Computers & Electrical Engineering最新文献_第3页

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

A novel Distributed Denial of Service attack defense scheme for Software-Defined Networking using Packet-In message and frequency domain analysis 利用包入信息和频域分析为软件定义网络设计的新型分布式拒绝服务攻击防御方案

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.compeleceng.2024.109827

Ramin Fadaei Fouladi , Leyli Karaçay , Utku Gülen , Elif Ustundag Soykan

引用次数: 0

A gradual approach to knowledge distillation in deep supervised hashing for large-scale image retrieval 用于大规模图像检索的深度监督哈希算法中的渐进式知识提炼方法

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.compeleceng.2024.109799

Abid Hussain , Heng-Chao li , Mehboob Hussain , Muqadar Ali , Shaheen Abbas , Danish Ali , Amir Rehman

{"title":"A gradual approach to knowledge distillation in deep supervised hashing for large-scale image retrieval","authors":"Abid Hussain , Heng-Chao li , Mehboob Hussain , Muqadar Ali , Shaheen Abbas , Danish Ali , Amir Rehman","doi":"10.1016/j.compeleceng.2024.109799","DOIUrl":"10.1016/j.compeleceng.2024.109799","url":null,"abstract":"<div><div>Deep learning-based hashing methods have emerged as superior techniques for large-scale image retrieval, surpassing non-deep and unsupervised algorithms. However, most hashing models do not consider memory usage and computational costs, which hinders their use on resource-constrained devices. This paper proposes an Optimized Knowledge Distillation (OKD) approach for training compact deep supervised hashing models to address this issue. OKD utilizes a unique growing teacher-student training strategy where an evolving teacher continuously imparts enriched knowledge to the student. The teacher and student networks are divided into blocks, with auxiliary training modules placed between corresponding blocks. These modules extract knowledge from intermediate layers to capture multifaceted relationships in data and enhance distillation. Furthermore, a noise- and background-reduction mask (NBRM) is employed to filter noise from transferred knowledge, promoting focus on discriminative features. During training, the student utilizes various sources of supervision, including dynamically improving the teacher's predictions, ground truths, and hash code matching. This assists the student in closely replicating the teacher's abilities despite using fewer parameters. Experimental evaluation on four benchmark datasets - CIFAR-10, CIFAR-100, NUS-WIDE, and ImageNet - demonstrates that OKD outperforms existing hashing methods. OKD achieves 92.98 %, 88.72 %, and 75.88 % mean average precision on CIFAR-10, NUS-WIDE, and ImageNet datasets, respectively, with up to 1.83 %, 1.69 %, and 0.80 % higher accuracy than the previous best methods, across different hash code lengths. By matching teacher ability using distilled knowledge, OKD addresses the barriers that prevent powerful models from being deployed on resource-constrained mobile/embedded platforms.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109799"},"PeriodicalIF":4.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593223","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-agent deep reinforcement learning based multiple access for underwater cognitive acoustic sensor networks 基于多代理深度强化学习的水下认知声学传感器网络多重接入

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-06 DOI: 10.1016/j.compeleceng.2024.109819

Yuzhi Zhang, Xiang Han, Ran Bai, Menglei Jia

{"title":"Multi-agent deep reinforcement learning based multiple access for underwater cognitive acoustic sensor networks","authors":"Yuzhi Zhang, Xiang Han, Ran Bai, Menglei Jia","doi":"10.1016/j.compeleceng.2024.109819","DOIUrl":"10.1016/j.compeleceng.2024.109819","url":null,"abstract":"<div><div>Considering the challenges posed by the significant propagation delays inherent in underwater cognitive acoustic sensor networks, this paper explores the application of multi-agent deep reinforcement learning for the design of multiple access protocols. We deal with the problem of sharing channels and time slots among multiple sensor nodes that adopt different time-slotted MAC protocols. The multiple intelligent nodes can independently learn the strategies for accessing available idle time slots through the proposed multi-agent deep reinforcement learning (DRL) based multiple access control (MDRL-MAC) protocol. Considering the long propagation delay associated with underwater acoustic channels, we reformulate proper state, action, and reward within the DRL framework to address the multiple access challenges and optimize network throughput. To mitigate the decision deviation stemming from partial observability, the gated recurrent unit (GRU) is integrated into DRL to enhance the deep neural network’s performance. Additionally, to ensure both the maximization of network throughput and the maintenance of fairness among multiple agents, an inspiration mechanism (IM) is proposed to inspire the lazy agent to take more actions to improve its contribution to achieve multi-agent fairness. The simulation results show that the proposed protocol facilitates the convergence of network throughput to optimal levels across various system configurations and environmental conditions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109819"},"PeriodicalIF":4.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593224","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 effective intrusion detection scheme for Distributed Network Protocol 3 (DNP3) applied in SCADA-enabled IoT applications 适用于 SCADA 物联网应用的分布式网络协议 3 (DNP3) 的有效入侵检测方案

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-05 DOI: 10.1016/j.compeleceng.2024.109828

Gagan Dangwal , Saksham Mittal , Mohammad Wazid , Jaskaran Singh , Ashok Kumar Das , Debasis Giri , Mohammed J.F. Alenazi

{"title":"An effective intrusion detection scheme for Distributed Network Protocol 3 (DNP3) applied in SCADA-enabled IoT applications","authors":"Gagan Dangwal , Saksham Mittal , Mohammad Wazid , Jaskaran Singh , Ashok Kumar Das , Debasis Giri , Mohammed J.F. Alenazi","doi":"10.1016/j.compeleceng.2024.109828","DOIUrl":"10.1016/j.compeleceng.2024.109828","url":null,"abstract":"<div><div>The widespread adoption of computers and the Internet in recent decades has led to a growing reliance on digital technologies. Supervisory Control and Data Acquisition (SCADA)-enabled Internet of Things (IoT) applications are now used in various sectors such as nuclear power plants, oil and gas extraction, and refineries. However, ensuring the security of computer networks and such autonomous systems is essential to thwart potential threats from hackers and intruders. In this article, an intrusion detection scheme is proposed by deploying different machine learning algorithms (referred to as IDM-DNP3). These algorithms are rigorously trained and tested on an extensive dataset encompassing nine Distributed Network Protocol 3 (DNP3) testbed attacks. Utilizing a range of algorithms, a multi-class classification model was successfully developed for detecting attacks related to SCADA and DNP3. The comparative study conducted shows that IDM-DNP3 can detect potential threats with higher accuracy than other existing schemes.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109828"},"PeriodicalIF":4.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587423","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

Mitigation of electric field near overhead transmission lines using electromechanical compensation based on genetic algorithm 利用基于遗传算法的机电补偿缓解架空输电线路附近的电场

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-05 DOI: 10.1016/j.compeleceng.2024.109845

Eslam Mohamed Ahmed , Khaled Hosny Ibrahim

{"title":"Mitigation of electric field near overhead transmission lines using electromechanical compensation based on genetic algorithm","authors":"Eslam Mohamed Ahmed , Khaled Hosny Ibrahim","doi":"10.1016/j.compeleceng.2024.109845","DOIUrl":"10.1016/j.compeleceng.2024.109845","url":null,"abstract":"<div><div>The electrical field is a function of both the voltage and the configuration of the overhead transmission line (OTL); thus, mitigation of the electrical field could be achieved by either electrical compensation or mechanical rearrangement of the line configuration. In the mechanical rearrangement method, the conductor positions are optimized under certain constraints so that the electrical field has the minimum possible value. In the proposed research, OTL mechanical rearrangement is improved using electrical compensation based on a genetic algorithm (GA). The electrical compensation is implemented by inserting a combination of passive series and shunt elements in each phase, creating an electric voltage imbalance. GA is an evolutionary optimization algorithm used to minimize the electric field near residences as a fitness function. The positions of conductors and passive-reactive elements are represented as genes. In addition, this paper includes a case study on a 500-kV high-voltage overhead transmission line. The results show that when passive-reactive compensation is combined with mechanical compensation, the lowest electric field can be obtained. Electrical compensation improves the mechanical rearrangement method by approximately 18.6% (the total reduction with mechanical compensation of only about 34% is increased to more than 52% with electromechanical compensation).</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109845"},"PeriodicalIF":4.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593225","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