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  • Open Access

    ARTICLE

    Research on Asymmetric Fault Location of Wind Farm Collection System Based on Compressed Sensing

    Huanan Yu1, Gang Han1,*, Hansong Luo2, He Wang1

    Energy Engineering, Vol.120, No.9, pp. 2029-2057, 2023, DOI:10.32604/ee.2023.028365

    Abstract Aiming at the problem that most of the cables in the power collection system of offshore wind farms are buried deep in the seabed, which makes it difficult to detect faults, this paper proposes a two-step fault location method based on compressed sensing and ranging equation. The first step is to determine the fault zone through compressed sensing, and improve the data measurement, dictionary design and algorithm reconstruction: Firstly, the phase-locked loop trigonometric function method is used to suppress the spike phenomenon when extracting the fault voltage, so that the extracted voltage value will not have a large error due… More >

  • Open Access

    ARTICLE

    Enhanced Electric Power Adaptability Using Hybrid Pumped-Hydro Technology with Wind and Photovoltaic Integration

    Uwem O. Ikitde1, Abayomi A. Adebiyi1,*, Innocent E. Davidson2, Ayodeji S. Akinyemi1

    Energy Engineering, Vol.120, No.9, pp. 1939-1961, 2023, DOI:10.32604/ee.2023.027574

    Abstract The integration of solar and wind energy into the electrical grid has received global research attention due to their unpredictable characteristics. Because wind energy varies across all timescales of utility activity, renewable energy generation should be supplemented and enhanced, from real-time, minute-to-minute variations to annual alterations influencing long-term strategy. Wind energy generation does not only fluctuate but is also challenging to accurately forecast the timeframes of significance to electricity decision makers; day-ahead and long-term making plans of framework sufficiency such as meeting the network peak load annually. A utility that integrates wind and solar energy into its electricity mix would… More >

  • Open Access

    ARTICLE

    Weak Fault Detection of Rotor Winding Inter-Turn Short Circuit in Excitation System Based on Residual Interval Observer

    Gang Liu1, Xinqi Chen2,3,*, Lijuan Bao1, Linbo Xu2,3, Chaochao Dai1, Lei Yang2,3, Chengmin Wang4

    Structural Durability & Health Monitoring, Vol.17, No.4, pp. 337-351, 2023, DOI:10.32604/sdhm.2022.023583

    Abstract Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system, an interval observer based on current residual is designed. Firstly, the mechanism of the inter-turn short circuit of the rotor winding in the excitation system is modeled under the premise of stable working conditions, and electromagnetic decoupling and system simplification are carried out through Park Transform. An interval observer is designed based on the current residual in the two-phase coordinate system, and the sensitive and stable conditions of the observer is preset. The fault… More >

  • Open Access

    ARTICLE

    Towards Generating a Practical SUNBURST Attack Dataset for Network Attack Detection

    Ehab AlMasri1, Mouhammd Alkasassbeh1, Amjad Aldweesh2,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2643-2669, 2023, DOI:10.32604/csse.2023.040626

    Abstract Supply chain attacks, exemplified by the SUNBURST attack utilizing SolarWinds Orion updates, pose a growing cybersecurity threat to entities worldwide. However, the need for suitable datasets for detecting and anticipating SUNBURST attacks is a significant challenge. We present a novel dataset collected using a unique network traffic data collection methodology to address this gap. Our study aims to enhance intrusion detection and prevention systems by understanding SUNBURST attack features. We construct realistic attack scenarios by combining relevant data and attack indicators. The dataset is validated with the J48 machine learning algorithm, achieving an average F-Measure of 87.7%. Our significant contribution… More >

  • Open Access

    ARTICLE

    Optimized Design of H-Type Vertical Axis Wind Airfoil at Multiple Angles of Attack

    Chunyan Zhang1, Shuaishuai Wang1,2, Yinhu Qiao1,*, Zhiqiang Zhang1,2

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2661-2679, 2023, DOI:10.32604/fdmp.2023.028059

    Abstract Numerical simulations are conducted to improve the energy acquisition efficiency of H-type vertical axis wind turbines through the optimization of the related blade airfoil aerodynamic performance. The Bézier curve is initially used to fit the curve profile of a NACA2412 airfoil, and the moving asymptote algorithm is then exploited to optimize the design of the considered H-type vertical-axis wind-turbine blade airfoil for a certain attack angle. The results show that the maximum lift coefficient of the optimized airfoil is 8.33% higher than that of the original airfoil. The maximum lift-to-drag ratio of the optimized airfoil exceeds the maximum lift-to-drag ratio… More > Graphic Abstract

    Optimized Design of H-Type Vertical Axis Wind Airfoil at Multiple Angles of Attack

  • Open Access

    ARTICLE

    Short-Term Wind Power Prediction Based on Combinatorial Neural Networks

    Tusongjiang Kari1, Sun Guoliang2, Lei Kesong1, Ma Xiaojing1,*, Wu Xian1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012

    Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used to deeply mine the wind… More >

  • Open Access

    ARTICLE

    Optimizing Decision-Making of A Smart Prosumer Microgrid Using Simulation

    Oussama Accouche1,*, Rajan Kumar Gangadhari2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 151-173, 2023, DOI:10.32604/cmc.2023.038648

    Abstract Distributed renewable energy sources offer significant alternatives for Qatar and the Arab Gulf region’s future fuel supply and demand. Microgrids are essential for providing dependable power in difficult-to-reach areas while incorporating significant amounts of renewable energy sources. In energy-efficient data centers, distributed generation can be used to meet the facility’s overall power needs. This study primarily focuses on the best energy management practices for a smart microgrid in Qatar while taking demand-side load management into account. This article looked into a university microgrid in Qatar that primarily aimed to get all of its energy from the grid. While diesel generators… More >

  • Open Access

    ARTICLE

    Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

    Aiping Xu1,2, Xuan Zou3, Chao Wang2,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1043-1059, 2023, DOI:10.32604/csse.2023.039645

    Abstract To protect the environment, the discharged sewage’s quality must meet the state’s discharge standards. There are many water quality indicators, and the pH (Potential of Hydrogen) value is one of them. The natural water’s pH value is 6.0–8.5. The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard. This paper aims to study the deep learning prediction model of wastewater’s pH. Firstly, the research uses the random forest method to select the data features and then, based on the sliding window, convert the data set into… More >

  • Open Access

    ARTICLE

    Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

    Musaed Alrashidi*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 491-513, 2023, DOI:10.32604/csse.2023.038628

    Abstract Statistical distributions are used to model wind speed, and the two-parameters Weibull distribution has proven its effectiveness at characterizing wind speed. Accurate estimation of Weibull parameters, the scale (c) and shape (k), is crucial in describing the actual wind speed data and evaluating the wind energy potential. Therefore, this study compares the most common conventional numerical (CN) estimation methods and the recent intelligent optimization algorithms (IOA) to show how precise estimation of c and k affects the wind energy resource assessments. In addition, this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi… More >

  • Open Access

    ARTICLE

    Distributed Robust Optimal Dispatch for the Microgrid Considering Output Correlation between Wind and Photovoltaic

    Ming Li1,*, Cairen Furifu1, Chengyang Ge2, Yunping Zheng1, Shunfu Lin2, Ronghui Liu2

    Energy Engineering, Vol.120, No.8, pp. 1775-1801, 2023, DOI:10.32604/ee.2023.027215

    Abstract As an effective carrier of integrated clean energy, the microgrid has attracted wide attention. The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids. This paper proposes an optimization scheme based on the distributionally robust optimization (DRO) model for a microgrid considering solar-wind correlation. Firstly, scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function; then the generated scenario results are reduced by K-means clustering; finally, the probability confidence interval of scenario distribution is constrained… More >

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