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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)

作者： H. Huang S. Huang W. Qin Y. Zhang Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology (HKL-MESI2T) People's Republic of China

Abstract–Due to the special structure of distributed power system on UAV and its critical reliability requirements, it is necessary to build an appropriate reliability evaluation method. This paper presents a reliability evaluation method based on N-K cascading fault model. The method is based on the simulation of power system fault, the calculation of DC power flow, and the analyzing of load shedding under different fault combinations. In order to validate the proposal, case studies are implemented on a solar UAV, where classic topology schemes of distributed power system are evaluated. The results show that the evaluation index is reasonable and the proposed method greatly reduces the computational complexity, thus providing a promising reliability evaluation tool for the optimization of grid topology.

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Path planning model for UAV collaborative search task Based on NGA

Path planning model for UAV collaborative search task Based ...

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第33届中国控制与决策会议

作者： Hong Huang Shengjun Huang Weijian Qin Huihui He Tao Zhang College of Systems Engineering National University of Defense Technology Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology (HKL-MSI2T)

To solve the problem of large search demand and insufficient search ability of UAVs,this article has proposed a task planning method of UAV group based on nested learning *** to the characteristics of the target path,the method combines task allocation strategy and path planning algorithm to make *** method can give reasonable suggestions on the number of UAVs,and effectively improve the efficiency of UAV group search *** algorithm uses the elite individual selection strategy based on tournament selection method to improve the optimization *** the algorithm uses neighborhood method to avoid local ***,the algorithm is verified by the data of a search *** experimental results show that the planning method used in this paper is suitable for the UAVs' cooperative search *** with other planning methods,it has faster solution speed and is conducive to emergency *** method also has reference value for the cooperative planning of search tasks.

关键词： Nested genetic algorithms TSP Neighborhood method Path planning

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Generalized Benders Decomposition Algorithm Incorporating Matheuristics to Solve Large-Scale Power Facility Location Problems

Generalized Benders Decomposition Algorithm Incorporating Ma...

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IEEE Conference on energy Internet and energy system Integration (EI2)

作者： Kang Yang Wei Liu Jinyuan Mo Wenhua Li Tao Zhang Rui Wang College of Systems Engineering National University of Defense Technology Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology (HKL-MESI2T) Changsha China

With the change of energy structure and the development of energy internet technology, the penetration rate of distributed generation is gradually increasing, and the location of distributed generation has an important impact on the distribution network line losses. In this paper, a mixed integer quadratic programming model with separability is considered to optimize the location of distributed generation and to reduce line losses in distribution networks, which measures line losses in distribution networks in terms of the quadratic term of current. In order to solve the problem of large-scale distributed power facility location problems with separability, a Benders decomposition algorithm incorporating matheuristics is proposed, which first decomposes the original problem into a mixed-integer linear programming master problem and a convex quadratic subproblem with separability, and then separates the generalized Benders cuts form solving the subproblem at the root node relaxed solution of the master problem. A simple in-out Stabilization technique is applied in order to efficiently raise the lower bound of the master problem at the root node. In order to avoid the use of branch-and-cut algorithms to solve the master problem to obtain optimal feasible solutions, two matheuristic repair strategies are proposed for the root node relaxation solution to obtain high-quality feasible solutions. Experimental results on 90 large-scale power facility location problems show that matheuristic repair strategies for the root node relaxation solution can find the global optimal solution.

关键词： Current measurement Distribution networks system integration Maintenance engineering Programming Loss measurement Distributed power generation

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Scheduling and Routing of Mobile Charging Vehicles for Unmanned Aerial Vehicles Charging

Scheduling and Routing of Mobile Charging Vehicles for Unman...

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IEEE Conference on energy Internet and energy system Integration (EI2)

作者： Weijian Qin Tao Zhang Zhichao Shi Hong Huang Huihui He Wenhua Li College of Systems Engineering Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology (HKL-MSI2T) National University of Defense Technology Changsha China

ISBN: (纸本)9781665434263

Battery capacity of Unmanned Aerial Vehicle (UAV) limits its ability to perform long-term reconnaissance missions. This paper proposes a model of Mobile Charging Vehicles (MCVs) to charge UAVs on missions. In this scenario, the UAV can land on top of the MCV and move together along its original route. Scheduling and routing of MCVs are optimized to minimize the investment cost and maximize the quality of mission completion while satisfying time- and power-related constraints. For this multi-objective optimization problem, an improved decomposition-based multi-objective evolutionary algorithm (MOEA/D) is adopted to solve it. Finally, the effectiveness of this model is verified by numerical experiments.

关键词： Costs Power demand system integration Reconnaissance Pareto optimization Autonomous aerial vehicles Routing

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Deep Reinforcement Learning for Orienteering Problems Based on Decomposition

arXiv

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arXiv 2022年

作者： Liu, Wei Zhang, Tao Wang, Rui Li, Kaiwen Li, Wenhua Yang, Kang The College of System Engineering National University of Defense Technology Changsha410073 China The Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology HKL-MSI2T Changsha410073 China

This paper presents a new method for solving an orienteering problem (OP) by breaking it down into two parts: a knapsack problem (KP) and a traveling salesman problem (TSP). A KP solver is responsible for picking nodes, while a TSP solver is responsible for designing the proper path and assisting the KP solver in judging constraint violations. To address constraints, we propose a dual-population coevolutionary algorithm (DPCA) as the KP solver, which simultaneously maintains both feasible and infeasible populations. A dynamic pointer network (DYPN) is introduced as the TSP solver, which takes city locations as inputs and immediately outputs a permutation of nodes. The model, which is trained by reinforcement learning, can capture both the structural and dynamic patterns of the given problem. The model can generalize to other instances with different scales and distributions. Experimental results show that the proposed algorithm can outperform conventional approaches in terms of training, inference, and generalization ability. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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An LSTM-Based Approach For Capacity Estimation on Lithium-ion Battery

An LSTM-Based Approach For Capacity Estimation on Lithium-io...

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第33届中国控制与决策会议

作者： Mengda Cao Yajun Zhang Jianjiang Hui Yajie Liu College of Systems Engineering National University of Defense Technology Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology (HKL-MSI2T) Beijing Institute of Tracking and Telecommunication Technology

With the wide application of lithium-ion batteries,the safety and stability of batteries have attracted much attention in recent *** capacity estimation can help understand the health status of lithium-ion ***-based methods can perform well on capacity estimation with enough prior ***,the internal electrochemical mechanism of lithium-ion battery degradation is so complicated that the cost of prior knowledge is usually *** to no need for prior knowledge,data-driven methods introduce new solutions to solve this *** these,deep learning methods(DL) show great potential for their advantages of high-dimensional feature *** this paper,an LSTM(Long Short-Term Memory) network model is proposed to estimate the capacity,in which seven time-related health features in the charge stage are used as input of the *** the one hand,health features extracted from charge time data can decrease the difficulties in data *** the other hand,the LSTM model has the ability to learn the long-term dependencies between data so that suit for constructing mapping relationship between health features and *** validation experiments are conducted based on two simulation *** results show that the proposed model can accurately track the capacity recovery effect and estimate the RUL of lithium-ion batteries.

关键词： Capacity Estimation Lithium-ion Battery Deep Learning(DL) Long short-term memory(LSTM)

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Helper Objective Assisted Evolutionary Algorithm for multi-modal Optimization

Helper Objective Assisted Evolutionary Algorithm for Multi-m...

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Congress on Evolutionary Computation

作者： Xu Yang Rui Wang Wenhua Li College of Systems Engineering National University of Defense Technology Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology (HKLMESI2T) Hangsha P.R. China

Evolutionary algorithms (EAs) have been widely applied in various optimization problems. However, EAs are found as less effective on multi-modal optimization due to their multiple local optima. Inspired by the idea of self-paced learning, that is, problems can be solved step by step, from easy to difficult. In this study, we propose to design a helper objective function to assist the optimization of multi-modal problems via multitask optimization framework. In principle the helper objective function shares common features with the original function but is easier to solve. Thus, the information gained by solving the helper objective can be utilized to tackle the multi-modal problems. Specifically, the Gaussian process is applied to build the helper objective function. The multi-factorial evolutionary algorithm is applied to optimize the helper and original objective functions simultaneously. Experimental results show that the idea is effective on a set of multi-modal optimization benchmarks.

关键词： Computational modeling Evolutionary computation Gaussian processes Benchmark testing Linear programming Optimization

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Enhancement of Photo-Thermal Conversion Performance of the Nanofluids Through Spectral Complementarity between Silver and Cesium Tungstate Oxide Nanoparticles

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Journal of Thermal Science 2020年第5期29卷 1322-1332页

作者： LIU Changqing ZHANG Liy HE Yan Yu Wei School of Mechanical and Energy Engineering Shaoyang UniversityShaoyang 422000China Key Laboratory of Hunan Province for Efficient Power System and Intelligent Manufacturing Shaoyang UniversityShaoyang 422000China College of Engineering Shanghai Polytechnic UniversityShanghai 201209China School of Mechanical and Electrical Engineering Qingdao University of Science and TechnologyQingdao 266061China

Nanofluids with full-spectrum absorption properties are highly desirable for direct solar thermal energy conversion *** this work,Ag and CsW03 nanofluids,which exhibit absorption both in the visible and near-infrared(NIR)region,are integrated to obtain two>component hybrid *** hybrid nanofluids show broad band absorption with a solar weighted absorption fraction of 99.6%,compared to 18%and 54%for the base liquid(ethylene glycol)and CsW03 nanofluids,*** highest photo-thermal conversion performance for the hybrid nanofluids is obtained with Ag/CsW03 weight ratio of 3/*** solar thermal conversion efficiency of the optimum hybrid nanofluids is 67%,10%and 15%higher than single Ag and CsW03 *** two-component hybrid nanofluid provides an alternative for making the best use of solar energy.

关键词： photo-thermal conversion nanofluids Ag cesium tungsten oxide near-infrared

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multimodal multi-objective Optimization: Comparative Study of the State-of-the-Art

arXiv

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arXiv 2022年

作者： Li, Wenhua Zhang, Tao Wang, Rui Huang, Shengjun Liang, Jing The College of Systems Engineering National University of Defense Technology Changsha China Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha410073 China The School of Electrical Engineering Zhengzhou University Zhengzhou China

multimodal multi-objective problems (MMOPs) commonly arise in real-world problems where distant solutions in decision space correspond to very similar objective values. To obtain more Pareto optimal solutions for MMOPs, many multimodal multi-objective evolutionary algorithms (MMEAs) have been proposed. For now, few studies have encompassed most of the recently proposed representative MMEAs and made a comparative comparison. In this study, we first review the related works during the last two decades. Then, we choose 12 state-of-the-art algorithms that utilize different diversity-maintaining techniques and compared their performance on existing test suites. Experimental results indicate the strengths and weaknesses of different techniques on different types of MMOPs, thus providing guidance on how to select/design MMEAs in specific scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： multiobjective optimization

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Coevolutionary Framework for Generalized multimodal multi-objective Optimization

arXiv

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arXiv 2022年

作者： Li, Wenhua Yao, Xingyi Li, Kaiwen Wang, Rui Zhang, Tao Wang, Ling College of Systems Engineering National University of Defense Technology Changsha China Hunan Key Laboratory of Multi-energy System Intelligent Interconnection Technology Changsha410073 China The Department of Automation Tsinghua University Beijing100084 China

Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). However, in real-world problems, decision makers (DMs) may be also interested in local PSs. Also, searching for both global and local PSs is more general in view of dealing with MMOPs, which can be seen as a generalized MMOP. In addition, current state-of-the-art MMEAs exhibit poor convergence on high-dimension MMOPs. To address the above two issues, we present a novel coevolutionary framework (CoMMEA) to better produce both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs. Specifically, the CoMMEA introduces two archives to the search process, and coevolves them simultaneously through effective knowledge transfer. The convergence archive assists the CoMMEA to quickly approaching the Pareto optimal front (PF). The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the ∊-dominance-based method to obtain global and local PSs effectively. Experimental results show that CoMMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs. Copyright © 2022, The Authors. All rights reserved.

关键词： multiobjective optimization

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