检索结果-内蒙古大学图书馆

A novel approach of intensified barnacles mating optimization for the mitigation of power system oscillations

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第17期33卷

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Kumari, Archana Indian Inst Technol ISM Dept Elect Engn Dhanbad Jharkhand India BIT Sindri Dept Elect Engn Dhanbad Jharkhand India

optimization is the process of attaining the best solution from the available set of prioritized constraints to maximize or minimize the desired function involved in it. It is imperative in engineering to plan and design to find the best out of confined resource and time. A recently recommended barnacles matting optimization (BMO) has been recognized in computing the optimal parameters of the electric power system with excellent performance characteristics. In this paper, the intensified version of BMO has been proposed with the aid of cuckoo search (CS) and traditional particle swarm optimization (PSO). An in-depth analysis was made on the BMO technique and proposed suitable modifications to deal with the electrical power system stability enhancement issue efficiently. The proposed technique in the power system stabilizer (PSS) parameter computation is validated on the 23 benchmark functions to examine for its suitability. The robustness of the proposed method is presented via statistical analysis and boxplot of the 23 benchmark functions. The PSS parameters are computed in a benchmark two area four machine system using intensified BMO under self-clearing fault conditions. A multi-objective function is designed to improve the damping nature offered under system uncertainties, and the comparative analysis is presented among conventional PSS, BMO, intensified BMO with CS and PSO (BMO-CS and BMO-PSO), and harris hawks optimizer.

关键词： barnacles matting optimization cuckoo search Harris hawks optimizer hybrid optimization algorithm power system oscillation damping

来源：评论

学校读者我要写书评

暂无评论

Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators

引用

ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2017年第2期20卷 411-426页

作者： Teeparthi, Kiran Kumar, D. M. Vinod NIT Warangal Dept Elect Engn Warangal Andhra Pradesh India

In this paper, a new low level with teamwork heterogeneous hybrid particle swarm optimization and artificial physics optimization (HPSO-APO) algorithm is proposed to solve the multi-objective security constrained optimal power flow (MO-SCOPF) problem. Being engaged with the environmental and total production cost concerns, wind energy is highly penetrating to the main grid. The total production cost, active power losses and security index are considered as the objective functions. These are simultaneously optimized using the proposed algorithm for base case and contingency cases. Though PSO algorithm exhibits good convergence characteristic, fails to give near optimal solution. On the other hand, the APO algorithm shows the capability of improving diversity in search space and also to reach a near global optimum point, whereas, APO is prone to premature convergence. The proposed hybrid HPSO-APO algorithm combines both individual algorithm strengths, to get balance between global and local search capability. The APO algorithm is improving diversity in the search space of the PSO algorithm. The hybrid optimization algorithm is employed to alleviate the line overloads by generator rescheduling during contingencies. The standard IEEE 30-bus and Indian 75-bus practical test systems are considered to evaluate the robustness of the proposed method. The simulation results reveal that the proposed HPSO-APO method is more efficient and robust than the standard PSO and APO methods in terms of getting diverse Pareto optimal solutions. Hence, the proposed hybrid method can be used for the large interconnected power system to solve MO-SCOPF problem with integration of wind and thermal generators. (C) 2017 Karabuk University. Publishing services by Elsevier B.V.

关键词： Multi-objective hybrid optimization algorithm Security constrained optimal power flow Pareto optimal solution

来源：评论

学校读者我要写书评

暂无评论

Estimating maize evapotranspiration based on hybrid back-propagation neural network models and meteorological, soil, and crop data

引用

INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024年第3期68卷 511-525页

作者： Zhao, Long Qing, Shunhao Li, Hui Qiu, Zhaomei Niu, Xiaoli Shi, Yi Chen, Shuangchen Xing, Xuguang Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Henan Univ Sci & Technol Coll Hort & Plant Protect Luoyang 471000 Henan Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Xianyang 712100 Shaanxi Peoples R China

Crop evapotranspiration is a key parameter influencing water-saving irrigation and water resources management of agriculture. However, current models for estimating maize evapotranspiration primarily rely on meteorological data and empirical coefficients, and the estimated evapotranspiration contains uncertainties. In this study, the evapotranspiration data of summer maize were collected from typical stations in Northern China (Yucheng Station), and a back-propagation neural network (BP) model for predicting maize evapotranspiration was constructed based on meteorological data, soil data, and crop data. To further improve its accuracy, the maize evapotranspiration model was optimized using three bionic optimization algorithms, namely the sand cat swarm optimization (SCSO) algorithms, hunter-prey optimizer (HPO) algorithm, and golden jackal optimization (GJO) algorithm. The results showed that the fusion of meteorological, soil moisture, and crop data can effectively improve the accuracy of the maize evapotranspiration model. The model showed higher accuracy with the hybrid optimization model SCSO-BP compared to the stand-alone BP neural network model, with improvements of 2.7-4.8%, 17.2-25.5%, 13.9-26.8%, and 3.3-5.6% in terms of R2, RMSE, MAE, and NSE, respectively. Comprehensively compared with existing maize evapotranspiration models, the SCSO-BP model presented the highest accuracy, with R2 = 0.842, RMSE = 0.433 mm/day, MAE = 0.316 mm/day, NSE = 0.840, and overall global evaluation index (GPI) ranking the first. The results have reference value for the calculation of daily evapotranspiration of maize in similar areas of northern China.

关键词： Evapotranspiration Maize Back-propagation neural network hybrid optimization algorithm Sand cat swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A distributionally robust optimization model for batch nonlinear switched time-delay system considering uncertain output measurements

引用

NONLINEAR ANALYSIS-hybrid SYSTEMS 2023年 48卷

作者： Yuan, Jinlong Guo, Ke Meng, Lixia Liu, Chongyang Teo, Kok Lay Xie, Jun Dalian Maritime Univ Sch Sci Dept Appl Math Dalian 116026 Peoples R China Dalian Maritime Univ Coll Transportat Engn Dalian 116026 Liaoning Peoples R China China West Normal Univ Sichuan Coll & Univ Key Lab Optimizat Theory & App Sch Math & Informat Nanchong 637009 Peoples R China Shandong Technol & Business Univ Sch Math & Informat Sci Yantai 264005 Peoples R China Sunway Univ Sch Math Sci Kuala Lumpur 47500 Malaysia PLA Dalian Naval Acad Dept Basics Dalian 116018 Liaoning Peoples R China

In this paper, we consider a nonlinear switched time-delay (NSTD) system with unknown switching times and unknown system parameters, where the output measurement is uncertain. This system is the underling dynamical system for the batch process of glycerol bioconversion to 1,3-propanediol induced by Klebsiella pneumoniae. The uncertain output measurement is regarded as a stochastic vector (whose components are stochastic variables) and the only information about its distribution is the first-order moment. The objective of this paper is to identify the unknown quantities of the NSTD system. For this, a distributionally robust optimization problem (a bi-level optimization problem) governed by the NSTD system is proposed, where the relative error under the environment of uncertain output measurements is involved in the cost functional. The bi-level optimization problem is transformed into a single-level optimization problem with non-smooth term through the application of duality theory in probability space. By applying the smoothing technique, the non-smooth term is approximated by a smooth term and the convergence of the approximation is established. Then, the gradients of the cost functional with respect to switching times and system parameters are derived. A hybrid optimization algorithm is developed to solve the transformed problem. Finally, we verify the obtained switching times and system parameters, as well as the effectiveness of the proposed algorithm, by solving this distributionally robust optimization problem.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Distributionally robust optimization Uncertain output measurements Switched time -delay system Convergence analysis hybrid optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks

引用

APPLIED ENERGY 2022年 322卷

作者： Adetunji, Kayode E. Hofsajer, Ivan W. Abu-Mahfouz, Adnan M. Cheng, Ling Univ Witwatersrand Sch Elect & Informat Engn 1 Jan Smuts Ave ZA-2000 Johannesburg South Africa CSIR ZA-0184 Pretoria South Africa

In developing a sustainable and efficient power systems network while reducing carbon footprint, renewable energy (RE)-based Distribution Generation (DG) units are highly recommended. Furthermore, Battery Energy Storage Systems (BESS) and other passive electronic units are adopted to improve grid performance and mitigate the effects of high variability from RE power. Hence, planning frameworks are developed to optimally allocate these units to distribution networks. However, current planning mechanisms do not consider the relative effect of different allocated units in planning frameworks. To bridge this gap, this paper presents a novel comprehensive planning framework for allocating DG units, BESS units, and Electric Vehicle Charging Station (EVCS) facilities in a distribution network while optimizing its technical, economic, and environmental benefits. The proposed framework uses a recombination technique to generate more solutions by dynamically updating the DG and BESS units' locations in one iteration. A Reinforcement Learning (RL)-based algorithm is introduced to coordinate EV charging that suggests the optimal EVCS location in relation to other units' locations. To cope with the complexity ensuing from searching a larger solution space, a multi-stage, hybrid optimization scheme is developed to produce optimal allocation variables. A category-based multiobjective framework is further developed to simultaneously optimize many objective functions - power loss, voltage stability, voltage deviation, installation and operation cost, and emission cost. Through numerical simulations on the IEEE 33-and 118-bus distribution network, it is shown that the proposed optimization scheme achieves higher metric values than the adopted benchmark optimization schemes. A validation process was also carried out on the proposed multiobjective optimization approach, comparing it with other approaches. Using the Spacing metric, the proposed approach proves to be efficient, depicting

关键词： Battery energy storage systems Distributed generation Electric vehicles Electric vehicle charging station hybrid optimization algorithm Multiobjective optimization Pareto optimal solutions Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Solution of economic dispatch problem for wind-thermal power systems by a modified hybrid optimization method

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

引用

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2019年第4期34卷 1871-1895页

作者： Tefek, Mehmet Fatih Uguz, Harun Kirsehir Ahi Evran Univ Kaman Vocat Sch Dept Comp Technol TR-40300 Kaman Kirsehir Turkey Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn TR-42250 Konya Turkey

Economic Dispatch Problem (EDP) is a complex, constrained and non-linear optimization problem. In the EDP, it is aimed to minimize the system fuel cost between minimum and maximum limits of the active power buses. In this study, a modified hybrid Gravitational Search- Teaching-Learning Based optimization algorithm (MHGT), a quick, efficient and reliable method is proposed by combining standard Gravitational Search algorithm (GSA) and Teaching-Learning Based optimization (TLBO). The proposed MHGT method was developed by modifying the global search superiority in GSA and powerful local search specialty in TLBO for the solution of constrained optimization problem. The MHGT was tested experimentally by well-known and mostly used ten benchmark function in the literature. The proposed method was first implemented on a 6 bus wind-thermal power system for 400, 450 and 500 MW powers. Then, it was implemented on Turkey 19 bus wind-thermal power system according to different ratios of the installed power as 25, 27.5 and 30 percent to solve the EDP problem. The obtained results were compared with the results of other studies. From the results, it is seen that the proposed MHGT method finds the solution in a short execution time and less fuel cost with more reliably and more efficiently in terms of both fuel cost and execution time.

关键词： hybrid optimization algorithm gravitational search algorithm teaching-learning based algorithm economic dispatch problem wind-thermal power system

来源：评论

学校读者我要写书评

暂无评论

A hybrid TCNN optimization Approach for the Capacity Vehicle Routing Problem

引用

Journal of Shanghai Jiaotong university(Science) 2006年第1期11卷 34-39页

作者：孙华丽谢剑英薛耀锋 Dept.of Automation Shanghai Jiaotong Univ.Shanghai 200030China

A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehicles among the available vehicles, the initial routing of the selected fleet and the routing optimization. Fuzzy C-means (FCM) can group the customers with close Euclidean distance into the same vehicle according to the principle of similar feature partition. Transiently chaotic neural network (TCNN) combines local search and global search, possessing high search efficiency. It will solve the routes to near optimality. A simple tabu search (TS) procedure can improve the routes to more optimality. The computations on benchmark problems and comparisons with other results in literatures show that the proposed algorithm is a viable and effective approach for CVRP.

关键词： capacity vehicle routing problem fuzzy C-means transiently chaotic neural network hybrid optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Adaptive protection coordination in microgrid based on nature inspired meta-heuristic optimization algorithm

ENGINEERING RESEARCH EXPRESS

引用

ENGINEERING RESEARCH EXPRESS 2024年第2期6卷

作者： Kumari, Rani Naick, Bhukya Krishna Indian Inst Technol IIT Dept Elect Engn Dhanbad India

Ensuring a robust protection system is crucial for safeguarding the integrity of the overall system against abnormalities. Incorporating distributed generation (DG) into the distribution network can introduce fluctuations in fault current levels and directions, potentially causing mismatches in the response of the existing coordination system. This study proposes an adaptive protection coordination scheme designed to accommodate both grid-connected and standalone modes, addressing various fault scenarios. Utilizing a hybrid WCMFO algorithm, optimal relay settings are determined to facilitate effective coordination within a microgrid setup. The proposed method has been analyzed on 9 bus Canadian benchmark system integrated with four DGs. The performance of the proposed method is compared to other optimization techniques to demonstrate its effectiveness. System modelling is conducted using MATLAB/Simulink, and validation is further carried out using industrial ETAP software on a test microgrid system. The analysis extends to evaluating the enhancement in overall system reliability, quantified in terms of energy not supplied (ENS).

关键词： adaptive protection distributed generation DOCR hybrid optimization algorithm protection coordination

来源：评论

学校读者我要写书评

暂无评论

hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-29页

作者： Mohammad, Saleem Jeebaseelan, S. D. Sundarsingh Sathyabama Inst Sci & Technol Dept Elect & Elect Engn Chennai 600119 India

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

关键词： hybrid optimization algorithm Sine-Cosine algorithm (SCA) Spotted Hyena Optimizer (SHO) Chimp optimization algorithm (ChOA) Proportional-integral (PI) controllers Microgrid Renewable energy integration Integral of Time-Weighted Squared Error (ITSE)

来源：评论

学校读者我要写书评

暂无评论

Adjustable Robust optimization for Large-Scale Photovoltaics Planning in Smart Distribution Networks

IEEE Transactions on Sustainable Computing

引用

IEEE Transactions on Sustainable Computing 2024年

作者： Ge, Leijiao Li, Yuanliang Yan, Jun Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin300072 China Jinan Power Supply Company State Grid Shandong Jinan250013 China Concordia University Concordia Institute for Information Systems Engineering MontréalQCH3G 1M8 Canada

High penetration in distributed photovoltaics (PVs) enhances the resilience of distribution network operations. However, it brings many uncertain issues such as the voltage and harmonic fluctuations that may affect the distribution network stability. Therefore, the determination of the PV location and capacity is of vital importance for smart distribution network planning. Considering the fluctuation of PV outputs and the robustness of optimization results, this paper proposes an adjustable robust optimization method for large-scale PV planning in smart distribution networks. The AC power flow is first converted into a convex program by the linearization theory, and the bi-level optimization is converted to a single-layer optimization problem using the strong duality theory. A hybrid algorithm based on the integration of grey wolf optimization (GWO) and improved particle swarm optimization (IPSO), named GWO-IPSO, is proposed to determine the robust optimal location and the capacity of distributed PVs. The proposed method is verified based on the IEEE 69-bus radial distribution network case study. The proposed method efficiently adapts to parameter uncertainties resulting from extreme scenarios and delivers better performance than general heuristic methods. © 2016 IEEE.

关键词： adjustable robust optimization bi-level optimization conversion Distributed photovoltaic planning hybrid optimization algorithm

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：