (XGBoost) algorithm, and exponentially weighted… More >

  • Open Access

    ARTICLE

    An Algorithm for Short-Circuit Current Interval in Distribution Networks with Inverter Type Distributed Generation Based on Affine Arithmetic

    Yan Zhang1, Bowen Du2,*, Benren Pan1, Guannan Wang1, Guoqiang Xie1, Tong Jiang2

    Energy Engineering, Vol.121, No.7, pp. 1903-1920, 2024, DOI:10.32604/ee.2024.048718

    Abstract During faults in a distribution network, the output power of a distributed generation (DG) may be uncertain. Moreover, the output currents of distributed power sources are also affected by the output power, resulting in uncertainties in the calculation of the short-circuit current at the time of a fault. Additionally, the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources. Thus, it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network. In this study, an affine arithmetic algorithm… More >

  • Open Access

    REVIEW

    A Research Progress of CO2-Responsive Plugging Channeling Gels

    Yang Xiong1,2, Jianxin Liu1,2,*, Xianhao Yi2, Bangyan Xiao2, Dan Wu2, Biao Wu2, Chunyu Gao2

    Energy Engineering, Vol.121, No.7, pp. 1759-1780, 2024, DOI:10.32604/ee.2024.048536

    Abstract In the heterogeneous reservoirs, CO2 flooding easily leads to CO2 gas channeling, which can seriously affect sweeping efficiency and reduce oil recovery. After thoroughly investigating the advantages and shortcomings of various CO2 plugging technologies, this paper focuses on the feasibility of improving conventional water-alternating gas (WAG) through CO2-responsive gel materials. Based on the different chemical reaction mechanisms between the unique chemical structure and CO2, changes in the material’s physical and chemical properties can respond to CO2. The feasibility of utilizing these property changes for CO2-responsive plugging is explored. Various CO2-responsive gels and gel nanoparticles have been extensively researched in More >

  • Open Access

    ARTICLE

    Optimization Study of Active-Passive Heating System Parameters in Village Houses in the Southern Xinjiang Province

    Xiaodan Wu1, Jie Li1,*, Yongbin Cai2, Sihui Huang1

    Energy Engineering, Vol.121, No.7, pp. 1963-1990, 2024, DOI:10.32604/ee.2024.048477

    Abstract Aiming at the problems of large energy consumption and serious pollution of winter heating existing in the rural buildings in Southern Xinjiang, a combined active-passive heating system was proposed, and the simulation software was used to optimize the parameters of the system, according to the parameters obtained from the optimization, a test platform was built and winter heating test was carried out. The simulation results showed that the thickness of the air layer of 75 mm, the total area of the vent holes of 0.24 m, and the thickness of the insulation layer of 120… More >

  • Open Access

    ARTICLE

    Carbon Emission Factors Prediction of Power Grid by Using Graph Attention Network

    Xin Shen1, Jiahao Li1, Yujun Yin1, Jianlin Tang2,3,*, Weibin Lin2,3, Mi Zhou2,3

    Energy Engineering, Vol.121, No.7, pp. 1945-1961, 2024, DOI:10.32604/ee.2024.048388

    Abstract Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice, which is of immense importance in mobilizing the entire society to reduce carbon emissions. The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid. Therefore, it cannot provide carbon factor information beforehand. To address this issue, a prediction model based on the graph attention network is proposed. The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised More >

  • Open Access

    ARTICLE

    Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

    Ying Qu1, Jinhao Wang1, Xueting Cheng1, Jie Hao1, Weiru Wang1, Zhewen Niu2, Yuxiang Wu2,*

    Energy Engineering, Vol.121, No.7, pp. 1847-1863, 2024, DOI:10.32604/ee.2024.048300

    Abstract The data-driven transient stability assessment (TSA) of power systems can predict online real-time prediction by learning the temporal features before and after faults. However, the accuracy of the assessment is limited by the quality of the data and has weak transferability. Based on this, this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting (XGBoost) model. Firstly, the gradient detection method is employed to remove noise interference while maintaining the original time series trend. On this basis, a focal loss function is introduced to guide the training of… More >

  • Open Access

    ARTICLE

    Optimal Scheduling Strategy of Source-Load-Storage Based on Wind Power Absorption Benefit

    Jie Ma1, Pengcheng Yue2, Haozheng Yu1, Yuqing Zhang3, Youwen Zhang1, Cuiping Li3, Junhui Li3,*, Wenwen Qin3, Yong Guo1

    Energy Engineering, Vol.121, No.7, pp. 1823-1846, 2024, DOI:10.32604/ee.2024.048225

    Abstract In recent years, the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing, but the peak regulation capacity of the power grid in the three north regions of China is limited, resulting in insufficient local wind power consumption capacity. Therefore, this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid's wind power consumption capacity. The objective of the upper model is to minimize the peak-valley difference of the system load, which is mainly to optimize the system… More >

  • Open Access

    ARTICLE

    Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

    Feng Zhao, Guangdi Liu*, Xiaoqiang Chen, Ying Wang

    Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209

    Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

  • Open Access

    ARTICLE

    Synergizing Wind, Solar, and Biomass Power: Ranking Analysis of Off-Grid System for Different Weather Conditions of Iran

    Razieh Keshavarzi, Mehdi Jahangiri*

    Energy Engineering, Vol.121, No.6, pp. 1381-1401, 2024, DOI:10.32604/ee.2024.050029

    Abstract Nowadays, the use of renewable energies, especially wind, solar, and biomass, is essential as an effective solution to address global environmental and economic challenges. Therefore, the current study examines the energy-economic-environmental analysis of off-grid electricity generation systems using solar panels, wind turbines, and biomass generators in various weather conditions in Iran. Simulations over 25 years were conducted using HOMER v2.81 software, aiming to determine the potential of each region and find the lowest cost of electricity production per kWh. In the end, to identify the most suitable location, the Technique for Order Preference by Similarity… More > Graphic Abstract

    Synergizing Wind, Solar, and Biomass Power: Ranking Analysis of Off-Grid System for Different Weather Conditions of Iran

Displaying 21-30 on page 3 of 579. Per Page