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  • Open Access

    ARTICLE

    Research on Grid-Connected Control Strategy of Distributed Generator Based on Improved Linear Active Disturbance Rejection Control

    Xin Mao*, Hongsheng Su, Jingxiu Li

    Energy Engineering, Vol.121, No.12, pp. 3929-3951, 2024, DOI:10.32604/ee.2024.057106 - 22 November 2024

    Abstract The virtual synchronous generator (VSG) technology has been proposed to address the problem of system frequency and active power oscillation caused by grid-connected new energy power sources. However, the traditional voltage-current double-closed-loop control used in VSG has the disadvantages of poor disturbance immunity and insufficient dynamic response. In light of the issues above, a virtual synchronous generator voltage outer-loop control strategy based on improved linear autonomous disturbance rejection control (ILADRC) is put forth for consideration. Firstly, an improved first-order linear self-immunity control structure is established for the characteristics of the voltage outer loop; then, the… More >

  • Open Access

    REVIEW

    Research Progress of Photovoltaic Power Prediction Technology Based on Artificial Intelligence Methods

    Daixuan Zhou1, Yujin Liu1, Xu Wang2, Fuxing Wang1, Yan Jia2,*

    Energy Engineering, Vol.121, No.12, pp. 3573-3616, 2024, DOI:10.32604/ee.2024.055853 - 22 November 2024

    Abstract With the increasing proportion of renewable energy in China’s energy structure, among which photovoltaic power generation is also developing rapidly. As the photovoltaic (PV) power output is highly unstable and subject to a variety of factors, it brings great challenges to the stable operation and dispatch of the power grid. Therefore, accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy. Currently, the short-term prediction of PV power has received extensive attention and research, but the accuracy and precision of the prediction have to be further improved. More > Graphic Abstract

    Research Progress of Photovoltaic Power Prediction Technology Based on Artificial Intelligence Methods

  • Open Access

    ARTICLE

    Combined Wind-Storage Frequency Modulation Control Strategy Based on Fuzzy Prediction and Dynamic Control

    Weiru Wang1, Yulong Cao1,*, Yanxu Wang1, Jiale You1, Guangnan Zhang1, Yu Xiao2

    Energy Engineering, Vol.121, No.12, pp. 3801-3823, 2024, DOI:10.32604/ee.2024.055398 - 22 November 2024

    Abstract To ensure frequency stability in power systems with high wind penetration, the doubly-fed induction generator (DFIG) is often used with the frequency fast response control (FFRC) to participate in frequency response. However, a certain output power suppression amount (OPSA) is generated during frequency support, resulting in the frequency modulation (FM) capability of DFIG not being fully utilised, and the system’s unbalanced power will be increased during speed recovery, resulting in a second frequency drop (SFD) in the system. Firstly, the frequency response characteristics of the power system with DFIG containing FFRC are analysed. Then, based… More >

  • Open Access

    ARTICLE

    Photovoltaic Power Generation Power Prediction under Major Extreme Weather Based on VMD-KELM

    Yuxuan Zhao1,2,*, Bo Wang1, Shu Wang1, Wenjun Xu2, Gang Ma2

    Energy Engineering, Vol.121, No.12, pp. 3711-3733, 2024, DOI:10.32604/ee.2024.054032 - 22 November 2024

    Abstract The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather… More >

  • Open Access

    ARTICLE

    DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection

    Yuanyuan Ma1, Ang Chen1, Cunzhi Hou1, Ruixia Jin2, Jinghui Zhang1, Ruixiang Li3,4,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3301-3323, 2024, DOI:10.32604/cmc.2024.056854 - 18 November 2024

    Abstract Currently, telecom fraud is expanding from the traditional telephone network to the Internet, and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights. However, existing telecom fraud identification methods based on blacklists, reputation, content and behavioral characteristics have good identification performance in the telephone network, but it is difficult to apply to the Internet where IP (Internet Protocol) addresses change dynamically. To address this issue, we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise) clustering (DC-FIPD). First, we… More >

  • Open Access

    REVIEW

    A Review of Generative Adversarial Networks for Intrusion Detection Systems: Advances, Challenges, and Future Directions

    Monirah Al-Ajlan*, Mourad Ykhlef

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2053-2076, 2024, DOI:10.32604/cmc.2024.055891 - 18 November 2024

    Abstract The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems (IDSs). IDSs have become a research hotspot and have seen remarkable performance improvements. Generative adversarial networks (GANs) have also garnered increasing research interest recently due to their remarkable ability to generate data. This paper investigates the application of (GANs) in (IDS) and explores their current use within this research field. We delve into the adoption of GANs within signature-based, anomaly-based, and hybrid IDSs, focusing on their objectives, methodologies, and advantages. Overall, GANs have been widely employed, mainly focused on solving the More >

  • Open Access

    ARTICLE

    Adaptive Nonlinear PD Controller of Two-Wheeled Self-Balancing Robot with External Force

    Van-Truong Nguyen1,*, Dai-Nhan Duong1, Dinh-Hieu Phan1, Thanh-Lam Bui1, Xiem HoangVan2, Phan Xuan Tan3

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2337-2356, 2024, DOI:10.32604/cmc.2024.055412 - 18 November 2024

    Abstract This paper proposes an adaptive nonlinear proportional-derivative (ANPD) controller for a two-wheeled self-balancing robot (TWSB) modeled by the Lagrange equation with external forces. The proposed control scheme is designed based on the combination of a nonlinear proportional-derivative (NPD) controller and a genetic algorithm, in which the proportional-derivative (PD) parameters are updated online based on the tracking error and the preset error threshold. In addition, the genetic algorithm is employed to adaptively select initial controller parameters, contributing to system stability and improved control accuracy. The proposed controller is basic in design yet simple to implement. The… More >

  • Open Access

    ARTICLE

    Identification of M2 macrophage-related genes for establishing a prognostic model in pancreatic cancer: FCGR3A as key gene

    ZHEN WANG1, JUN FU1, SAISAI ZHU1, HAODONG TANG2, KUI SHI1, JIHUA YANG3, MENG WANG3, MENGGE WU1, DUNFENG QI1,*

    Oncology Research, Vol.32, No.12, pp. 1851-1866, 2024, DOI:10.32604/or.2024.055286 - 13 November 2024

    Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) has a rich and complex tumor immune microenvironment (TIME). M2 macrophages are among the most extensively infiltrated immune cells in the TIME and are necessary for the growth and migration of cancers. However, the mechanisms and targets mediating M2 macrophage infiltration in pancreatic cancer remain elusive. Methods: The M2 macrophage infiltration score of patients was assessed using the xCell algorithm. Using weighted gene co-expression network analysis (WGCNA), module genes associated with M2 macrophages were identified, and a predictive model was designed. The variations in immunological cell patterns, cancer mutations, and… More > Graphic Abstract

    Identification of M2 macrophage-related genes for establishing a prognostic model in pancreatic cancer: <i>FCGR3A</i> as key gene

  • Open Access

    ARTICLE

    Using Multi-Omics Analysis to Explore Diagnostic Tool and Optimize Drug Therapy Selection for Patients with Glioma Based on Cross-Talk Gene Signature

    YUSHI YANG1,#, CHUJIAO HU2,#, SHAN LEI3, XIN BAO3, ZHIRUI ZENG3,*, WENPENG CAO4,*

    Oncology Research, Vol.32, No.12, pp. 1921-1934, 2024, DOI:10.32604/or.2024.046191 - 13 November 2024

    Abstract Background: The heterogeneity of prognosis and treatment benefits among patients with gliomas is due to tumor microenvironment characteristics. However, biomarkers that reflect microenvironmental characteristics and predict the prognosis of gliomas are limited. Therefore, we aimed to develop a model that can effectively predict prognosis, differentiate microenvironment signatures, and optimize drug selection for patients with glioma. Materials and Methods: The CIBERSORT algorithm, bulk sequencing analysis, and single-cell RNA (scRNA) analysis were employed to identify significant cross-talk genes between M2 macrophages and cancer cells in glioma tissues. A predictive model was constructed based on cross-talk gene expression, and… More >

  • Open Access

    PROCEEDINGS

    Identification of the Anisotropic Thermal-Mechanical Properties of Sheet Metals Using the Virtual Fields Method

    Jiawei Fu1,2,*, Yahui Cai1, Bowen Zhang1, Zengxiang Qi1, Lehua Qi1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.013007

    Abstract The accurate characterization of the anisotropic thermal-mechanical constitutive properties of structural sheet metals at elevated temperatures and under nonuniform stress/strain states is crucial for the precise hot plastic forming and structural behavior evaluation of an engineering sheet part. Traditional thermal-mechanical testing methods rely on the assumption of states homogeneity, leading to a large number of tests required for the characterization of material anisotropy and nonlinearity at various high temperatures. In this work, a highly efficient identification method is proposed that allows the simultaneous characterization of the anisotropic yielding, strain hardening and elasto-plasticity thermal softening material More >

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