检索结果-内蒙古大学图书馆

A reactive power planning model for power transmission systems using meta-heuristics algorithms

Decision Analytics Journal 2023年 7卷

作者： Karmakar, Nihar Bhattacharyya, Biplab Department of Electrical & Electronics Engineering Adamas University Barasat 700126 India Department of Electrical Engineering Indian Institute of Technology (ISM) Dhanbad 826004 India

The load patterns in electricity markets are changing rapidly worldwide. The power engineers are becoming more vigilant in providing an uninterrupted power supply at different loading conditions. This study proposes a unique planning strategy of reactive power (VAr) considering a static load model in the power transmission system. The active/ reactive power-oriented exponential load model for static load is considered during the study of load flow solution. The load flow solution's power losses are measured and minimized by properly placing compensation devices. A flexible ac transmission system (FACTS) technology has been applied in this proposed planning strategy to compensate for reactive power. The separate and collective effect of active and reactive power modeling on load flow is also observed during the calculation of the total operating cost (TOC) of the system. The TOC is formulated empirically by adding different cost components such as cost owing to real power loss, VAr generation cost component, and compensation device cost component. A real-world power transmission system is considered in the study to validate the impetus of the proposed planning strategy. © 2023 The Authors

关键词： Active power loss Exponential load model meta-heuristics algorithms Optimal power flow Prescriptive analytics Reactive power planning

来源：评论

学校读者我要写书评

暂无评论

Fine Tuning of On-Board Traction Converters for High-Speed Electric Multiple Units at Depot

引用

IEEE ACCESS 2024年 12卷 22479-22489页

作者： Dey, Prasenjit Myint, Shwe Kirawanich, Phumin Saha, Anulekha Sumpavakup, Chaiyut Mahidol Univ Cluster Logist & Rail Engn Salaya 73170 Thailand Mahidol Univ Dept Elect Engn Salaya 73170 Thailand Chulalongkorn Univ Dept Elect Engn Bangkok 10330 Thailand King Mongkuts Univ Technol North Bangkok Res Ctr Combust Technol & Alternat Energy CTAE Bangkok 10800 Thailand King Mongkuts Univ Technol North Bangkok Coll Ind Technol Bangkok 10800 Thailand

This article presents a meticulous exploration of on-board traction converters deployed in Electric Multiple Units (EMUs). The study involves the development of a comprehensive traction converter and control system, encompassing essential elements such as transformers, front-end rectifiers, and DC link capacitors. The precise control of the front-end rectifier's switching states is crucial for achieving high-quality power. A new application of the advanced Hybrid Particle Swarm Optimization (Hybrid PSOS) technique for the optimization of controller parameters is presented. This parameter tuning process aims to minimize the integral time absolute error (ITAE), a critical metric governing the regulation of DC-link capacitor voltage. Simulation results showcase the impressive attributes of on-board traction converters, including low harmonic content, a high-power factor, and stable DC voltage. Additionally, a rigorous comparative analysis is conducted between Hybrid PSOS and other established algorithms like Symbiotic Organisms Search (SOS) and Particle Swarm Optimization (PSO). Hybrid PSOS traction unit outperforms SOS and PSO, with a minimal overshoot of 1.3401%, faster settling time of 0.2413 seconds, compared to SOS (0.3884 seconds) and PSO (0.5531 seconds). Total Harmonic Distortion (THD) for secondary line currents, the values are 12.48% for PSO, 2.17% for SOS, and 1.08% for Hybrid PSOS. Hybrid PSOS consistently demonstrates its superiority, significantly enhancing system performance and stability. This research underscores the substantial potential of on-board traction converters, emphasizing their role in facilitating efficient and stable electric multiple unit (EMU) operations.

关键词： DC-link capacitor electric multiple units high speed railways meta-heuristics algorithms

来源：评论

学校读者我要写书评

暂无评论

Improved team learning-based grey wolf optimizer for optimization tasks and engineering problems

引用

JOURNAL OF SUPERCOMPUTING 2023年第10期79卷 10864-10914页

作者： Cui, Jingkai Liu, Tianyu Zhu, Mingchao Xu, Zhenbang Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Optimization refers to finding the optimal solution to minimize or maximize the objective function. In the field of engineering, this plays an important role in designing parameters and reducing manufacturing costs. meta-heuristics such as the grey wolf optimizer (GWO) are efficient ways to solve optimization problems. However, the GWO suffers from premature convergence or low accuracy. In this study, a team learning-based grey wolf optimizer (TLGWO), which consists of two strategies, is proposed to overcome these shortcomings. The neighbor learning strategy introduces the influence of neighbors to improve the local search ability, whereas the random learning strategy provides new search directions to enhance global exploration. Four engineering problems with constraints and 21 benchmark functions were employed to verify the competitiveness of the TLGWO. The test results were compared with three derivatives of the GWO and nine other state-of-the-art algorithms. Furthermore, the experimental results were analyzed using the Friedman and mean absolute error statistical tests. The results show that the proposed TLGWO can provide superior solutions to the compared algorithms on most optimization tasks and solve engineering problems with constraints.

关键词： meta-heuristics algorithms Grey wolf optimizer Benchmark functions optimization Constrained problems optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal coordination of electric vehicle charging and photovoltaic power curtailment in unbalanced low voltage networks: An experimental case

引用

IET SMART GRID 2023年第3期6卷 233-245页

作者： Cortes Borray, Andres Felipe Rauma, Kalle Torres, Esther Basque Res & Technol Alliance BRTA Derio Spain TECNALIA Astondo BideaBldg 700 E-48160 Deno Spain Univ Basque Country Dept Elect Engn UPV EHU Bilbao Spain TU Dortmund Univ Inst Energy Syst Energy Efficiency & Energy Econ Dortmund Germany VTT Tech Res Ctr Finland Espoo Finland

This study introduces a quadratic programming-based optimisation method to coordinate electric vehicle (EV) charging and photovoltaic (PV) curtailment in unbalanced low voltage (LV) networks. The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non-linear models and surpassing the limitations of local solutions derived from meta-heuristics algorithms reported in the literature. The coordination is carried out through a centralised controller installed at the header of the LV feeder. The objective of the proposed strategy is to minimise the power curtailment of all PV systems and maximise the power delivered to all EVs by optimising at every time step a suitable setpoint for the PV units and the charging rate of each EV connected without surpassing network constraints. A new energy-boundary model is also proposed to meet the energy requirements of all EVs, which is based on a recurrent function that depends on the arrival-and-desired energy states of the vehicle to compute its charging trajectory optimally. The effectiveness of the proposed coordination strategy was successfully proven through three scenarios in a laboratory environment, making use of two commercial EVs and a PV inverter in a Power Hardware-in-the-Loop setup.

关键词： Optimisation techniques Power system management, operation and economics Solar power stations and photovoltaic power systems metaheuristics centralised controller quadratic programming-based optimisation method power hardware-in-the-loop setup invertors PV inverter meta-heuristics algorithms arrival-and-desired energy states Automobile electronics and electrics optimal coordination centralised control optimal global solution DC-AC power convertors (invertors) energy-boundary model quadratic programming network constraints nonlinear models convex model PV systems unbalanced low voltage networks charging trajectory Power system control coordination strategy Control of electric power systems power grids demand side management Power electronics, supply and supervisory circuits electric vehicle charging photovoltaic power systems photovoltaic power curtailment PV units

来源：评论

学校读者我要写书评

暂无评论

Solving systems of nonlinear equations with real world problems using an advanced hybrid algorithm

引用

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2022年第4期19卷 238-252页

作者： Verma, Pooja Parouha, Raghav Prasad Indira Gandhi Natl Tribal Univ Dept Math Lalpur 484886 Madhya Pradesh India

Systems of nonlinear equations (SNLEs) existed in various disciplines like engineering and applied mathematics. Finding solutions to SNLES is one of the most challenging problems. An advanced hybrid algorithm (haDEPSO) is proposed in this paper for finding the solution of SNLEs and real world problems, based on multi-population approach. Suggested advanced differential evolution (aDE) and particle swarm optimisation (aPSO) integrated with haDEPSO where in aDE a novel mutation strategy, crossover probability and slightly changed selection scheme is introduced (to avoid premature convergence) and novel gradually varying parameters familiarised in aPSO (to escape stagnation). Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. Performance of proposed haDEPSO as well as its integrating component aDE and aPSO are used to solve three SNLEs and three complex real world problems. Comparative analysis confirms superiority of the proposed algorithms.

关键词： systems of nonlinear equations SNLEs real world applications meta-heuristics algorithms hybrid algorithm

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence for Sustainable Applications 1

引用

丛书名： Artificial intelligence and soft computing for industrial transformation

2023年

作者： K. Umamaheswari B. Vinoth Kumar S. K. Somasundaram

ISBN: (数字)9781394175253

ISBN: (纸本)9781394174584

ARTIFICAL INTELLIGENCE for SUSTAINABLE APPLICATIONS The objective of this book is to leverage the significance of artificial intelligence in achieving sustainable solutions using interdisciplinary research through innovative ideas. With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore. This book highlights the recent advances in AI and its allied technologies with a special focus on sustainable applications. It covers theoretical background, a hands-on approach, and real-time use cases with experimental and analytical results. Audience AI researchers as well as engineers in information technology and computer science.

关键词： Artificial Intelligence Machine Learning Deep Learning meta-heuristics algorithms Evolutionary algorithms Fuzzy systems Swarm Intelligence Bio-inspired algorithms Expert systems Artificial Neural Networks probabilistic theory Cognitive Computing Natural Language Processing

来源：评论

学校读者我要写书评

暂无评论

Development of Green and Non-green Product Manufacturing Model Under Carbon Emission and Price Dependent Demand Using Multi-objective Optimization

引用

International Journal of Applied and Computational Mathematics 2023年第5期9卷 1-12页

作者： Sahoo, Deepanjali Manna, Amalesh Kumar Pati, Jitendra Kumar Tripathy, Arun Kumar Department of Mathematics C. V. Raman Global University Bhubaneswar 752054 India Department of Basic Science and Humanities University of Engineering and Management Kolkata 700160 India KIIT International School Bhubaneswar Odisha 751024 India Department of Mathematics S.S.B College Mahakalapara Odisha Kendrapara India

A large number of customers in the developed countries are aware of regarding the pollution effects of manmade products on environment and human body. Keeping this issue in mind, the manufacturer is produced both green (eco-friendly with environment) and non-green (harmful for environment and human being) products. Motivated from this fact, an inventory model for non-green and green production process with carbon emission investment is developed. The production firms inspect produced non-green and green products to separate perfect and defective items. A part of defective items is reworked by replacing or repairing defective components to reduce the manufacturer average cost. In this study, customers’ demand is considered as a function of selling prices of green and non-green products as well as green level of the product. The aim of this work is to construct multiple objective functions related to the green production inventory model and solve these objective functions by metaheuristic algorithms. Also, the optimal values of production rates, selling prices of the green and non-green products, green level of the green product, carbon emission investment level, production periods of green and non-green products are determined which is minimizing the manufacturer average costs. Finally, numerical experiment is considered to examine the proposed model. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Carbon emission Green production meta-heuristics algorithms Multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Hybrid Binary SGO-GA for solving MAX-SAT problem

引用

Procedia Computer Science 2025年 252卷 944-953页

作者： Rhiddhi Prasad Das Anuruddha Paul Junali Jasmine Jena Bibhuti Bhusan Dash Utpal Chandra De Mahendra Kumar Gourisaria School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India School of Computer Applications KIIT Deemed to be University Bhubaneswar Odisha India

The Maximum Satisfiability Problem (MAX-SAT) is a crucial NP-hard optimization problem with applications in artificial intelligence, circuit design, scheduling, and combinatorial optimization. In this work, we provide a unique hybrid strategy that blends Genetic algorithms (GA) with Social Group Optimization (SGO) algorithm to effectively solve the MAX-SAT problem. The SGO algorithm, inspired by the social behavior of groups, excels in exploring diverse regions of the search space. w used a binary variant of SGO i.e. Binary-SGO which is defined specifically for binary search spaces, while GA leverages evolutionary principles to exploit local optima through selection, crossover, and mutation. By integrating the exploration capabilities of SGO with the exploitation strengths of GA, the hybrid approach strikes an optimal balance between global and local search. Extensive experimental evaluations conducted on standard MAX-SAT benchmarks demonstrate that our hybrid algorithm outperforms several existing state-of-the-art meta-heuristic algorithms. Hybrid BSGO-GA achieved the highest average fitness values, with an average accuracy of 99.7% in Experiment 1, 99.61% in Experiment 2, and 99.21% in Experiment 3 and achieved complete satisfiability in 55 out of 75 cases in Experiment 1, 42 out of 75 cases in Experiment 2, and 7 out of 75 cases in Experiment 3. This approach demonstrates the potential of hybrid metaheuristics in addressing complex optimization problems and offers a robust framework for tackling other NP-hard problems.

关键词： Binary Social Group Optimization Genetic Algorithm MAX-SAT Problem meta-heuristics algorithms

来源：评论

学校读者我要写书评

暂无评论

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

引用

NEURAL COMPUTING & APPLICATIONS 2021年第24期33卷 16899-16919页

作者： Houssein, Essam H. Emam, Marwa M. Ali, Abdelmgeid A. Minia Univ Fac Computers & Informat Al Minya Egypt

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.

关键词： COVID-19 CT images Otsu's method Multilevel thresholding image segmentation Manta ray foraging optimization meta-heuristics algorithms

来源：评论

学校读者我要写书评

暂无评论

An advanced hybrid algorithm for constrained function optimization with engineering applications

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第7期14卷 8185-8217页

作者： Verma, Pooja Parouha, Raghav Prasad Indira Gandhi Natl Tribal Univ Dept Math Amarkantak MP India

An advanced hybrid algorithm (haDEPSO) is proposed in this paper for constrained optimization problems, based on a multi-population approach. It integrated with suggested advanced differential evolution (aDE) and particle swarm optimization (aPSO). In aDE a novel mutation strategy, crossover probability and random nature selection scheme are introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, to avoid stagnation. The convergence characteristic of aDE and aPSO provides a different approximation to the solution space. Thus, haDEPSO achieves better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The performance of proposed hybrid and its integrated component is verified on IEEE CEC2006 and IEEE CEC2010 constrained benchmark functions plus five complex engineering problems. Several numerical, statistical, graphical and comparative analyses confirm superiority of proposed algorithms over many state-of-the-art algorithms.

关键词： meta-heuristics algorithms Hybrid algorithm Constrained functions Engineering problems

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

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

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：