Yingnan Zhao^*, Yuyuan Ruan, Zhen Peng

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 549-566, 2024, DOI:10.32604/cmc.2024.056240 - 15 October 2024

Abstract As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimization Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose some NWP features with strong correlation to wind power to form the feature set. Then, it decomposes the feature set More >

Zeshuang Song¹, Xiao Wang^1,*, Qing Wu¹, Yanting Tao¹, Linghua Xu¹, Yaohua Yin², Jianguo Yan³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 985-1008, 2024, DOI:10.32604/cmc.2024.055614 - 15 October 2024

Abstract This research is the first application of Unmanned Aerial Vehicles (UAVs) equipped with Multi-access Edge Computing (MEC) servers to offshore wind farms, providing a new task offloading solution to address the challenge of scarce edge servers in offshore wind farms. The proposed strategy is to offload the computational tasks in this scenario to other MEC servers and compute them proportionally, which effectively reduces the computational pressure on local MEC servers when wind turbine data are abnormal. Finally, the task offloading problem is modeled as a multi-intelligent deep reinforcement learning problem, and a task offloading model… More >

Husam AlShannaq, Aly Mousaad Aly^*

Structural Durability & Health Monitoring, Vol.18, No.6, pp. 707-737, 2024, DOI:10.32604/sdhm.2024.054731 - 20 September 2024

Abstract Wind turbines have emerged as a prominent renewable energy source globally. Efficient monitoring and detection methods are crucial to enhance their operational effectiveness, particularly in identifying fatigue-related issues. This review focuses on leveraging artificial neural networks (ANNs) for wind turbine monitoring and fatigue detection, aiming to provide a valuable reference for researchers in this domain and related areas. Employing various ANN techniques, including General Regression Neural Network (GRNN), Support Vector Machine (SVM), Cuckoo Search Neural Network (CSNN), Backpropagation Neural Network (BPNN), Particle Swarm Optimization Artificial Neural Network (PSO-ANN), Convolutional Neural Network (CNN), and nonlinear autoregressive… More >

Juan Li¹, Tingting Xu^1,*, Yi Gu², Chuang Liu¹, Guiping Zhou², Guoliang Bian¹

Energy Engineering, Vol.121, No.10, pp. 2777-2795, 2024, DOI:10.32604/ee.2024.052331 - 11 September 2024

Abstract In fossil energy pollution is serious and the “double carbon” goal is being promoted, as a symbol of fresh energy in the electrical system, solar and wind power have an increasing installed capacity, only conventional units obviously can not solve the new energy as the main body of the scheduling problem. To enhance the system scheduling ability, based on the participation of thermal power units, incorporate the high energy-carrying load of electro-melting magnesium into the regulation object, and consider the effects on the wind unpredictability of the power. Firstly, the operating characteristics of high energy… More >

Zhan Wang¹, Liang Li^2,*, Long Wang¹, Weidong Zhu³, Yinghui Li⁴, Echuan Yang⁵

Energy Engineering, Vol.121, No.10, pp. 2981-3000, 2024, DOI:10.32604/ee.2024.048161 - 11 September 2024

Abstract A dynamic pitch strategy is usually adopted to improve the aerodynamic performance of the blade of a wind turbine. The dynamic pitch motion will affect the linear vibration characteristics of the blade. However, these influences have not been studied in previous research. In this paper, the influences of the rigid pitch motion on the linear vibration characteristics of a wind turbine blade are studied. The blade is described as a rotating cantilever beam with an inherent coupled rigid-flexible vibration, where the rigid pitch motion introduces a parametrically excited vibration to the beam. Partial differential equations More > Graphic Abstract

Caifeng Wen^1,2, Boxin Zhang^1,*, Yuanjun Dai³, Wenxin Wang⁴, Wanbing Xie¹, Qian Du¹

Energy Engineering, Vol.121, No.10, pp. 2961-2979, 2024, DOI:10.32604/ee.2024.041677 - 11 September 2024

Abstract Power quality improvements help guide and solve the problems of inefficient energy production and unstable power output in wind power systems. The purpose of this paper is mainly to explore the influence of different energy storage batteries on various power quality indicators by adding different energy storage devices to the simulated wind power system, and to explore the correlation between system entropy generation and various indicators, so as to provide a theoretical basis for directly improving power quality by reducing loss. A steady-state experiment was performed by replacing the wind wheel with an electric motor,… More >

Amal El Berry, Marwa M. Ibrahim^*, A. A. Elfeky, Mohamed F. Nasr

Frontiers in Heat and Mass Transfer, Vol.22, No.4, pp. 1189-1207, 2024, DOI:10.32604/fhmt.2024.050684 - 30 August 2024

Abstract A photovoltaic (PV) inverter is a vital component of a photovoltaic (PV) solar system. Photovoltaic (PV) inverter failure can mean a solar system that is no longer functioning. When electronic devices such as photovoltaic (PV) inverter devices are subjected to vapor condensation, a risk could occur. Given the amount of moisture in the air, saturation occurs when the temperature drops to the dew point, and condensation may form on surfaces. Numerical simulation with “COMSOL Software” is important for obtaining knowledge relevant to preventing condensation by using two steps. At first, the assumption was that the… More >

Xin Guan^*, Yuqi Xie, Shuaijie Wang, Mingyang Li, Shiwei Wu

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 2045-2058, 2024, DOI:10.32604/fdmp.2024.049671 - 23 August 2024

Abstract The effects of the erosion present on the leading edge of a wind turbine airfoil (DU 96-W-180) on its aerodynamic performances have been investigated numerically in the framework of a SST k–ω turbulence model based on the Reynolds Averaged Navier-Stokes equations (RANS). The results indicate that when sand-induced holes and small pits are involved as leading edge wear features, they have a minimal influence on the lift and drag coefficients of the airfoil. However, if delamination occurs in the same airfoil region, it significantly impacts the lift and resistance characteristics of the airfoil. Specifically, as More >

Xin Guan^1,2,*, Mingyang Li¹, Shiwei Wu¹, Yuqi Xie¹, Yongpeng Sun¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.9, pp. 2029-2043, 2024, DOI:10.32604/fdmp.2024.049499 - 23 August 2024

Abstract The focus of this research was on the equivalent particle roughness height correction required to account for the presence of ice when determining the performances of wind turbines. In particular, two icing processes (frost ice and clear ice) were examined by combining the FENSAP-ICE and FLUENT analysis tools. The ice type on the blade surfaces was predicted by using a multi-time step method. Accordingly, the influence of variations in icing shape and ice surface roughness on the aerodynamic performance of blades during frost ice formation or clear ice formation was investigated. The results indicate that More >

Islam Zada^1,*, Mohammed Naif Alatawi², Syed Muhammad Saqlain¹, Abdullah Alshahrani³, Adel Alshamran⁴, Kanwal Imran⁵, Hessa Alfraihi⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835 - 15 August 2024

Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >