检索结果-内蒙古大学图书馆

Improvement of active distribution systems with high penetration capacities of shunt reactive compensators and distributed generators using bald eagle search

引用

AIN SHAMS ENGINEERING JOURNAL 2022年第6期13卷

作者： Eid, Ahmad Kamel, Salah Zawbaa, Hossam M. Dardeer, Mostafa Aswan Univ Dept Elect Engn Aswan 81542 Egypt Qassim Univ Dept Elect Engn Unaizah 56452 Saudi Arabia Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Technol Univ Dublin Pk House191 N Circular Rd Dublin D07 EWV4 Ireland

This work proposes an intelligent allocation of distributed generation (DG) units and shunt reactive com-pensators (SRC) with high penetration capacities into distribution systems for power loss mitigation using the bald eagle search (BES) optimization algorithm. The intelligent allocation causes a reduction in voltage variations and enhances the voltage stability of the systems. The SRC units include shunt capacitors (SC), Static Var Compensators (SVC), and Distribution Static Compensators (DSTATCOM), which are determined according to their capacities. The optimization study includes the 33-bus and the 118-bus distribution systems as medium to large systems. Performance parameters, including the reactive power loss, Total Voltage Deviation (TVD), and Stability Index (SI), besides the power loss, are recorded for each optimization case study. When the BES algorithm optimizes 1, 2, and 3 DG units oper-ating at optimal power factor (OPF) into the 33-bus systems, percentage reductions of power loss reach 67.84%, 86.49%, and 94.44%, respectively. Reductions of 28.26%, 34.47%, 35.24%, and 35.44% are achieved in power loss while optimizing 1, 3, 5, and 7 SRC units. With a combination of DG/SRC units, the power loss reductions achieve 72.30%, 93.89%, and 97.49%, optimizing 1, 3, and 5 pairs of them. Similar reduc-tions are achieved for the rest of the performance parameters. With high penetration of compensators into the 118-bus system, the percentage reductions of power loss are 29.14%, 73.27%, 83.72%, 90.14%, and 93.41% for optimal allocations of 1, 3, 5, 7, and 9 DG units operating at OPF. The reduction reaches 11.15%, 39.08% with 1 and 21 devices when optimizing the SRC. When DG SRC units are optimized together, power loss turns out to be 32.83%, 73.31%, 83.32%, 88.52%, and 91.29% with 1, 3, 5, 7, and 9 pairs of them. The approach leads to an enhanced voltage profile near an acceptable range of bus voltages, reduces the voltage fluctuation substantially, and enhances t

关键词： Active distribution systems Distributed generation Shunt reactive power compensators bald eagle search algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid model of bald eagle search and relevance vector machine for dam safety monitoring using long-term temperature

引用

ADVANCED ENGINEERING INFORMATICS 2023年 55卷

作者： Yu, Xin Li, Junjie Kang, Fei Dalian Univ Technol Fac Infrastruct Engn Sch Hydraul Engn Dalian 116024 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Accurate simulation of temperature effect is a major challenge for computational (intelligent) prediction models used for monitoring health of high concrete dams, especially in regions with long freezing periods and distinct seasons, occasional extreme weather. A Hydrostatic-Temperature long-term-Time (HTLT) model was proposed for better temperature effect simulation using long-term measured environment temperatures and explored the influence of temperatures data sets of different time lengths on dam displacement prediction accuracy with the traditional Hydrostatic-Season-Time model as control. The bald eagle search algorithm was coupled with the Relevance Vector Machine to form a novel hybrid model (BES-RVM) for predicting concrete gravity dam displacement response and comparison of nonlinear mapping capability between different kernel functions was conducted. Further optimized by Successive Projections algorithm (SPA) for feature selection, sensitive features were selected from the proposed HTLT variable sets to improve the prediction model's accuracy. The prediction model was experimented on a real concrete dam with results showing that the BES-RVM model gave excellent prediction performance. Using the HTLT variable sets significantly reduced the prediction errors and the best performed result came from variables of the two-year long temperatures data. The SPA optimized BES-RVM model largely brought down the dimension and the collinearity of the input variables and further improved the prediction performance.

关键词： Structural health monitoring Concrete dam displacement prediction bald eagle search algorithm Relevance vector machine regression Successive projections algorithm Temperature simulation

来源：评论

学校读者我要写书评

暂无评论

Adam bald eagle optimization enabled transfer learning for underwater image fusion

引用

IMAGING SCIENCE JOURNAL 2024年第5期72卷 615-630页

作者： Sarath, Devika Sucharitha, M. Noorul Islam Ctr Higher Educ Dept Elect & Commun Engn Kumaracoil Tamil Nadu India VITAP Univ Sch Elect Engn Elect & Commun Engn Amaravathi Andhra Pradesh India Noorul Islam Ctr Higher Educ Dept Elect & Commun Engn Kumaracoil 629180 Tamil Nadu India

In this paper, a clear underwater image is attained by a fusion process using Transfer Learning (TL). Two images are selected from the underwater colour image dataset and those images are allowed to Discrete Wavelet Transform (DWT), Tetrolet transform and Saliency maps. Here, the outputs gained from images by the Tetrolet transform are fused and allowed for inverse Tetrolet transform. Moreover, the DWT process done with two images is fused and the output gained is allowed for inverse DWT. Similarly, the same fusion process is carried out with image outputs from Saliency maps. Finally, three image outputs that are considered as input to TL with newly devised optimization. Here, Convolutional Neural Network (CNN) is used with hyperparameters from trained models, such as SqueezeNet and AlexNet, where weights are updated using Adam Based bald eagle algorithm (ABBEA). This ABBEA is obtained by combining the bald eagle search (BES) algorithm and Adam algorithm. Further, the ABBEA has Peak Signal-to-Noise Ratio (PSNR) with maximal of 38.95, Mean Squared Error (MSE) with lesser value of 20.14, Structural Similarity Index Measure (SSIM) with maximal value of 0.92, Mutual Information (MI) with maximal value of 0.86, Signal-to-Noise Ratio (SNR) with lesser value of 0.38.

关键词： SqueezeNet bald eagle search algorithm AlexNet Adam algorithm Discrete Wavelet Transform Convolutional Neural Network Transfer Learning

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Review and Analysis of the Allocation of Electric Vehicle Charging Stations in Distribution Networks

引用

IEEE ACCESS 2024年 12卷 5404-5461页

作者： Yuvaraj, T. Devabalaji, K. R. Kumar, J. Anish Thanikanti, Sudhakar Babu Nwulu, Nnamdi I. Chennai Inst Technol Ctr Computat Modeling Chennai 600069 India Vinayaka Missions Res Fdn Aarupadai Veedu Inst Technol Dept Elect & Elect Engn Chennai 603104 India Saveetha Engn Coll Dept Elect & Elect Engn Chennai 602105 India Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Univ Johannesburg Ctr Cyber Phys Food Energy & Water Syst ZA-2006 Johannesburg South Africa

In recent times, the limited availability of fossil fuels and growing concerns regarding the emission of greenhouse gases (GHGs) have directly impacted the shift from conventional automobiles to electric vehicles (EVs). Additionally, there have been notable advancements in new energy research, which have significantly improved the viability of EVs. Consequently, EVs have gained widespread recognition and have been rapidly adopted in many countries worldwide. However, the rapid growth of EVs has given rise to several challenges, such as insufficient charging infrastructure, unequal distribution, high costs, and a lack of charging stations, which have become increasingly significant. The limited availability of charging facilities is hindering the widespread adoption of EVs. However, as more people embrace EVs, there has been a growth in the installation of electric vehicle charging stations (EVCSs) in public locations. Recent research has focused on identifying the ideal locations for EVCSs in order to assist the electrification of transport systems and meet the growing demand for EVs. A well-developed EVCS infrastructure can help address some key issues facing EVs, such as pricing and range limitations. Researchers have used various methodologies, objective functions, and constraints to formulate the problem of identifying the best sites for EVCSs. Current research is focused on determining the best locations for EVCSs. This endeavor intends to ease the transition to electrified transport networks while also addressing the growing demand for EVs. This review article explores various optimization techniques to achieve optimal solutions while considering the impact of EV charging load on the distribution system (DS), environmental implications, and economic impact. The research used a standard IEEE 33-bus radial distribution system (RDS) with a full variety of potential energy sources to improve understanding of the subject. The use of the bald eagle search algorithm

关键词： Electrical vehicles electric vehicle charging stations bald eagle search algorithm cuckoo search algorithm renewable distributed generations distribution static compensator battery energy storage system capacitor innovative charging technique radial distribution system

来源：评论

学校读者我要写书评

暂无评论

A novel AI approach for optimal deployment of EV fast charging station and reliability analysis with solar based DGs in distribution network

引用

ENERGY REPORTS 2022年 8卷 11646-11660页

作者： Ahmad, Fareed Ashraf, Imtiaz Iqbal, Atif Marzband, Mousa Khan, Irfan Aligarh Muslim Univ Dept Elect Engn Aligarh India Qatar Univ Dept Elect Engn Doha Qatar Northumbria Univ Dept Math Phys & Elect Engn Newcastle Upon Tyne England King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21589 Saudi Arabia Texas A&M Univ Clean & Resilient Energy Syst CARES Lab Galveston TX 77573 USA

The transportation sector is one of the most prevalent fossil fuel users worldwide. Therefore, to mitigate the impacts of carbon-dioxide emissions and reduce the use of non-environmentally friendly traditional energy resources, the electrification of the transportation system, such as the development of electric vehicles (EV), has become crucial. For impeccable EVs deployment, a well-developed charging infrastructure is required. However, the optimal placement of fast charging stations (FCSs) is a critical concern. Therefore, this article provides a functional approach for identifying the optimal location of FCSs using the east delta network (EDN). In addition, the electrical distribution network's infrastructure is susceptible to changes in electrifying the transportation sector. Therefore, actual power loss, reactive power loss, and investment cost are three areas of consideration in deploying FCSs. Furthermore, including FCSs in the electricity distribution network increases the energy demand from the electrical grid. Therefore, this research paper recommends integrating solar-based distributed generations (SDGs) at selected locations in the distribution network, to mitigate the burden of FCSs on the system. Hence, making the system self-sustaining and reliable. In addition, the reliability of the distribution system is also analyzed after deploying the FCSs and SDGs. Furthermore, six case studies (CS) have been proposed to deploy FCSs with or without DG integration. Consequently, the active power loss went from 1014.48 kW to 829.68 kW for the CS-6.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Artificial Intelligence Fast -charging stations bald eagle search algorithm Optimal placement Electric vehicle Reliability

来源：评论

学校读者我要写书评

暂无评论

Trust Aware Multi-Objective Metaheuristic Optimization Based Secure Route Planning Technique for Cluster Based IIoT Environment

引用

IEEE ACCESS 2022年 10卷 112686-112694页

作者： Nagappan, Krishnaraj Rajendran, Surendran Alotaibi, Youseef SRM Inst Sci & Technol Sch Comp Kattankulathur 603203 India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India Umm Al Qura Univ Univ Coll Al Jamoum Dept Comp Sci Mecca 21421 Saudi Arabia

Industrial Internet of Things (IIoT) finds use in several industrial applications like robots, medical devices, and software-defined manufacturing processes. Apart from the promising benefits of the IIoT networks, several challenging issues need to be resolved, such as network connectivity, security, privacy, heterogeneity, scheduling, and energy efficiency. Due to the large-scale deployment and heterogeneity of the nodes in IIoT networks, some energy-limited nodes have existed in the IIoT networks, resulting in reduced network lifetime. At the same time, security and privacy are also considered as the major issues that exist in the design of IIoT, which can be addressed using secure routing techniques. In this view, this study develops a trust-aware multiobjective metaheuristic optimization-based secure clustering with route planning (TAMOMO-SCRP) technique for cluster-based IIoT environment. The presented TAMOMO-SCRP technique mainly focuses on the design of bald eagle search (BES) algorithm for clustering and routing processes. The proposed TAMOMO-SCRP model derives a fitness function for accomplishing maximum energy efficiency and security. For an effective clustering process, the TAMOMO-SCRP model designs an objective function involving four parameters such as trust level (TL), communication cost (CC), residual energy (RE), and node degree (ND). Besides, the route selection process is based on the fitness function with two variables, namely, queue length and link quality. For assessing the enhanced performance of the TAMOMO-SCRP model, a wide range of experiments were carried out to get the outcomes of network life time(NLT) as 39451,half network die (HND) as 25950 and Stability period (SP) 8000 time calculated no. of alive nodes. The achieved outcomes make sure the better performance of the TAMOMO-SCRP technique against the other recent approaches.

关键词： Industrial Internet of Things Routing Security Clustering algorithms Trust management Performance evaluation Metaheuristics Industrial Internet of Things clustering bald eagle search algorithm trust aware protocols security

来源：评论

学校读者我要写书评

暂无评论

CB-HDM: ECG signal based heart disease classification using convolutional block attention assisted hybrid deep Maxout network

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartB期95卷

作者： Rao, Ch Lakshmi Narayana Kakollu, Vanitha GITAM Deemed Univ Dept Comp Sci Visakhapatnam 530045 Andhra Prades India

Cardiovascular diseases (CVD) stand as the leading cause of death globally. Diagnosis of CVD relies on electrocardiograms (ECGs), which record the heart ' s activity. Adequate labeled data is essential for effectively training machine learning techniques, which may also demand considerable time and memory resources. To address these existing issues, this research introduces an improved detection technique for predicting CVD. Initially, data is sourced from the MIT-BIH arrhythmia database, followed by pre-processing using the Modified FIR filter model (M -FIR) to filter ECG signals. It is used to enhance signal quality and eliminate unwanted noise. M -FIR is used to preserve the specific features of the signal. Feature extraction utilizes the Radial Hilbert Function Transform Network (RHFTN) technique. It can extract the temporal and spectral signal features for effective CVD detection. The Improved bald eagle search algorithm (IBES) is employed to select features and minimize irrelevant features. Detection and classification of heart disease from ECG signals are achieved using the Convolutional Block Attention Assisted Hybrid Deep Maxout Network model (CB-HDM). Losses in the network model are mitigated by the Gazelle Optimization algorithm (GOA). The evaluation results are simulated using Python. The proposed model achieved 98.81% accuracy, 96.18% precision, 96.87% recall, and a 94.39% F1 score. The proposed model demonstrates superior efficiency compared to other classifiers.

关键词： Cardiovascular disease Electrocardiogram FIR filtering Radial Hilbert function bald eagle search algorithm Convolutional block attention hybrid deep Maxout network

来源：评论

学校读者我要写书评

暂无评论

BEFA: bald eagle firefly algorithm enabled deep recurrent neural network-based food quality prediction using dairy products

引用

International Journal of Wireless and Mobile Computing 2024年第4期27卷 320-334页

作者： Manisha, Noothi Jagadeeshwar, Madiraju Department of Computer Science Chaitanya Deemed to be University Urban Telangana Warangal506001 India

Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%. © 2024 Inderscience Enterprises Ltd.

关键词： DNN deep neural network DRNN deep recurrent neural network BES bald eagle search algorithm firefly algorithm min-max normalisation

来源：评论

学校读者我要写书评

暂无评论

Water grade judgment of aquaculture based on IBES and evidence fusion

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期212卷

作者： Dai Junfeng Fu Li-hui Huaiyin Inst Technol Fac Elect Informat Engn Huaian 223003 Peoples R China Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China

In aquaculture, water measuring data obtained by conventional multi-sensors often have some errors, therefore, a new water grade judgment method is proposed in the paper. First, by using the refractive learning strategy, an improved bald eagle search algorithm (IBES) is studied. IBES can effectively overcome the problem of weak local search ability and slow convergence speed in traditional BES algorithm. Second, an aquaculture water grade judgment method based on IBES and evidence fusion is proposed. The interval number is used to represent the aquaculture water parameters, and the reliability coefficient Nk is optimized by IBES to complete the modification of the mass function. Then, the combination rule of interval evidence and the modified mass function are synthesized to obtain the comprehensive interval evidence. Finally, the water grade is judged according to the decision rule. In the experiments, three sensors (temperature, dissolved oxygen and pH) are used to obtain water data. The experimental results show that the median value of the fusion uncertainty obtained by the proposed method is reduced to 0.3769, its reliability is greatly improved compared with a single sensor. In addition, the performance of the proposed method is compared with other optimization algorithms, including GWO, ABC, PSO, WOA, BES, IBES, the experimental results show that the average probabilities of IBES in judgment of water grades for Excellent (I) and Poor (III) are all 93% which rank first. Therefore, the method can accurately determine the water grade from uncertain measurement data with inevitable error and random error, it can provide a new idea for aquaculture water grade judgment.

关键词： D-S theory Interval number Decision rule bald eagle search algorithm Inevitable error Random error

来源：评论

学校读者我要写书评

暂无评论

Identification method for inter-turn faults in transformers based on digital twin concept

引用

FRONTIERS IN ENERGY REsearch 2024年 12卷

作者： Ma, Aiqing Gao, Dawei Qin, Tonghui Wang, Wei Shanghai Univ Elect Power Dept Elect Engn Shanghai Peoples R China

A transformer inter-turn fault identification method is proposed based on the digital twin concept to tackle the challenges of high operational complexity and low accuracy associated with traditional transformer fault identification methods. Initially, the bald eagle search algorithm is employed to optimize the critical parameters of the Extreme Learning Machine (ELM), determining the optimal input layer weight and hidden layer threshold of the Extreme Learning Machine. Subsequently, leveraging the digital twin concept, a digital replica of the physical transformer is established, enabling multi-physical field coupling simulation encompassing electrical, thermal, and acoustic domains to elucidate the variation patterns of various physical parameters across different operational scenarios and fault scenarios. Furthermore, key physical characteristic parameters such as sound pressure and winding hot spot temperature are carefully selected to drive a fault identification model tailored to inter-turn faults within the framework of the digital twin concept. Through a detailed investigation using 630 kV A/10 kV transformers as a case study, the results exhibit an impressive fault identification accuracy of 95.24% for the proposed method. Comparative analysis reveals notable enhancements in fault identification accuracy of 12.22%, 7.85%, and 3.73% for ELM, Support Vector Machine and Tuna Swarm Optimization-ELM models, respectively. These findings underscore the effectiveness and practicality of the transformer inter-turn fault identification method based on the digital twin concept, offering valuable insights for the real-time monitoring and diagnosis of inter-turn faults in transformers.

关键词： oil immersed transformer digital twin bald eagle search algorithm inter-turn fault multiple physical characteristic parameters fault identification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：