Weiru Wang¹, Yulong Cao^1,*, 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 >

Xiaohui Ye^1,*, Fucheng Tan¹, Xinli Song², Hanyang Dai², Xia Li², Shixia Mu², Shaohang Hao²

Energy Engineering, Vol.121, No.12, pp. 3689-3710, 2024, DOI:10.32604/ee.2024.055200 - 22 November 2024

Abstract Energy storage batteries can smooth the volatility of renewable energy sources. The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system (BESS). However, the current modeling of grid-connected BESS is overly simplistic, typically only considering state of charge (SOC) and power constraints. Detailed lithium (Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions. Additionally, there is a lack of real-time batteries risk assessment frameworks. To address these issues, in this… More >

Yameng Yin¹, Lieping Zhang^2,*, Xiaoxu Shi¹, Yilin Wang³, Jiansheng Peng⁴, Jianchu Zou⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2769-2790, 2024, DOI:10.32604/cmc.2024.056791 - 18 November 2024

Abstract By integrating deep neural networks with reinforcement learning, the Double Deep Q Network (DDQN) algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots. However, the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data. Targeting those problems, an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed. First, to enhance the precision of the target Q-value, the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value… More >

Yuan Li^1,3,*, Zhiqiang Fan^1,2, Tao Suo^1,3

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.013336

Abstract Light armored vehicles, as the primary means of force transport on contemporary battlefields, require not only high mobility but also better protection to meet the complex battlefield environment and mission requirements. Composite armor is widely used in the design of light armored vehicles due to its lightweight and excellent defensible performance. In this paper, the damage law of the composite armor of an infantry fighting vehicle, when penetrated by fragment-simulated projectiles (FSP), is studied by numerical simulation, and the homogeneous equivalent targets surrogating a combination of local protective armor and vulnerable parts are constructed based More >

Tatyana Lyubimova^1,2,*, Yulia Garicheva², Andrey Ivantsov¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2417-2429, 2024, DOI:10.32604/fdmp.2024.052391 - 28 October 2024

Abstract External vibrations are known to be one of the promising ways to control the behavior of multiphase systems. The computational modeling of the behavior of a gas bubble in a viscous liquid in a horizontal cylinder of square cross-section, which undergoes linearly polarized translational oscillations in weightless conditions, has been carried out. Under vibrations, the bubble moves towards the wall of the vessel with acceleration determined by the amplitudes and frequency of vibrations. Near the wall, at a distance of the order of the thickness of the viscous Stokes boundary layer, the effects of viscosity More >

Alexei Vjatkin^*, Svyatoslav Petukhov, Victor Kozlov

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2475-2488, 2024, DOI:10.32604/fdmp.2024.052377 - 28 October 2024

Abstract A genuine technological issue–the thermal convection of liquid in a rotating cavity–is investigated experimentally. The experiments are conducted within a horizontal annulus with isothermal boundaries. The inner boundary of the annulus has a higher temperature, thus exerting a stabilising influence on the system. It is shown that when the layer rotation velocity diminishes, two-dimensional azimuthally periodic convective rolls, rotating together with the cavity, emerge in a threshold manner. The development of convection is accompanied by a significant intensification of heat transfer through the layer. It is shown that the averaged thermal convection excitation in the… More > Graphic Abstract

Jingjing Bai¹, Yunpeng Cheng¹, Shenyun Yao^2,*, Fan Wu¹, Cheng Chen¹

Energy Engineering, Vol.121, No.11, pp. 3381-3400, 2024, DOI:10.32604/ee.2024.053683 - 21 October 2024

Abstract To mitigate the impact of wind power volatility on power system scheduling, this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy. And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit. The strategy takes smoothing power output as the main objectives. The first level is the wind-storage joint scheduling, and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster (WPC), respectively, according to the scheduling power of WPC and… More >

Gang Wang^*, Yuedong Wu, Xiaoyi Qian, Yi Zhao

Energy Engineering, Vol.121, No.11, pp. 3417-3435, 2024, DOI:10.32604/ee.2024.052268 - 21 October 2024

Abstract A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems. A hybrid carbon trading mechanism that combines short-term and long-term carbon trading is constructed, and a fuzzy set based on Wasserstein measurement is proposed to address the uncertainty of wind power access. Moreover, a robust scheduling optimization method for wind–fire storage systems is formed. Results of the multi scenario comparative analysis of practical cases show that the More >

Hind Baageel¹, Md Mahfuzur Rahman^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1215-1233, 2024, DOI:10.32604/cmc.2024.055908 - 15 October 2024

Abstract The advent of blockchain technology has transformed traditional methods of information exchange, shifting reliance from centralized data centers to decentralized frameworks. While blockchain’s decentralization and security are strengths, traditional consensus mechanisms like Proof of Work (PoW) and Proof of Stake (PoS) face limitations in scalability. PoW achieves decentralization and security but struggles with scalability as transaction volumes grow, while PoS enhances scalability, but risks centralization due to monopolization by high-stake participants. Sharding, a recent advancement in blockchain technology, addresses scalability by partitioning the network into shards that process transactions independently, thereby improving throughput and reducing… More >

Mahesh Thyluru Ramakrishna¹, Kuppusamy Pothanaicker², Padma Selvaraj³, Surbhi Bhatia Khan^4,7,*, Vinoth Kumar Venkatesan⁵, Saeed Alzahrani⁶, Mohammad Alojail⁶

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 867-883, 2024, DOI:10.32604/cmc.2024.053563 - 15 October 2024

Abstract Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pituitary tumors, as well as confirming the absence of tumors, poses a significant challenge using MRI images. Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification. These methods often rely on manual feature extraction and basic convolutional neural networks (CNNs). The limitations include inadequate accuracy, poor generalization of new data, and limited ability… More >