检索结果-内蒙古大学图书馆

A Literature Review on the Optimal Placement of Static Synchronous Compensator (STATCOM) in Distribution Networks

ENERGIES 2023年第17期16卷 6122页

作者： Mumtahina, Umme Alahakoon, Sanath Wolfs, Peter Cent Queensland Univ Sch Engn & Technol Rockhampton 4701 Australia EleXsys Ptd Ltd Brisbane 4072 Australia

The existing distribution networks were designed at a time when there was virtually no embedded generation. The design methods ensured the voltage at various parts of the network remained within the limits required by standards, and for the most part, this was very successfully achieved. As Distributed Energy Resources (DERs) started to grow, the rise in voltage due to injected currents and the local impedances started to push network voltages toward, and even above, the desired upper limits. Voltage limits are based on typical appliance requirements, and long-term over-voltages will ultimately result in unacceptably short appliance life spans. Distribution Static Compensators (dSTATCOMs) are shunt-connected devices that can improve low-voltage networks' performance by injecting currents that do not transfer real power. The currents can be reactive, negative or zero sequence, or harmonic. System performance can be improved by reducing conduction loss, improving voltage profile and voltage balance, or reducing Total Harmonic Distortion (THD). To obtain these benefits, optimal sizes of dSTATCOMs need to be placed at optimal locations within the distribution network. This paper has considered seventy research articles published over the past years related to the optimal placement and sizing of dSTATCOMs. In this study, minimization of power losses, voltage profile improvement, loadablity factor, voltage sag mitigation, and reduction in annual operating costs are considered fitness functions that are subjected to multiple constraint sets. The optimization algorithms found in the literature are categorized into six methods: analytical methods, artificial neural network-based methods, sensitivity approaches, metaheuristic methods, a combination of metaheuristic and sensitivity analysis, and miscellaneous. This study also presents a comparison among distribution network types, load flow methods optimization tools, etc. Therefore, a comprehensive review of optimal allocatio

关键词： optimal placement optimal sizing dSTATCOM distribution network optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Comparing Mechanical Neural-Network Learning algorithms

引用

JOURNAL OF MECHANICAL DESIGN 2023年第7期145卷 071704-071704页

作者： Lee, Ryan H. H. Sainaghi, Pietro Hopkins, Jonathan B. Univ Calif Los Angeles Dept Mech & Aerosp Engn 420 Westwood PlazaRoom 46-127 Los Angeles CA 90095 USA

The purpose of this work is to compare learning algorithms to identify which is the fastest and most accurate for training mechanical neural networks (MNNs). MNNs are a unique class of lattice-based artificial intelligence (AI) architected materials that learn their mechanical behaviors with repeated exposure to external loads. They can learn multiple behaviors simultaneously in situ and re-learn desired behaviors after being damaged or cut into new shapes. MNNs learn by tuning the stiffnesses of their constituent beams similar to how artificial neural networks (ANNs) learn by tuning their weights. In this work, we compare the performance of six algorithms (i.e., genetic algorithm, full pattern search, partial pattern search, interior point, sequential quadratic progression, and Nelder-Mead) applied to MNN leaning. A computational model was created to simulate MNN learning using these algorithms with experimentally measured noise included. A total of 3900 runs were simulated. The results were validated using experimentally collected data from a physical MNN. We identify algorithms like Nelder-Mead that are both fast and able to reject noise. Additionally, we provide insights into selecting learning algorithms based on the desired balance between accuracy and speed, as well as the general characteristics that are favorable for training MNNs. These insights will promote more efficient MNN learning and will provide a foundation for future algorithm development.

关键词： mechanical neural network optimization algorithms learning materials AI materials architected materials metamaterials smart materials

来源：评论

学校读者我要写书评

暂无评论

Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information

引用

EUROPEAN JOURNAL OF CONTROL 2023年 74卷

作者： Mascherpa, Michele Haasler, Isabel Ahlgren, Bengt Karlsson, Johan KTH Royal Inst Technol Dept Math Stockholm Sweden Ecole Polytech Fed Lausanne LTS4 Lausanne Switzerland RISE Res Inst Sweden Stockholm Sweden

Incidents where water networks are contaminated with microorganisms or pollutants can result in a large number of infected or ill persons, and it is therefore important to quickly detect, localize and estimate the spread and source of the contamination. In many of today's water networks only limited measurements are available, but with the current internet of things trend the number of sensors is increasing and there is a need for methods that can utilize this information. Motivated by this fact, we address the problem of estimating the spread of pollution in a water network given measurements from a set of sensors. We model the water flow as a Markov chain, representing the system as a set of states where each state represents the amount of water in a specific part of the network, e.g., a pipe or a part of a pipe. Then we seek the most likely flow of the pollution given the expected water flow and the sensors observations. This is a large-scale optimization problem that can be formulated as a Schrodinger bridge problem with partial information, and we address this by exploiting the connection with the entropy regularized multimarginal optimal transport problem. The software EPANET is used to simulate the spread of pollution in the water network and will be used for testing the performance of the methodology.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of European Control Association. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： optimization algorithms Markov processes Sensor and signal fusion Schrodinger bridge

来源：评论

学校读者我要写书评

暂无评论

Saddle-Point Convergence of Constrained Primal-Dual Dynamics

引用

IEEE CONTROL SYSTEMS LETTERS 2021年第4期5卷 1357-1362页

作者： Adegbege, Ambrose A. Kim, Mun Y. Coll New Jersey Dept Elect & Comp Engn Ewing NJ 08628 USA

In this letter, we consider saddle-point convergence of primal-dual dynamics for inequality constrained convex optimization problems. By considering the primal-dual dynamics as the interconnection of (i) a gradient system and (ii) a nonlinear controller with incremental sector-bounded static nonlinearity, we establish asymptotic stability by invoking the classical notions of Lyapunov stability, the invariance principle, along with some regularity assumptions. For the special case of strongly convex problem with affine constraints, we prove global exponential convergence. As compared to existing techniques in the literature, the proposed approach offers simple and transparent tuning guideline for robust exponential stability of the saddle-point dynamics and allows for establishing an upper bound on the exponential decay rate.

关键词： Stability of nonlinear systems optimization algorithms neural networks

来源：评论

学校读者我要写书评

暂无评论

A survey of recently developed metaheuristics and their comparative analysis

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期117卷

作者： Alorf, Abdulaziz Qassim Univ Coll Engn Dept Elect Engn Buraydah 52571 Saudi Arabia

The aim of this study was to gather, discuss, and compare recently developed metaheuristics to understand the pace of development in the field of metaheuristics and make some recommendations for the research community and practitioners. By thoroughly and comprehensively searching the literature and narrowing the search results, we created with a list of 57 novel metaheuristic algorithms. Based on the availability of the source code, we reviewed and analysed the optimization capability of 26 of these algorithms through a series of experiments. We also evaluated the exploitation and exploration capabilities of these metaheuristics by using 50 unimodal functions and 50 multimodal functions, respectively. In addition, we assessed the capability of these algorithms to balance exploration and exploitation by using 29 shifted, rotated, composite, and hybrid CEC-BC-2017 benchmark functions. Moreover, we evaluated the applicability of these metaheuristics on four real-world constrained engineering optimization problems. To rank the algorithms, we performed a nonparametric statistical test, the Friedman mean rank test. Based on the statistical results for the unimodal and multimodal functions, we declared that the GBO, PO, and MRFO algorithms have better exploration and exploitation capabilities. Based on the results for the CEC-BC-2017 benchmark functions, we found the MPA, FBI, and HBO algorithms to be the most balanced. Finally, based on the results for the constrained engineering optimization problems, we declared that the HBO, GBO, and MA algorithms are the most suitable. Collectively, we confidently recommend the GBO, MPA, PO, and HBO algorithms for real-world optimization problems.

关键词： Marine predator algorithm Metaheuristics survey optimization algorithms Political optimizer optimization survey optimization algorithm comparison Engineering optimization

来源：评论

学校读者我要写书评

暂无评论

Modeling and optimal operation of hybrid wave energy and PV system feeding supercharging stations based on golden jackal optimal control strategy

引用

ENERGY 2023年第PartD期263卷

作者： Mahdy, Ahmed Hasanien, Hany M. Turky, Rania A. Aleem, Shady H. E. Abdel Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Elect Engn Dept Cairo 11835 Egypt Sci Valley Acad Valley High Inst Engn & Technol Dept Elect Engn Qalyubia 44971 Egypt

This paper provides a novel application for the Archimedes wave swing (AWS) device that converts the sea waves into electrical energy using a linear permanent magnet synchronous generator (LPMSG), which is connected to a rectifier that extracts the most significant energy from sea waves and reduces the stator losses. The generator's power combined with a photovoltaic (PV) system provides a total of 250 kW, sufficient for feeding a V3 Tesla Supercharging system, which is currently the latest and most advanced electric vehicle (EV) charging technology. The hybrid system is connected to a DC link, its voltage is stabilized using a supercapacitor energy storage system, and the output is provided to a buck converter that reduces the voltage for the Tesla Supercharger. The control system comprises seven proportional-integral (PI) controllers with four anti-wind back-calculation co-efficients to improve transient stability. The PI gains are optimized using the Golden Jackal optimization Al-gorithm (GJOA), and the system stability is evaluated by applying different disturbances like a sudden variation in the DC load, temporary DC short circuit, and connection of the EV in various modes such as a grid to vehicle and vehicle to grid modes. The results obtained by the GJOA are compared with the Particle Swarm optimization and the hybrid Augmented Grey Wolf Optimizer and Cuckoo Search algorithms. Finally, hybrid renewable en-ergy systems can efficiently be employed in EV supercharging stations as validated with the strategy proposed in this work.

关键词： Archimedes wave swing Electric vehicles Hybrid renewable energy systems optimization algorithms Photovoltaic system Wave power application

来源：评论

学校读者我要写书评

暂无评论

Review of Modeling, Modulation, and Control Strategies for the Dual-Active-Bridge DC/DC Converter

引用

ENERGIES 2023年第18期16卷 6646页

作者： He, Jiayang Chen, Yangyu Lin, Jiongtao Chen, Jianghui Cheng, Li Wang, Yu Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Zhongkai Univ Agr & Engn Coll Automat Guangzhou 510225 Peoples R China

This paper provides a comprehensive review of the existing research on the Dual Active Bridge (DAB) DC-DC converter, focusing on modeling methods, modulation strategies, optimization algorithms, and control methods. A comparative analysis of selected methods along with guidelines to assist engineers and researchers in their study of DAB is also presented. Firstly, a comprehensive review of modulation strategies for DAB is provided, ranging from classical phase-shift modulation to the popular asymmetric duty modulation. The intrinsic relationships among different modulation methods are summarized, and a comparison is made based on the difficulty of control and DAB operating characteristics. Secondly, the various modeling methods for DAB are described, including reduced-order modeling, generalized state-space averaging modeling, and discrete-time modeling methods. A comparison is made based on the suitability for different application scenarios, providing recommendations for the adoption of different modeling methods. Furthermore, a survey of optimization algorithms for modulation methods is presented, including classical algorithms, swarm intelligence optimization, and reinforcement learning algorithms. A number of criteria are proposed for different algorithms, and an analysis of the unresolved challenges and future prospects is provided. Finally, the advanced control methods for DAB are summarized based on control effectiveness and applicability. The article concludes with a summary and an outlook on future research directions is also provided.

关键词： dual active bridge (DAB) modeling methods modulation strategies optimization algorithms advanced control methods

来源：评论

学校读者我要写书评

暂无评论

An improved grey wolf optimizer algorithm for identification and location of gas emission

引用

JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 2023年第1期82卷

作者： Liu, Yizhe Jiang, Yu Zhang, Xin Pan, Yong Wang, Jun Nanjing Tech Univ Coll Safety Sci & Engn Jiangsu Key Lab Hazardous Chem Safety & Control Nanjing 211816 Peoples R China

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identifi-cation and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.

关键词： Source term estimation (STE) Swarm optimization method Hazardous gases leakage Grey wolf optimizer (GWO) optimization algorithms Differential evolutionary (DE)

来源：评论

学校读者我要写书评

暂无评论

Improved Population Intelligence Algorithm and BP Neural Network for Network Security Posture Prediction

引用

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2023年第1期2023卷

作者： Li, Yueying Wu, Feng Xinyang Agr & Forestry Univ Coll Informat Engn Xinyang 464000 Peoples R China

To address the problems of low prediction accuracy and slow convergence of the network security posture prediction model, a population intelligence optimization algorithm is proposed to improve the network security posture prediction model of the BP neural network. First, the adaptive adjustment of the two parameters with the increase of iterations is achieved by improving the inertia weights and learning factors in the particle swarm optimization (PSO) algorithm so that the PSO has a large search range and high speed at the initial stage and a strong and stable convergence capability at the later stage. Secondly, to address the problem that PSO is prone to fall into a local optimum, the genetic operator is embedded into the operation process of the particle swarm algorithm, and the excellent global optimization performance of the genetic algorithm is used to open up the spatial vision of the particle population, revive the stagnant particles, accelerate the update amplitude of the algorithm, and achieve the purpose of improving the premature problem. Finally, the improved algorithm is combined with the BP neural network to optimize the BP neural network and applied to the network security posture assessment. The experimental comparison of different optimization algorithms is applied, and the results show that the network security posture prediction method of this model has the smallest error, the highest accuracy, and the fastest convergence, and can effectively predict future changes in network security posture.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

P-FARFAR2: A multithreaded greedy approach to sampling low-energy RNA structures in Rosetta FARFAR2

引用

COMPUTATIONAL BIOLOGY AND CHEMISTRY 2023年第1期104卷 107878页

作者： Youmbi, Franklin Ingrid Kamga Tchendji, Vianney Kengne Djamegni, Clementin Tayou Univ Dschang Dept Math & Comp Sci POB 67 Dschang Cameroon Univ Dschang Dept Comp Engn POB 134 Bandjoun Cameroon

RNA (ribonucleic acid) structure prediction finds many applications in health science and drug discovery due to its importance in several life regulatory processes. But despite significant advances in the close field of protein prediction, RNA 3D structure still poses a tremendous challenge to predict, especially for large sequences. In this regard, the approach unfolded by Rosetta FARFAR2 (Fragment Assembly of RNA with Full-Atom Refinement, version 2) has shown promising results, but the algorithm is non-deterministic by nature. In this paper, we develop P-FARFAR2: a parallel enhancement of FARFAR2 that increases its ability to assemble low-energy structures via multithreaded exploration of random configurations in a greedy manner. This strategy, appearing in the literature under the term "parallel mechanism", is made viable through two measures: first, the synchronization window is coarsened to several Monte Carlo cycles;second, all but one of the threads are differentiated as auxiliary and set to perform a weakened version of the problem. Following empirical analysis on a diverse range of RNA structures, we report achieving statistical significance in lowering the energy levels of ensuing samples. And consequently, despite the moderate-to-weak correlation between energy levels and prediction accuracy, this achievement happens to propagate to accuracy measurements.

关键词： RNA structure prediction Fragment assembly Parallel computing Multicore CPU Greedy algorithms optimization algorithms

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：