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Reliable Islanded Microgrid Operation Using Dynamic Optimal Power Management

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第2期57卷 1755-1766页

作者： Harasis, Salman Sozer, Yilmaz Elbuluk, Malik Univ Akron Dept Elect & Comp Engn Akron OH 44325 USA

The tradeoff between the operating cost and the reliability of microgrids imposes challenges of having cost-effective power dispatch and reliable operations. The work presented in this article optimally solves this issue in a microgrid that contains photovoltaic (PV), dispatchable generator, and storage battery. Flexible control algorithms are developed to enable high penetration of PV power in islanded microgrids such that the challenge of having a cost-effective and reliable operation can be mitigated. This is achieved by creating a power flow optimization framework that considers all variables associated with the system operation. Particle swarm optimization is utilized to effectively optimize the power flow among the integrated sources and to dynamically schedule the battery state of charge. The proposed controller of each source works to fulfill the requirements of the optimization. The energy index of reliability is employed as a direct measure to quantify the reliability level of the microgrid. The effectiveness of the proposed approach is demonstrated by simulations and validated by experimental studies.

关键词： Batteries Microgrids Reliability State of charge optimization Generators Control systems Economic power-sharing microgrid optimal battery state of charge (SOC) optimal power optimization algorithm particle swarm optimization (PSO) photovoltaic (PV) power flow reliability

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Prediction and analysis of domestic water consumption based on optimized grey and Markov model

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WATER SUPPLY 2021年第7期21卷 3887-3899页

作者： Wang, Zhaocai Wu, Xian Wang, Huifang Wu, Tunhua Shanghai Ocean Univ Coll Informat Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Food Shanghai 201306 Peoples R China Wenzhou Business Coll Sch Informat Engn Wenzhou 325035 Peoples R China

With the rapid development of urbanization and the continuous improvement of living standards, China's domestic water consumption shows a growing trend. However, in some arid and water deficient areas, the shortage of water resources is a crucial factor affecting regional economic development and population growth. Therefore, it is essential important to reliably predict the future water consumption data of a region. Aiming at the problems of poor prediction accuracy and overfitting of non-growth series in traditional grey prediction, this paper uses residual grey model combined with Markov chain correction to predict domestic water consumption. Based on the traditional grey theory prediction, the residual grey prediction model is established. Combined with the Markov state transition matrix, the grey prediction value is modified, and the model is applied to the prediction of domestic water consumption in Shaanxi Province from 2003 to 2019. The fitting results show that the accuracy grade of the improved residual grey prediction model is "good". This shows that the dynamic unbiased grey Markov model can eliminate the inherent error of the traditional grey GM (1,1) model, improve the prediction accuracy, have better reliability, and can provide a new method for water consumption prediction.

关键词： grey model Markov chain optimization algorithm state transition matrix water consumption prediction

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Research on 3D Laser Scanning Monitoring Method for Mining Subsidence Based on the Auxiliary for Probability Integral Method

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KSCE JOURNAL OF CIVIL ENGINEERING 2021年第11期25卷 4403-4416页

作者： Lei Wang Zhu Shangjun Jiang Chuang Li Jingyu Jiang Kegui Teng Chaoqun Wei Tao Guo Qing-biao Anhui Univ Sci & Technol Sch Geomat Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Peoples R China China Univ Min & Technol Sch Environm & Geoinformat Xuzhou 221116 Jiangsu Peoples R China

When 3D laser scanning technology is used to monitor the surface deformation of mining subsidence in mining area, the surface of the working face is covered with a large number of vegetation, and the surface water accumulates above the working face, which makes the point cloud data obtained by the 3D laser scanning difficult to denoise, or even missing. At this time, the conventional 3D laser scanning technology can not obtain the surface deformation field of mining subsidence. Aiming at the above problems, the 3D laser scanning monitoring method for mining subsidence based on the auxiliary for PIM proposed (3DLS-PIM). Firstly, this paper introduces the PIM prediction model. Secondly, the mining subsidence observation equation based on 3DLS-PIM is constructed, and then the prediction parameters of PIM are solved based on quantum particle swarm optimization (QPSO). Finally, according to the PIM and its parameters, the mining subsidence surface deformation basin is predicted and obtain the surface deformation field of mining subsidence. Robust experiments show that QPSO has a certain ability to resist random errors and gross errors. The results of engineering application show that the mining area 3D deformation monitoring method proposed in this paper has certain engineering application value.

关键词： Mining subsidence Probability integral method 3D laser scanning technology Quantum particle swarm optimization algorithm Deformation monitoring

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Group-based multiple pipe routing method for aero-engine focusing on parallel layout

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Frontiers of Mechanical Engineering 2021年第4期16卷 798-813页

作者： Hexiang YUAN Jiapeng YU Duo JIA Qiang LIU Hui MA School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System(Ministry of Education) Northeastern UniversityShenyang 110819China Shenyang Engine Research Institute of AECC Shenyang 110015China School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China

External pipe routing for aero-engine in limited three-dimensional space is a typical nondetermini-stic polynomial hard problem,where the parallel layout of pipes plays an important role in improving the utilization of layout space,facilitating pipe assembly,and *** paper presents an automatic multiple pipe routing method for aero-engine that focuses on parallel *** compressed visibility graph construction algorithm is proposed first to determine rapidly the rough path and interference relationship of the pipes to be *** on these rough paths,the information of pipe grouping and sequencing are obtained according to the difference degree and interference degree,***,a coevolutionary improved differential evolution algorithm,which adopts the coevolutionary strategy,is used to solve multiple pipe layout optimization *** using this algorithm,pipes in the same group share the layout space information with one another,and the optimal layout solution of pipes in this group can be obtained in the same evolutionary ***,to eliminate the minor angle deviation of parallel pipes that would cause assembly stress in actual assembly,an accurate parallelization processing method based on the simulated annealing algorithm is ***,the simulation results on an aero-engine demonstrate the feasibility and effectiveness of the proposed method.

关键词： multiple pipe routing optimization algorithm aero-engine pipe grouping parallel layout

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Management of Energy Storage in Transactive Energy Communities 5

Management of Energy Storage in Transactive Energy Communiti...

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5th International Conference on Smart Energy Systems and Technologies (SEST)

作者： Soares, Ana Goncalves, Goncalo Moura, Pedro Univ Coimbra Elect & Comp Engn Dept INESC Coimbra Coimbra Portugal Univ Coimbra Elect & Comp Engn Dept Coimbra Portugal Univ Coimbra ISR Coimbra Elect & Comp Engn Dept Coimbra Portugal

ISBN: (数字)9781665405577

ISBN: (纸本)9781665405577

The energy sector has been evolving towards a more sustainable model in which renewables, energy storage systems and energy communities play an important role in a successful energy transition. In the near future, a large share of small-scale renewable distributed generation is expected to be deployed in buildings, leading to a mismatch between generation and demand which needs to be addressed with energy flexibility options. In such a context, the use of energy management systems endowed with adequate control algorithms will be crucial for leveraging the spatio-temporal flexibility of controllable energy resources. This paper proposes a framework for the optimization of energy storage in a transactive energy community on a Portuguese University Campus. The used methodology ensures economic benefits with the management of dedicated storage systems coupled with renewable generation systems. The considered constraints include mechanisms to encourage renewable generation surplus sharing within the community and that export and import power flows between the building and the community are limited to the net energy demand in the building, maximum (dis)charging power of the storage systems, the minimum and maximum state of charge, among others. Results showed that the proposed framework achieves an increase in renewable self-consumption at building and community levels, as well as a reduction in electricity costs.

关键词： Battery storage system community storage energy communities optimization algorithm transactive energy

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optimization of Elastic Properties of Composite Laminates Using Fiber-Optic Strain Sensors

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APPLIED COMPOSITE MATERIALS 2020年第5期27卷 491-509页

作者： Tashkinov, Mikhail A. Matveenko, Valeriy P. Perm Natl Res Polytech Univ Komsomolsky Ave 29 Perm 614990 Russia Russian Acad Sci Inst Continuous Media Mech Academician Korolev St 1 Perm 614013 Russia

The paper is devoted to combined computational and experimental approach for estimation of the elastic mechanical properties of structures made of laminate polymer composite materials (PCM). The computational component of the technique is connected with numerical simulation of mechanical behavior during quasistatic deformation of structures made of PCM. The experimental component is based on measurement of strains by fiber-optic strain sensors (FOSS) with Bragg gratings (FBG sensors), embedded in composite laminates or attached to them. The principle of the proposed method is based on the comparison of the data from FBG sensors, placed in the predetermined control points in the composite structure, with the data of numerical finite element modelling of the same structure. To refine the elastic constants in accordance with the information received from the FBG sensors, an algorithm is proposed, according to which the inverse problems are solved in order to ensure that the numerical and experimental results coincide with the specified accuracy. The implementation of the algorithm is demonstrated on the example case studies.

关键词： Mechanical characteristics Fiber-optic strain sensors Bragg gratings optimization algorithm Finite element method

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Formation optimization of blockchain-assisted swarm robotics systems against failures based on energy balance

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SIMULATION MODELLING PRACTICE AND THEORY 2022年 120卷

作者： Wang, Kunpeng Liu, Zhongren Zhu, Zhiqin Qi, Guanqiu Yao, Juan Miao, Guozhen Southwest Univ Sci & Technol Sch Informat & Engn Mianyang 621010 Peoples R China Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China SUNY Buffalo Comp Informat Syst Dept Buffalo NY 14222 USA Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Hebrew SeniorLife Hinda & Arthur Marcus Inst Aging Boston MA 02131 USA Maranatha High Sch Pasadena CA 91105 USA

The blockchain-assisted swarm robotics system, as an emerging distributed multi-robot system, has been widely discussed and studied for its outstanding advantages in secure communication, data logging and consensus achievement. Due to faults, formation changes, and obstructions, the network topology and communication rate change in the process of multi-robot collab-oration, which inevitably affect the stability and operation efficiency of the swarm robotics system. However, few studies have quantitatively assessed the degradation of system stability performance after failure and how to respond to failure of team members and optimize the system for failure. In this paper, the concept of "energy"is introduced into the quantitative evaluation of system failure resistance, and an evaluation model based on communication distance, communication quality and communication volume is proposed, which can evaluate the stability of swarm robotics systems at different moments and any member in real-time collaboration. As the stability improves, the "energy"increases, and vice versa. Additionally, the "energy"balance is applied to the optimization process of multi-robot formation, and the optimization equation based on the efficiency of swarm robots is proposed. The optimal formation efficiency equation of the system at a certain moment is obtained by jointly solving the energy optimization equation and the system. The simulation results show that the proposed formation optimization method can improve the stability of the swarm robotics system at any time and in all directions, and maximize the operational efficiency of the queue.

关键词： Swarm robotics system Robot Formation optimization Energy balance Blockchain technology optimization algorithm

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Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain

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REMOTE SENSING 2021年第1期13卷 101-101页

作者： Golilarz, Noorbakhsh Amiri Gao, Hui Pirasteh, Saied Yazdi, Mohammad Zhou, Junlin Fu, Yan Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Southwest Jiaotong Univ Fac Geosci & Environm Engn Chengdu 611756 Peoples R China Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal

The presence of noise in remote sensing satellite images may cause limitations in analysis and object recognition. Noise suppression based on thresholding neural network (TNN) and optimization algorithms perform well in de-noising. However, there are some problems that need to be addressed. Furthermore, finding the optimal threshold value is a challenging task for learning algorithms. Moreover, in an optimization-based noise removal technique, we must utilize the optimization algorithm to overcome the problem. These methods are effective at reducing noise but may blur some parts of an image, and they are time-consuming. This flaw motivated the authors to develop an efficient de-noising method to discard un-wanted noises from these images. This study presents a new enhanced adaptive generalized Gaussian distribution (AGGD) threshold for satellite and hyperspectral image (HSI) de-noising. This function is data-driven, non-linear, and it can be fitted to any image. Applying this function provides us with an optimum threshold value without using any least mean square (LMS) learning or optimization algorithms. Thus, it is possible to save the processing time as well. The proposed function contains two main parts. There is an AGGD threshold in the interval [-sigma(n), sigma(n)], and a new non-linear function behind the interval. These combined functions can tune the wavelet coefficients properly. We applied the proposed technique to various satellite remote sensing images. We also used hyperspectral remote sensing images from AVIRIS, HYDICE, and ROSIS sensors for our experimental analysis and validation process. We applied peak signal-to-noise ratio (PSNR) and Mean Structural Similarity Index (MSSIM) to measure and evaluate the performance analysis of different de-noising techniques. Finally, this study shows the superiority of the developed method as compared with the previous TNN and optimization-based noise suppression methods. Moreover, as the results indicate, the pr

关键词： adaptive enhanced AGGD image de-noising optimization algorithm remote sensing TNN AVIRIS sensor HYDICE sensor ROSIS sensor

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Flexible job-shop scheduling and heterogeneous repairman assignment with maintenance time window and employee timetable constraints

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115693-115693页

作者： An, Youjun Chen, Xiaohui Li, Yinghe Zhang, Ji Jiang, Junwei Chongqing Univ State Key Lab Mech Transmiss Chongqing 400030 Peoples R China

This work focuses on a new flexible scheduling problem in the job-shop that considers both maintenance activity and repairman competence under maintenance time window and employee timetable constraints. To deal with this problem, a hybrid multi-objective biogeography-based optimization (HMOBBO) algorithm is proposed, which has the following features: (1) a flexible decoding mechanism that considers maintenance time window and heterogeneous repairman constraints is designed;(2) three calculation methods of habitat suitability index (HSI) are defined;(3) tabu search (TS) algorithm is incorporated into the presented algorithm;and (4) a new offspring population generation mechanism is constructed. In numerical simulation, the parameter setting is firstly analyzed to ensure its robustness for different datasets via comparing the performance of each critical parameter combination. Secondly, different HSIs and migration models are separately compared through multiply running the literature instances, it is shown that fitness function 2 (the reciprocal of the sum of the normalized objective function values) and migration model 1 (constant immigration and linear emigration model) are the most suitable and steady in our experiments. Thirdly, the superiority of HMOBBO is proved by confronting with other six intelligent algorithms. Finally, through contrast variant models, the significance of considering employee timetable and repairman assignment is verified, and the benefits of the integrated optimization model/method are also demonstrated by comparing with hierarchical optimization model/method.

关键词： Flexible job-shop scheduling Heterogeneous repairman assignment Maintenance time window Employee timetable Hybrid multi-objective biogeography-based optimization algorithm

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Optimal Dynamic Recharge Scheduling for Two-Stage Wireless Power Transfer

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第8期17卷 5719-5729页

作者： Pandiyan, Akshayaa Y. S. Boyle, David E. Kiziroglou, Michail E. Wright, Steven W. Yeatman, Eric M. Imperial Coll London Dept Elect & Elect London SW7 2AZ England Int Hellenic Univ Dept Ind Engn & Management Nea Moudania 57001 Greece Imperial Coll London Dyson Sch Design Engn London SW7 2AZ England

Many industrial-Internet-of-Things applications require autonomous operation and incorporate devices in inaccessible locations. Recent advances in wireless power transfer (WPT) and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded devices. Equipping inexpensive unmanned aerial vehicles (UAV) and embedded devices with subsystems to facilitate WPT allows a UAV to become a viable mobile power delivery vehicle (PDV) and data collection agent. A key challenge is, therefore, to ensure that a PDV can optimally schedule power delivery across the network, such that it is as reliable and resource efficient as possible. To achieve this and out-perform naive on-demand recharging strategies, in this article, we propose a two-stage wireless power network (WPN) approach in which a large network of devices may be grouped into small clusters, where packets of energy inductively delivered to each cluster by the PDV are acoustically distributed to devices within the cluster. In this article, we describe a novel dynamic recharge scheduling algorithm that combines genetic weighted clustering with nearest neighbor search to jointly minimize PDV travel distance and WPT losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces, and the algorithm is shown to achieve similar to 90% throughput for large, dense networks.

关键词： Dynamic recharge scheduling genetic algorithm (GA) optimization algorithm wireless power network (WPN) wireless rechargeable sensor network

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