Computers & Electrical Engineering最新文献_第6页

Optimal user selection scheme in wireless powered downlink CNOMA communication under Nakagami-m fading channel 中上衰减信道下的无线供电下行 CNOMA 通信中的最佳用户选择方案

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-13 DOI: 10.1016/j.compeleceng.2024.109839

Samparna Parida, Pawan Kumar, Santos Kumar Das

{"title":"Optimal user selection scheme in wireless powered downlink CNOMA communication under Nakagami-m fading channel","authors":"Samparna Parida, Pawan Kumar, Santos Kumar Das","doi":"10.1016/j.compeleceng.2024.109839","DOIUrl":"10.1016/j.compeleceng.2024.109839","url":null,"abstract":"<div><div>Cooperative non-orthogonal multiple access (CNOMA) stands out as a promising approach for fostering widespread user connectivity and promoting fair allocation of network resources. For a self-sustainable, low complexity, high-speed, ultra-reliable data transmission we need user cooperative relaying with an optimal and low complexity user selection scheme. This research article models a Wireless power transfer (WPT) enabled user-assisted CNOMA communication network using Nakagami-m channel and compares the outage performance of user nodes at different existing relay selection techniques. A comparative outage performance analysis of several popular user selection systems is conducted through simulation in an effort to identify the best scheme with the least amount of complexity for the proposed communication network. However, in conventional cooperative communication networks optimal performance can never be achieved with low complexity schemes. It also provides a closed-form exact expression for outage probability and throughput at both the near and far user and the energy efficiency of the proposed communication system using the optimal scheme. The expressions derived are validated through Monte Carlo simulation. The outage performance analysis is obtained at the optimal scheme varying different parameters. The performance of the WPT-assisted CNOMA is compared with that of a Simultaneous Wireless Information and Power Transfer (SWIPT) assisted CNOMA using various existing user selection schemes to find the best combination of techniques.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109839"},"PeriodicalIF":4.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved hybrid model for wind power forecasting through fusion of deep learning and adaptive online learning 通过融合深度学习和自适应在线学习改进风能预测混合模型

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-13 DOI: 10.1016/j.compeleceng.2024.109768

Xiongfeng Zhao, Hai Peng Liu, Huaiping Jin, Shan Cao, Guangmei Tang

{"title":"An improved hybrid model for wind power forecasting through fusion of deep learning and adaptive online learning","authors":"Xiongfeng Zhao, Hai Peng Liu, Huaiping Jin, Shan Cao, Guangmei Tang","doi":"10.1016/j.compeleceng.2024.109768","DOIUrl":"10.1016/j.compeleceng.2024.109768","url":null,"abstract":"<div><div>Accurate and effective wind power forecasting is crucial for wind power dispatch and wind energy development. However, existing methods often lack adaptive updating capabilities and struggle to handle real-time changing data. This paper proposes a new hybrid wind power forecasting model that integrates the Maximal Information Coefficient (MIC), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), an improved Harris Hawks Optimization (IHHO) algorithm, and an Adaptive Deep Learning model with Online Learning and Forgetting mechanisms (ADL-OLF). First, MIC is used to reconstruct input features, enhancing their correlation with the target variable, and DBSCAN is employed to handle outliers in the dataset. The ADL-OLF model enables continuous updating with new data through online learning and forgetting mechanisms. Its deep learning component consists of Bidirectional Long Short-Term Memory (BiLSTM) networks and self-attention mechanisms, which improve the prediction accuracy for sequential data. Finally, IHHO optimizes the parameters of the ADL-OLF model, achieving strong predictive performance and adaptability to real-time changing data. Experimental simulations based on actual wind power data over four seasons from a U.S. wind farm show that the proposed model achieves a coefficient of determination exceeding 0.99. Compared with 12 benchmark models (taking IHHO-ADL-OLF as an example), the Root Mean Square Error (RMSE) is reduced by more than 20%. These results indicate that the model significantly improves the accuracy and robustness of wind power forecasting, providing valuable references for the development and optimization of wind power systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109768"},"PeriodicalIF":4.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of grounding grid corrosion extent based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning 基于激光诱导击穿光谱 (LIBS) 并结合机器学习评估接地网腐蚀程度

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-09 DOI: 10.1016/j.compeleceng.2024.109849

Zhicheng Huang , Langyu Xia , Huan Zhang , Fan Liu , Yanming Tu , Zefeng Yang , Wenfu Wei

{"title":"Evaluation of grounding grid corrosion extent based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning","authors":"Zhicheng Huang , Langyu Xia , Huan Zhang , Fan Liu , Yanming Tu , Zefeng Yang , Wenfu Wei","doi":"10.1016/j.compeleceng.2024.109849","DOIUrl":"10.1016/j.compeleceng.2024.109849","url":null,"abstract":"<div><div>As one of the most widely used forms of energy, the safety and stability of power systems are crucial to modern society. Grounding grids dissipate current and reduce touch and pace voltage during lightning strikes or fault currents, ensuring the safety of personnel and equipment. However, prolonged submersion in soil causes inevitable corrosion, compromising grounding efficacy by increasing resistance and reducing current dissipation. This deterioration can result in unsafe local potential differences. This study uses Laser-Induced Breakdown Spectroscopy (LIBS) to measure corrosion degrees in grounding grids. Spectral data from samples with varying corrosion extent were collected, with outliers removed using the Local Outlier Factor (LOF) algorithm. Principal Component Analysis (PCA) reduced data dimensionality, revealing clustering in spectral data corresponding to corrosion extent. Three machine learning models were compared: Adaptive Boosting - Backpropagation Neural Network (Adaboost-BP), Support Vector Machine (SVM), and Random Forest (RF). The RF model showed the highest accuracy in predicting corrosion degree (R²=0.9845, MSE=0.0296), outperforming Adaboost-BP and SVM, especially for intermediate corrosion extent. These findings validate the effectiveness and reliability of combining LIBS with machine learning for predicting grounding grid corrosion, providing a theoretical foundation for the safe operation of power systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109849"},"PeriodicalIF":4.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive optimization strategy and evaluation of vehicle-road collaborative perception algorithm in real-time settings 车路协同感知算法的自适应优化策略和实时设置评估

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-09 DOI: 10.1016/j.compeleceng.2024.109785

Jiaxi Liu, Bolin Gao, Wei Zhong, Yanbo Lu, Shuo Han

{"title":"Adaptive optimization strategy and evaluation of vehicle-road collaborative perception algorithm in real-time settings","authors":"Jiaxi Liu, Bolin Gao, Wei Zhong, Yanbo Lu, Shuo Han","doi":"10.1016/j.compeleceng.2024.109785","DOIUrl":"10.1016/j.compeleceng.2024.109785","url":null,"abstract":"<div><div>The Intelligent and Connected Vehicle Cloud Control System is a critical approach for achieving high-level autonomous driving. One of the key challenges at the perception level is utilizing multi-source sensory data to create a real-time digital twin of the transportation system. Collaborative perception technology plays a pivotal role in addressing this challenge. However, most prior research has been conducted offline, where the focus has primarily been on comparing ground truth at the sensing timestamp with the algorithm’s predicted perception values. This approach tends to prioritize computational accuracy, neglecting the fact that the physical world continues to evolve during the processing time, which can result in an accuracy drop. As a result, there is a growing consensus that both latency and accuracy must be considered simultaneously for real-time applications, such as digital twins and beyond. To address this gap, we first analyze the comprehensive time delay problem in vehicle-road collaborative perception algorithms and formally define the real-time perception problem within this context. Next, we propose an adaptive optimization strategy for vehicle-road collaborative perception, which accounts for the complexity of the perception environment and the vehicle-road communication pipeline. Our approach dynamically selects the optimal model parameter set based on the perception scenario and real-time communication conditions. Experimental results demonstrate that our strategy enhances real-time performance by 5.8% compared to the best global single-model algorithm and by up to 27.5% compared to the conservative fixed single-model approach.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109785"},"PeriodicalIF":4.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Penetration and control of grid-forming (GFM) inverter in LFC of an enhanced IEEE 9-Bus interconnected power system 增强型 IEEE 9 总线互联电力系统 LFC 中电网成形 (GFM) 逆变器的渗透与控制

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-09 DOI: 10.1016/j.compeleceng.2024.109837

Suriya Sharif, Asadur Rahman

{"title":"Penetration and control of grid-forming (GFM) inverter in LFC of an enhanced IEEE 9-Bus interconnected power system","authors":"Suriya Sharif, Asadur Rahman","doi":"10.1016/j.compeleceng.2024.109837","DOIUrl":"10.1016/j.compeleceng.2024.109837","url":null,"abstract":"<div><div>Load-frequency-control (LFC) is employed for frequency stability and balanced power flow among control-areas. Inverters are becoming critical assets in modern power networks with increasing renewable energy. As these inverter-based resources prevail, the system inertia decreases, leading to potential frequency instability problems. Grid-forming (GFM) inverter development and applications are gaining significant attraction because of their ability to maintain quality power-grid operations. GFM inverter, acting as a voltage-source-converter, adjusts its output frequency by contributing a portion of the load change to reduce the frequency deviations. The virtual synchronous generator (VSG) control mechanism for GFM is implemented in this work. A two-area interconnected power system model emulating an enhanced IEEE 9-Bus system is developed and simulated in MATLAB-Simulink® for analysis. Secondary controllers are applied in each LFC and GFM-loop of the proposed LFC-GFM system with a magnetotactic-bacteria-optimization (MBO) algorithm for simultaneously tuning these parameters. The proposed control strategy’s effectiveness is verified by comparing simulation results with the basic LFC system under varying inverter penetration levels. The simulated dynamic responses verify the efficacy of the controlled penetration of the GFM inverter in the proposed LFC-GFM system with enhanced damping characteristics, improving small-signal stability and reducing settling time by 43.46% and frequency deviation by 55.5%.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109837"},"PeriodicalIF":4.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint detection and localization of False Data Injection Attacks in smart grids: An enhanced state estimation approach 智能电网中虚假数据注入攻击的联合检测与定位：增强型状态估计方法

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-09 DOI: 10.1016/j.compeleceng.2024.109834

Guoqing Zhang, Wengen Gao, Yunfei Li, Yixuan Liu, Xinxin Guo, Wenlong Jiang

{"title":"Joint detection and localization of False Data Injection Attacks in smart grids: An enhanced state estimation approach","authors":"Guoqing Zhang, Wengen Gao, Yunfei Li, Yixuan Liu, Xinxin Guo, Wenlong Jiang","doi":"10.1016/j.compeleceng.2024.109834","DOIUrl":"10.1016/j.compeleceng.2024.109834","url":null,"abstract":"<div><div>The transition to smart grids introduces significant cybersecurity vulnerabilities, particularly with the rise of False Data Injection Attacks (FDIAs). These attacks allow malicious actors to manipulate sensor data, alter the internal state of the grid, and bypass traditional Bad Data Detection (BDD) systems. FDIAs pose a serious threat to grid security, potentially leading to incorrect state estimation and destabilization of the power system, which could result in system outages and economic losses. To address this challenge, this paper proposes a novel detection and localization method. First, false data and measurement errors are modeled as non-Gaussian noise. Recognizing the limitations of the traditional Extended Kalman Filter (EKF) under non-Gaussian conditions, the Maximum Correntropy Criterion (MCC) is integrated into the EKF to improve the robustness of state estimation. Additionally, the Maximum Correntropy Criterion Extended Kalman Filter (MCCEKF) is combined with Weighted Least Squares (WLS), and cosine similarity is introduced to quantify the differences between these two estimators for FDIA detection. A partition approach is then used to construct a logical localization matrix, with cosine similarity detection applied in each section to generate a detection matrix. By performing a logical AND operation on these matrices, the attacked bus is identified. Simulations on IEEE-14-bus and IEEE-30-bus systems validate the proposed approach, demonstrating its effectiveness in reliably detecting and localizing FDIAs in smart grids.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109834"},"PeriodicalIF":4.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimized multilayer perceptron-based network intrusion detection using Gray Wolf Optimization 基于多层感知器的网络入侵检测灰狼优化法

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-08 DOI: 10.1016/j.compeleceng.2024.109838

Asad Ali , Muhammad Assam , Faheem Ullah Khan , Yazeed Yasin Ghadi , Zhumazhan Nurdaulet , Alibiyeva Zhibek , Syed Yaqub Shah , Tahani Jaser Alahmadi

{"title":"An optimized multilayer perceptron-based network intrusion detection using Gray Wolf Optimization","authors":"Asad Ali , Muhammad Assam , Faheem Ullah Khan , Yazeed Yasin Ghadi , Zhumazhan Nurdaulet , Alibiyeva Zhibek , Syed Yaqub Shah , Tahani Jaser Alahmadi","doi":"10.1016/j.compeleceng.2024.109838","DOIUrl":"10.1016/j.compeleceng.2024.109838","url":null,"abstract":"<div><div>The exponential growth in the use of network services through the design of various network infrastructures, has led to increased complexities and challenges in the network. A major problem in computer networks is privacy and security breach. Cyber attackers exploit loopholes to infiltrate and disrupt the operation of the network through various attacks. Anomaly-based intrusion detection often employs Artificial Neural Network techniques like Multi-layer Perceptron (MLP) to classify malicious and legitimate traffic. Nevertheless, these techniques are vulnerable to overfitting and require extensive labeled data and computational resources. Consequently, this reduces the accuracy of intrusion detection systems and increases the error detection rate. To minimize the error detection rate of the intrusion detection system, it is necessary to optimize the connection parameters of the MLP neural network such as weights and biases. To this end, we proposed an optimized MLP-based Intrusion Detection using Gray Wolf Optimization (GWOMLP-IDS) to optimize the learning process of the MLP neural network by optimizing weights and biases. GWO aims to select an optimal connection parameter during the learning process to minimize the error rate of intrusion detection. Extensive simulations in Python reveal the effectiveness of the proposed approach in terms of designated performance metrics.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109838"},"PeriodicalIF":4.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization and uncertainty analysis of hybrid energy systems using Monte Carlo simulation integrated with genetic algorithm 利用蒙特卡罗模拟与遗传算法相结合，对混合能源系统进行优化和不确定性分析

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-07 DOI: 10.1016/j.compeleceng.2024.109833

Hassan M. Hussein Farh , Abdullrahman A. Al-Shamma'a , Fahad Alaql , Hammed Olabisi Omotoso , Walied Alfraidi , Mohamed A. Mohamed

{"title":"Optimization and uncertainty analysis of hybrid energy systems using Monte Carlo simulation integrated with genetic algorithm","authors":"Hassan M. Hussein Farh , Abdullrahman A. Al-Shamma'a , Fahad Alaql , Hammed Olabisi Omotoso , Walied Alfraidi , Mohamed A. Mohamed","doi":"10.1016/j.compeleceng.2024.109833","DOIUrl":"10.1016/j.compeleceng.2024.109833","url":null,"abstract":"<div><div>This study investigates the optimization of hybrid energy systems (HES) composed of wind turbines, battery banks, and diesel generators, focusing on addressing the challenges posed by wind speed uncertainty. This research contributes significantly to the field by developing a novel methodology that combines uncertainty analysis with hybrid optimization techniques to improve the reliability and cost-effectiveness of HES. The findings revealed that initial simulations without renewable energy sources result in high diesel consumption, with fuel usage reaching 534,810 liters per year and associated carbon emissions totaling 797,070 kg/year. Through optimization, an economically viable configuration is identified, consisting of 37 battery banks, two 250 kW wind turbines, and a 340-kW diesel generator, achieving an Annualized System Cost (ASC) of $166,500 and a Cost of Energy (COE) of $0.1480/kWh. The Monte Carlo simulations indicate a most probable COE of $0.1450/kWh for the wind turbine/battery/diesel system, occurring with an 8.3 % probability, while approximately 90 % of COE values fall below $0.1669/kWh. The average COE is $0.14834/kWh, with a minimum of $0.12163/kWh. The Renewable Energy Fraction (REF) spans from 28 % to 97 %, with an average of 64 % and a standard deviation error of 9.6 % at a 95 % confidence level. The results underscore the potential implications for informing policymakers and industry leaders about the design and evaluation of HES under uncertain environmental conditions. By addressing the limitations of current approaches, this work contributes valuable insights into the economic, environmental, and social dimensions of hybrid renewable energy systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109833"},"PeriodicalIF":4.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization and energy management strategies, challenges, advances, and prospects in electric vehicles and their charging infrastructures: A comprehensive review 电动汽车及其充电基础设施的优化和能源管理战略、挑战、进展和前景：全面回顾

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-07 DOI: 10.1016/j.compeleceng.2024.109842

Jamiu Oladigbolu , Asad Mujeeb , Li Li

{"title":"Optimization and energy management strategies, challenges, advances, and prospects in electric vehicles and their charging infrastructures: A comprehensive review","authors":"Jamiu Oladigbolu , Asad Mujeeb , Li Li","doi":"10.1016/j.compeleceng.2024.109842","DOIUrl":"10.1016/j.compeleceng.2024.109842","url":null,"abstract":"<div><div>Electric vehicles (EVs) are at the forefront of global efforts to reduce greenhouse gas emissions and transition to sustainable energy systems. This review comprehensively examines the optimization and energy management strategies for EVs and their charging infrastructure, focusing on technological advancements, persistent challenges, and future prospects. By the end of 2023, the number of electric cars on the road globally reached 40 million, with 14 million new registrations recorded in 2023 alone—95% of which were in China, Europe, and the United States. Governments across the globe have introduced incentives and policies to promote EV adoption, and by 2030, EVs are expected to comprise a significant portion of light-duty vehicles in major regions. Despite these encouraging developments, challenges such as range anxiety, the relatively low energy density of 200–300 Wh/kg in Li-ion batteries (compared to 13,000 Wh/kg for petroleum), and insufficient public charging infrastructure remain key barriers to widespread EV adoption. This review also explores the critical role of smart grid technologies, vehicle-to-grid (V2G) systems, and renewable energy integration in supporting the growing EV market. V2G technologies are projected to enhance grid stability by 20–30% and reduce operational costs by 10–15% through load balancing and real-time energy price forecasting. By thoroughly analyzing optimization techniques such as load balancing, dynamic scheduling, and real-time energy management, this paper offers a roadmap for researchers, policymakers, and industry stakeholders to accelerate the integration of EVs into global energy systems and enhance sustainability in urban transportation networks.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109842"},"PeriodicalIF":4.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-timescale modeling and order reduction towards stability analysis of isolated microgrids 面向孤立微电网稳定性分析的多时标建模和阶次缩减

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-07 DOI: 10.1016/j.compeleceng.2024.109835

Chaofeng Yan , Yang Han , Ensheng Zhao , Yuxiang Liu , Ping Yang , Congling Wang , Amr S. Zalhaf

{"title":"Multi-timescale modeling and order reduction towards stability analysis of isolated microgrids","authors":"Chaofeng Yan , Yang Han , Ensheng Zhao , Yuxiang Liu , Ping Yang , Congling Wang , Amr S. Zalhaf","doi":"10.1016/j.compeleceng.2024.109835","DOIUrl":"10.1016/j.compeleceng.2024.109835","url":null,"abstract":"<div><div>Microgrids incorporate a significant proportion of renewable energy sources and power electronic converters in the energy conversion process, creating a sustainable and clean energy infrastructure. However, the multi-timescale dynamics of microgrids are interactively coupled under a nonlinear structure, which makes it difficult to gain insight into the instability mechanisms without a high-fidelity reduced-order model that preserves the main dynamic behaviors of the system. For the isolated AC microgrid dominated by voltage source inverters (VSI), a detailed state-space model of the system, including the inverter, network, and load, is first developed. Based on this model, the eigenvalue analysis is carried out, and a participation factor analysis tool is also utilized to identify the relevant dynamics that have a strong impact on the system's dominant mode. Furthermore, to simplify the system modeling process without losing essential dynamic interactions, a novel multi-timescale coupled reduced-order model is proposed using a transfer function-based order reduction method, which retains the open-loop gain characteristics to preserve the critical couplings between fast inner loop dynamics and slow droop control dynamics. Finally, the accuracy of the reduced-order model is verified by comparing it with the detailed model and the conventional singular perturbation reduced-order model through eigenvalue distribution and time-domain simulation analysis.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109835"},"PeriodicalIF":4.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0