检索结果-内蒙古大学图书馆

Reactive power management by using a modified differential evolution algorithm

OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第2期44卷 967-986页

作者： Kar, Manoj Kumar Kumar, Sanjay Singh, Arun Kumar Panigrahi, Sibarama Natl Inst Technol Dept Elect Engn Jamshedpur Jharkhand India Sambalpur Univ Inst Informat Technol Dept Comp Sci Engn & Applicat Burla 768019 Odisha India

In this article, a modified differential evolution (MDE) algorithm is proposed and applied to provide the solution for reactive power management by incorporating the flexible alternating current transmission systems (FACTS) controllers. The proper siting of FACTS controller has been achieved with an objective to minimize the losses and to improve the loading capability. The power flow analysis is performed to determine the optimal position for FACTS controllers. These controllers are incorporated in the most heavily loaded lines and hence controls the power flow in that particular line and allow more power to be transmitted in the remaining lines. The proposed MDE algorithm uses a novel DE/best3/1/bin mutation operator to produce three temporary mutant vectors which are averaged to obtain the mutant vector. Hence, the decision vectors of a generation simultaneously move toward the three best decision vectors of the population thereby maintains a better trade between exploration and exploitation. The proposed MDE algorithm is applied on different standard test bus (i.e., IEEE30, IEEE57, and IEEE118) systems with varying active and reactive loading (i.e., 100%, 110%, and 120%). The proposed method's performance is compared to those obtained from some well-known meta-heuristic algorithms. The proposed MDE algorithm optimized FACTS controllers reduce transmission loss by 60.90% in IEEE30 bus, 49.72% in IEEE57 bus and 8.37% in IEEE118 bus test system under base loading. The statistical analysis of the obtained results is carried out using the Wilcoxon signed rank test and the Friedman and Nemenyi hypothesis test, which ensures the reliability and robustness of the proposed method.

关键词： FACTS controllers modified differential evolution algorithm optimal location power loss reduction power system reliability reactive power management

来源：评论

学校读者我要写书评

暂无评论

Rice Leaf Disease Identification Using Adam Optimizer Based modified differential evolution algorithm 1

Rice Leaf Disease Identification Using Adam Optimizer Based ...

引用

1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Vijayarangan, R. Yamsani, Nagendar Thirumurugan, V. Arthy, P.S. Alzubaidi, Laith H. Rvt Research Associates Chennai India School of Computer Science and Artificial Intelligence Sr University Warangal India Vistas Cse Chennai India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India The Islamic University Najaf Iraq

ISBN: (纸本)9798350316469

Recently, one of the grain-based crops that is farmed the most is rice, which is important to the agricultural sector. Furthermore, rice is a significant crop that is consumed by almost half of the global population. However, biotic and abiotic elements including bacteria, viruses, pests, soil fertility, and so forth have an impact on agricultural production. Early disease identification saves farmers time, increases crop yield, and guards against production loss in rice plants. The rice fields used for cultivation are the source of the rice leaf image dataset. The three disease classes that made up the data acquisition were Brown Spot, Bacterial Blight, and Leaf Blast. The ultimate goal of this proposed study is to improve rice leaf detection and classification performance. Thus, using a deep learning approach, this study presented an Adam Optimizer (AO) to carry out an efficient classification of rice leaf disease. Moreover, rice leaf disease detection and classification involve the use of Transfer Learning in conjunction with Inception V3 architecture. The modified differential evolution algorithm (MDEA) technique is used to determine the transfer learning approach's optimal learning rate. Finally, the proposed AO-MDEA shows better performance metrics in terms of accuracy (98.74%), precision (99.04%), recall (99.12%) and F1-score (99.15%) respectively. © 2023 IEEE.

关键词： Adam optimizer Deep learning modified differential evolution algorithm Rice leaf disease detection Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Optimization of digital predistortion models for RF power amplifiers using a modified differential evolution algorithm

引用

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2020年 124卷 153323-153323页

作者： Devi, Sanjika R., V Kalyan, Robin Bindu, K. R. Kurup, Dhanesh G. Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Bengaluru India Indian Space Res Org ISRO UR Rao Satellite Ctr Commun Syst Grp Bengaluru India

This paper, investigates and presents the optimal parameter identification of digital pre-distortion (DPD) models for radio frequency power amplifiers (RF PAs) using a modified differential evolution (MDE) based optimization algorithm. Compared to the conventional exhaustive search method which is computationally intensive, our proposed approach enables the identification of a best-fit DPD model from a combinatorially large model space in a short time. In addition, applying information criteria based objective functions in the optimization process enables us to achieve sparse selection of dynamical models, which balances the model accuracy and model complexity. Experimental validation on a GaN based class AB power amplifier illustrates that, our proposed approach was able to accurately identify complexity reduced optimal DPD models without compromising the modeling accuracy. (C) 2020 Elsevier GmbH. All rights reserved.

关键词： RF power amplifier Digital predistortion modified differential evolution algorithm

来源：评论

学校读者我要写书评

暂无评论

modified differential evolution algorithm for a transportation software application

引用

Journal of Open Innovation: Technology, Market, and Complexity 2019年第4期5卷 84页

作者： Supattananon, Naratip Akararungruangkul, Raknoi Department of Industrial Engineering Faculty of Engineering KhonKaen University KhonKaen 40000 Thailand

This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company's profit by 5.90% to 6.42%. © 2019 by the authors.

关键词： Inbound transportation problem modified differential evolution algorithm Vehicle dispatching problem

来源：评论

学校读者我要写书评

暂无评论

Optimal planning of energy storage system using modified differential evolution algorithm 5

Optimal planning of energy storage system using modified dif...

引用

5th International Conference on Power and Energy Systems Engineering (CPESE)

作者： Sasantia, Chatchawan Siritaratiwat, Apirat Surawanitkun, Chayada Khunkitti, Pirat Chatthaworn, Rongrit Khon Kaen Univ Fac Engn Dept Elect Engn Khon Kaen Thailand Khon Kaen Univ Fac Appl Sci & Engn Nong Khai Campus Nong Khai Thailand

In smart distribution network, optimal planning of Energy Storage Systems (ESS) can lead to voltage profile improvement, network loss reduction and increasing the profit of distribution company (DISCO). This paper proposes the application of modified differential evolution algorithm (MDEA) which is very strong global search capability for maximizing the DISCO profit over the planning. The objective function is the difference between the benefit which is the energy arbitrage in a deregulated market and the costs which are investment cost, operation and maintenance cost. The implementation of MDEA is performed on a 30-bus radial distribution network. The simulation results demonstrate that the proposed MDEA application significantly increases the profit without any violation of network constraints. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Distribution Network Energy Storage System modified differential evolution algorithm Profit of Distribution Company

来源：评论

学校读者我要写书评

暂无评论

Synthesis of unequally spaced linear arrays using modified differential evolution algorithm

引用

IET MICROWAVES ANTENNAS & PROPAGATION 2018年第12期12卷 1908-1912页

作者： Wang, Rui-Qi Jiao, Yong-Chang Zhang, Huan Zhou, Zhipeng Xidian Univ Xian Branch Natl Key Lab Antennas & Microwave Technol Xian 710071 Shaanxi Peoples R China Nanjing Res Inst Elect Technol Nanjing Branch Natl Key Lab Antennas & Microwave Technol Nanjing 210039 Jiangsu Micronesia

Unequally spaced linear arrays are synthesised for the sidelobe level (SLL) suppression, the null control in some specified directions and the beamwidth (BW) set within predefined limits. Based on the vector mapping method and the position perturbation technique, the constrained vector projection (CVP) is defined to offer flexibility in controlling null depths with multiple constraints. A modified differential evolution (DE) algorithm with the CVP method (in short MDE-CVP) is then performed in the array synthesis, which effectively reduces the optimisation space, avoids the infeasible solutions and enhances the global search ability. Three examples are presented and the first two results are compared with those obtained by learning particle swarm optimisation, invasive weed optimisation and wind-driven optimisation and a modified version of the central force optimisation algorithms. Comparison results indicate that the MDE-CVP algorithm outperforms the existing algorithms in terms of minimum SLL, BW control, null control and convergence characteristics.

关键词： linear antenna arrays evolutionary computation search problems particle swarm optimisation unequally spaced linear array synthesis modified differential evolution algorithm sidelobe level suppression SLL suppression null depth control beamwidth predefined limits vector mapping method position perturbation technique constrained vector projection MDE-CVP algorithm optimisation space global search ability learning particle swarm optimisation invasive weed optimisation wind-driven optimisation central force optimisation algorithms

来源：评论

学校读者我要写书评

暂无评论

Optimal planning of energy storage system using modified differential evolution algorithm

引用

Energy Procedia 2019年 156卷 192-200页

作者： Chatchawan Sasantia Apirat Siritaratiwat Chayada Surawanitkun Pirat Khunkitti Rongrit Chatthaworn Department of Electrical Engineering Faculty of Engineering Khon Kaen University Khon Kaen Thailand Faculty of Applied Science and Engineering Khon Kaen University Nong Khai Campus Nong Khai Thailand

关键词： Distribution Network Energy Storage System modified differential evolution algorithm Profit of Distribution Company

来源：评论

学校读者我要写书评

暂无评论

Solving the Economic Load Dispatch Problems by a modified differential evolution algorithm based on Two Mutation Strategies and One Disturbance

Solving the Economic Load Dispatch Problems by a Modified Di...

引用

第30届中国控制与决策会议

作者： Xin Shen Dexuan Zou Xiangyong Kong Xin Zhang Zhen Hu School of Electrical Engineering and Automation Jiangsu Normal University

A modified differential evolution(MDE) algorithm is developed to solve the economic load dispatch(ELD) problems without or with valve-point effects(VPE). MDE mainly has two improvements compared to the original differential evolution(DE) algorithm. The whole evolution process is divided into the early and late phases. The early phase uses two mutation strategies, and the late phase only uses a mutation strategy. The operation is suitable for any evolution phase. The disturbance with a small probability is added to avoid falling into the local optima in the mutation operation. The control parameters are adaptively adjusted to improve the convergence performance of MDE. Additionally, a repair method is used to handle equality constraints, which enables MDE to find feasible solutions rapidly. Five ELD cases are selected to testify the effectiveness of the proposed method. Results show that MDE performs better than the other methods for the ELD problems.

关键词： modified differential evolution algorithm Economic Load Dispatch Valve-Point Effects Mutation Repair Method

来源：评论

学校读者我要写书评

暂无评论

modified differential evolution algorithm for multi-objective VAR management

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 55卷 731-740页

作者： Singh, Himmat Srivastava, Laxmi Madhav Inst Sci & Technol Dept Elect Engn Gwalior 474005 India

Reactive power or VAR management is one of the most crucial tasks required for proper operation and control of a power system. Reactive power management is carried out to reduce losses and to improve voltage profile in a power system, by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks or FACTS devices. VAR management provides better system security, improved power transfer capability and overall system operation. VAR management is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, the VAR management problem is formulated as a nonlinear constrained multi-objective optimization problem with equality and inequality constraints for minimization of real power losses and voltage deviation simultaneously. This multi-objective problem is solved using differential evolution (DE), which is a population based search algorithm. For avoiding the time and the effort in tuning the parameters of DE algorithm, a modified DE algorithm with time varying chaotic mutation and crossover is proposed for solving the multi-objective VAR management problem. Weighing factor method has been employed for finding Pareto optimal set for VAR management problem. Fuzzy membership function is used to obtain the best compromise solution out of the available Pareto-optimal solutions. Effectiveness of the proposed modified DE algorithm based approach has been demonstrated on IEEE 30-bus system and is found to be superior to classical DE and its variants Self-adaptive differential evolution (SaDE) and Ensemble of Mutation and Crossover Strategies and Parameters in differential evolution (EPSDE) in terms of convergence behavior and solution quality. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： modified differential evolution algorithm Pareto-optimal solution Real power loss minimization VAR management Voltage deviation

来源：评论

学校读者我要写书评

暂无评论

Energy management of wind turbine-based DC microgrid utilizing modified differential evolution algorithm

Energy management of wind turbine-based DC microgrid utilizi...

引用

International Conference on Renewable Power Generation (RPG 2015)

作者： M. Yu Y. Wang Y. G. Li North China Electric Power University - Baoding Campus Baoding Hebei CN

ISBN: (纸本)9781510821415

An energy management optimization model of a wind turbine-based DC microgrid with batteries as the energy storage system is proposed. With introduction of the state of health of battery in this model, the aging process of the storage system is measured. In addition, an improved differential evolution algorithm is proposed which uses the group fitness variance as parameter adjustment indicator and is applied to solve the optimization problem of the DC microgrid system model. In order to verify the effectiveness of the proposed energy management method, simulations are conducted with results showing that the proposed energy management model can reduce the operation cost caused by the aging of batteries and correct in advance the foreseeable power fluctuations, thus meets the requirement of stably and economically operation of the DC microgrid.

关键词： DC microgrid Wind turbine energy management modified differential evolution algorithm state of health

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：