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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

    Maximum Power Point Tracking Based on Improved Kepler Optimization Algorithm and Optimized Perturb & Observe under Partial Shading Conditions

    Zhaoqiang Wang1, Fuyin Ni2,*

    Energy Engineering, Vol.121, No.12, pp. 3779-3799, 2024, DOI:10.32604/ee.2024.055535 - 22 November 2024

    Abstract Under the partial shading conditions (PSC) of Photovoltaic (PV) modules in a PV hybrid system, the power output curve exhibits multiple peaks. This often causes traditional maximum power point tracking (MPPT) methods to fall into local optima and fail to find the global optimum. To address this issue, a composite MPPT algorithm is proposed. It combines the improved kepler optimization algorithm (IKOA) with the optimized variable-step perturb and observe (OIP&O). The update probabilities, planetary velocity and position step coefficients of IKOA are nonlinearly and adaptively optimized. This adaptation meets the varying needs of the initial… More > Graphic Abstract

    Maximum Power Point Tracking Based on Improved Kepler Optimization Algorithm and Optimized Perturb & Observe under Partial Shading Conditions

  • 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

    REVIEW

    A Systematic Literature Review on Blockchain Consensus Mechanisms’ Security: Applications and Open Challenges

    Muhammad Muntasir Yakubu1,2,*, Mohd Fadzil B Hassan1,3, Kamaluddeen Usman Danyaro1, Aisha Zahid Junejo4, Muhammed Siraj5, Saidu Yahaya1, Shamsuddeen Adamu1, Kamal Abdulsalam6

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1437-1481, 2024, DOI:10.32604/csse.2024.054556 - 22 November 2024

    Abstract This study conducts a systematic literature review (SLR) of blockchain consensus mechanisms, an essential protocols that maintain the integrity, reliability, and decentralization of distributed ledger networks. The aim is to comprehensively investigate prominent mechanisms’ security features and vulnerabilities, emphasizing their security considerations, applications, challenges, and future directions. The existing literature offers valuable insights into various consensus mechanisms’ strengths, limitations, and security vulnerabilities and their real-world applications. However, there remains a gap in synthesizing and analyzing this knowledge systematically. Addressing this gap would facilitate a structured approach to understanding consensus mechanisms’ security and vulnerabilities comprehensively. The… More >

  • Open Access

    ARTICLE

    IoT-Enabled Plant Monitoring System with Power Optimization and Secure Authentication

    Samsul Huda1,*, Yasuyuki Nogami2, Maya Rahayu2, Takuma Akada2, Md. Biplob Hossain2, Muhammad Bisri Musthafa2, Yang Jie2, Le Hoang Anh2

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3165-3187, 2024, DOI:10.32604/cmc.2024.058144 - 18 November 2024

    Abstract Global food security is a pressing issue that affects the stability and well-being of communities worldwide. While existing Internet of Things (IoT) enabled plant monitoring systems have made significant strides in agricultural monitoring, they often face limitations such as high power consumption, restricted mobility, complex deployment requirements, and inadequate security measures for data access. This paper introduces an enhanced IoT application for agricultural monitoring systems that address these critical shortcomings. Our system strategically combines power efficiency, portability, and secure access capabilities, assisting farmers in monitoring and tracking crop environmental conditions. The proposed system includes a… More >

  • Open Access

    REVIEW

    AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice

    Mitra Madanchian1,*, Hamed Taherdoost1,2,3,4

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2133-2159, 2024, DOI:10.32604/cmc.2024.057094 - 18 November 2024

    Abstract This comparative review explores the dynamic and evolving landscape of artificial intelligence (AI)-powered innovations within high-tech research and development (R&D). It delves into both theoretical models and practical applications across a broad range of industries, including biotechnology, automotive, aerospace, and telecommunications. By examining critical advancements in AI algorithms, machine learning, deep learning models, simulations, and predictive analytics, the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies. The review integrates both qualitative and quantitative data derived from academic studies, industry reports, and real-world case studies to showcase the… More >

  • Open Access

    ARTICLE

    A Combined Method of Temporal Convolutional Mechanism and Wavelet Decomposition for State Estimation of Photovoltaic Power Plants

    Shaoxiong Wu1, Ruoxin Li1, Xiaofeng Tao1, Hailong Wu1,*, Ping Miao1, Yang Lu1, Yanyan Lu1, Qi Liu2, Li Pan2

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

    Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

  • Open Access

    PROCEEDINGS

    Mechanisms of Thermo-Mechanical Fatigue Crack Growth in a Polycrystalline Ni-Base Superalloy

    Lu Zhang1,*, Yuzhuo Wang1, Zhiwei Yu1, Rong Jiang1, Liguo Zhao1, Yingdong Song1,2

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.012701

    Abstract Thermo-mechanical fatigue (TMF), as the main failure mode of hot components of an aeroengine, are increasingly investigated recently [1,2]. TMF crack growth is studied in a nickel-based powder metallurgy (PM) superalloy subjected to in-phase (IP) and out-of-phase (OP), as well as isothermal fatigue (IF) at peak temperature. The crack growth rate and path are evaluated for both coarse grain (CG) and fine grain (FG) structure, especially the effects of phase angle and polycrystalline microstructure. The results show that the TMF crack propagation is mainly transgranular in OP condition; while in IP condition, crack propagates intergranularly… More >

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