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 >

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 >

Aijia Ding, Tingzhang Liu^*

Energy Engineering, Vol.121, No.12, pp. 3735-3759, 2024, DOI:10.32604/ee.2024.054687 - 22 November 2024

Abstract The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources. This paper introduces novel dual-backup controllers utilizing advanced fractional order proportional integral derivative (FOPID) controllers to enhance frequency and tie-line power stability in microgrids amid increasing renewable energy integration. To improve load frequency control, the proposed controllers are applied to a two-area interconnected microgrid system incorporating diverse energy sources, such as wind turbines, photovoltaic cells, diesel generators, and various storage technologies. A novel meta-heuristic algorithm is adopted to select the optimal parameters of the proposed controllers. The efficacy… More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Tao Yan¹, Javed Rashid^2,3, Muhammad Shoaib Saleem^3,4, Sajjad Ahmad⁴, Muhammad Faheem^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024

Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

Trong Thua Huynh^*, Hoang Thanh Nguyen, Du Thang Phu

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2281-2298, 2024, DOI:10.32604/cmc.2024.057954 - 18 November 2024

Abstract In recent years, early detection and warning of fires have posed a significant challenge to environmental protection and human safety. Deep learning models such as Faster R-CNN (Faster Region based Convolutional Neural Network), YOLO (You Only Look Once), and their variants have demonstrated superiority in quickly detecting objects from images and videos, creating new opportunities to enhance automatic and efficient fire detection. The YOLO model, especially newer versions like YOLOv10, stands out for its fast processing capability, making it suitable for low-latency applications. However, when applied to real-world datasets, the accuracy of fire prediction is… More >

Mitra Madanchian^1,*, Hamed Taherdoost^1,2,3,4

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 >

Ting Chen¹, Shuna Jiang², Xin Fan^3,*, Jianchuan Xia², Xiujuan Zhang², Chuanwen Luo³, Yi Hong³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2195-2217, 2024, DOI:10.32604/cmc.2024.056960 - 18 November 2024

Abstract In blockchain-based unmanned aerial vehicle (UAV) communication systems, the length of a block affects the performance of the blockchain. The transmission performance of blocks in the form of finite character segments is also affected by the block length. Therefore, it is crucial to balance the transmission performance and blockchain performance of blockchain communication systems, especially in wireless environments involving UAVs. This paper investigates a secure transmission scheme for blocks in blockchain-based UAV communication systems to prevent the information contained in blocks from being completely eavesdropped during transmission. In our scheme, using a friendly jamming UAV… 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 >

Zongdong Du^1,2, Xuefeng Feng³, Feng Li³, Qinglong Xian³, Zhenhong Jia^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2607-2627, 2024, DOI:10.32604/cmc.2024.056616 - 18 November 2024

Abstract The importance of unmanned aerial vehicle (UAV) obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance, thereby protecting people and property. We propose UAD-YOLOv8, a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance. The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2 (DCNv2) to optimize the cross stage partial bottleneck with 2 convolutions and fusion (C2f) module. Additionally, it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable… More >