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A Monthly Hydropower Scheduling Model of Cascaded Reservoirs with the Zoutendijk Method

WATER 2022年第23期14卷 3978页

作者： Zhou, Binbin Feng, Suzhen Xu, Zifan Jiang, Yan Wang, Youxiang Chen, Kai Wang, Jinwen Yunnan Elect Power Dispatch Control Ctr Kunming 650041 Peoples R China Huazhong Univ Sci & Technol Inst Water Resources & Hydropower 1037 Luoyu Rd Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital River Basin Sci & Technol 1037 Luoyu Rd Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn 1037 Luoyu Rd Wuhan 430074 Peoples R China

A monthly hydropower scheduling determines the monthly flows, storage, and power generation of each reservoir/hydropower plant over a planning horizon to maximize the total revenue or minimize the total operational cost. The problem is typically a complex and nonlinear optimization that involves equality and inequality constraints including the water balance, hydraulic coupling between cascaded hydropower plants, bounds on the reservoir storage, etc. This work applied the Zoutendijk algorithm for the first time to a medium/long-term hydropower scheduling of cascaded reservoirs, where the generating discharge capacity is handled with an iterative procedure, while the other head-related nonlinear constraints are represented with exponential functions fitting to discrete points. The procedure starts at an initial feasible solution, from which it finds a feasible improving direction, along which a better feasible solution is sought with a one-dimensional search. The results demonstrate that the Zoutendijk algorithm, when applied to six cascaded hydropower reservoirs on the Lancang River, worked very well in maximizing the hydropower production while ensuring the highest firm power output to be secured.

关键词： hydropower scheduling Zoutendijk method optimization algorithm large scale power system

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Multi-UAV continuous power inspection scheduling algorithm based on NSGA-III

Multi-UAV continuous power inspection scheduling algorithm b...

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2024 International Conference on Intelligent Robotics and Automatic Control

作者： Liang, Manshu Wu, Wenbin Zhang, Weihao Yao, Shuning State Grid Fujian Elect Power Co Ltd Elect Power Sci Res Inst Fuzhou 350007 Peoples R China

ISBN: (纸本)9798350389814;9798350389807

This paper focuses on addressing the optimization scheduling problem of multi-UAV power inspection tasks. In the context of power inspections, it is often not feasible for a single UAV to complete the inspection due to limitations such as power endurance, UAV hardware constraints, and task settings. Consequently, multiple UAVs are required to perform inspections in a continuous manner. The study models the complex scenario of power line inspection as a multi-objective optimization problem, considering various risk factors associated with UAV operations. By examining these risks, the study proposes a solution based on the Non-dominated Sorting Genetic algorithm III (NSGA-III) algorithm. The proposed approach aims to minimize both the total risk and the maximum risk faced by UAVs during the inspection process. Comparative experiments are conducted to validate the effectiveness of the proposed algorithm. The results demonstrate that, compared to traditional rule-based methods, the NSGA-III algorithm significantly reduces both total risk and maximum risk, ensuring efficient and safe power inspection. Furthermore, the study also considers the failure continuation case, where the remaining UAVs can effectively take over and complete the inspection tasks in case of failure of one UAV. This contributes to the development of more reliable and robust UAV-based power inspection techniques.

关键词： Drones NSGA-III optimization algorithm Inspection Task Allocation continuous power inspection

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Research on Image Denoising algorithm Based on Dictionary Learning 24

Research on Image Denoising Algorithm Based on Dictionary Le...

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3rd International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Lin, Deshu Cheng, Caifeng School of Computer and Information Engineering Guangdong Songshan Polytechnic Guangdong Shaoguan China

ISBN: (纸本)9798400716942

This paper proposes an image denoising algorithm based on dictionary learning, aiming to improve the effectiveness and performance of image denoising. Firstly, we construct a dictionary learning model by sparsely representing the image signal to better represent the noise and detail information in the image signal. Secondly, we use the dictionary learning algorithm to denoise the image by optimizing the atoms and their corresponding coefficients in the dictionary. Experimental results demonstrate that this algorithm has good effectiveness and performance in image denoising. © 2024 ACM.

关键词： Dictionary learning Image denoising optimization algorithm Sparse representation

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Holonic-C2 organization Structure Generation Method Based on Clustering optimization algorithms

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IEEE ACCESS 2019年 7卷 118459-118471页

作者： Wang, Xun Zhang, Jieyong Wan, Lujun Jiao, Zhiqiang Air Force Engn Univ Informat & Nav Coll Xian 710077 Shaanxi Peoples R China Air Force Engn Univ Air Traff Control & Nav Coll Xian 710051 Shaanxi Peoples R China

For Holonic-C2 organization-structure generation, a set of methods based on task-cluster clustering and platform-set optimization are proposed. Initially, the mathematical descriptions of the Holonic-C2 organization components are presented, and the characteristics and advantages of the Holonic-C2 organization are analyzed. To address the critical problems of task cluster and platform-set construction in the organization structure, with the equalization of the task resource requirements and platform resource capabilities, the mathematical models for two-stage clustering optimization are established, respectively. The multi-view clustering optimization and neighborhood search artificial bee colony algorithms are proposed and applied, respectively, for solving these mathematical models. Finally, Monte Carlo simulations are performed, and the obtained results demonstrate the effectiveness of the proposed models and algorithms.

关键词： Artificial bee colony algorithm holonic-C2 organization K-means multi-view clustering neighborhood search optimization algorithm resource balance

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Study of the optimal orientation of vertical surfaces in different thermal floors in Colombia for solar

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SOLAR ENERGY 2022年 239卷 25-32页

作者： Ospina-Metaute, Cristian C. Medina-Garzon, Luis F. Betancur, Esteban Marulanda-Bernal, Jose Ignacio Univ EAFIT Phys Sci Dept Carrera 49 7 Sur-50 Medellin Colombia Univ EAFIT Design Engn Res Grp GRID Medellin Colombia

The use of solar energy incident on vertical surfaces in building integrated photovoltaic (BIPV) systems is shown as an opportunity to contribute to achieve sustainable energy consumption in buildings in urban spaces. In this study, a computational analysis of the optimal orientation for vertical surfaces in three thermal floors of Colombia is performed, using clear sky conditions and meteorological data. The results show the need to position the designs of vertical surfaces in a range of azimuthal angles given the energy variability for each city, both in the morning and evening hours and throughout the years. In addition, the energy generation potential of vertical surfaces is analyzed with respect to a horizontal panel considering the maximum radiant energy received, finding that a single vertical surface can receive 17% less radiant energy than a horizontal panel, but when considering a pair oriented according to the optimal angles, it can be exceeded up to 60%.

关键词： BIPV Vertical PV Photovoltaic Solar energy Vertical fa?ades optimization algorithm

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General Design Method of Control Law for Adaptive Cycle Engine Mode Transition

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AIAA JOURNAL 2022年第1期60卷 330-344页

作者： Zhang, Jiyuan Dong, Pengcheng Tang, Hailong Zheng, Junchao Wang, Jingkai Chen, Min Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China Aero Engine Acad China Beijing 101304 Peoples R China Beihang Univ Res Inst Aeroengine Beijing 102206 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China AVIC Shenyang Engine Res Inst Shenyang 110015 Liaoning Peoples R China Beihang Univ Beijing 100191 Peoples R China

Mode transition (MT) is critical for adaptive cycle engine (ACE) to achieve mission adaptability. There are many constraints for the MT, making the control law difficult to design. At present, most design methods require manual analysis by researchers. When the design parameters of an engine are changed, it takes a lot of time to redesign the control law. What is more, most control laws need to adjust not only the mode selection valve (MSV), but also many other variable geometry components (VGCs) simultaneously, which makes the control system design complex and difficult. In this study, a general design method based on the particle swarm optimization method and the sensitivity calculation method is presented. This method can derive the control law for the MT without manual analysis, improving the efficiency of the design process. At most, one VGC needs to be adjusted during the change of state of the MSV, simplifying the control system. The fluctuation of thrust and airflow is less than 2%, and the surge margin of the compressors is higher than 10%, leading to a safe and stable MT. This method can adapt to the adjustment of the cycle parameters of the engine and the deviation of component performance, making it suitable for the engine performance design stage. It can also be applied to the design for other types of ACEs.

关键词： Particle Swarm optimization Control System Design High Pressure Turbine Variable Cycle Engines Compression Ratio Flow Patterns Adaptive Engine Technology Development optimization algorithm Conservation of Energy Turbofan

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Optimal energy management strategy for a renewable-based microgrid considering uncertainty

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年第2期44卷 3273-3293页

作者： Natani, Mohammad Ali Hosseini, Seyed Mohammad Hassan Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran *** Iran

Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively.

关键词： Microgrid optimization algorithm renewable resources energy storage

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Brain tumor segmentation using river formation dynamics and active contour model in magnetic resonance images

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NEURAL COMPUTING & APPLICATIONS 2022年第14期34卷 11807-11816页

作者： Pruthi, Jyotika Arora, Shaveta Khanna, Kavita NorthCap Univ Dept Comp Sci & Engn Sect 23A Gurgaon 122017 Haryana India

The human brain is quite complex in structure due to which it becomes quite challenging for a radiologist to differentiate tumor from normal tissues, blood clots, and edema. This paper presents a technique to segment the brain tumor from magnetic resonance images using the river formation dynamics (RFD) algorithm and active contour model. The brain tumor segmentation problem is modeled as a combinatorial optimization problem. It searches the tumor boundary using the active contour model which further uses RFD to search the optimized path in a region. RFD is heuristic optimization algorithm that mimics the way the water leads to the formation of rivers through erosion of ground and deposition of sediments. As a result, the best possible boundary with the minimum value of energy function is obtained. The technique has been evaluated quantitatively and qualitatively on the BrainWeb dataset. The results indicate the remarkable improvement over a few metaheuristic techniques, namely ant colony optimization algorithm, bacterial foraging optimization, particle swarm optimization algorithm, genetic algorithm, firefly algorithm, and cuckoo search optimization algorithm in terms of specificity, sensitivity, dice index, Hausdorff distance, Jaccard index, and accuracy. The presented approach gives continuous and smooth contours with an accuracy of 98.1% and is computationally faster in comparison to other metaheuristic techniques.

关键词： Active contour model Brain tumor Brain MRI optimization algorithm River formation dynamics algorithm Image segmentation

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Distributed Unmanned Aerial Vehicle Positioning for Multitarget Tracking with Bearing-Only Information

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2022年第5期19卷 355-365页

作者： Yang, Xiwen He, Shaoming Lin, Defu Wang, Yadong Beijing Inst Technol Sch Aerosp Engn 5th Zhongguancun South St Beijing 100081 Peoples R China AIAA Reston VA 20191 USA

This paper proposes a distributed positioning algorithm for a swarm of unmanned aerial vehicles (UAVs) to track multiple moving targets with bearing-only measurements. An approximate performance metric is first derived that can be used in position determination based on the properties of multisensor joint probabilistic data association (JPDA) filter. A fully distributed position planning algorithm using incremental optimization strategy is then proposed for tracking multiple moving targets. Simulation examples with comparison results validate the effectiveness of the proposed algorithm.

关键词： UAV Trajectory optimization Monte Carlo Experiment Multisensor Fusion Poisson Distribution Extended Kalman Filter Mathematical Models Consensus algorithm Heading Angle optimization algorithm

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Collaborative scheduling of spare parts production and service workers driven by distributed maintenance demand

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JOURNAL OF MANUFACTURING SYSTEMS 2022年 64卷 261-274页

作者： Miao, Bingxin Deng, Qianwang Zhang, Like Huo, Zhangwen Liu, Xiaoyan Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China 2 South Lushan Rd Changsha 410082 Hunan Peoples R China

We analyse the joint scheduling of spare parts production and service workers driven by distributed maintenance demand. For each failure, a service worker and a necessary spare part must be assigned for on-site maintenance. In particular, the spare part is not supplied from stock, but is delivered directly from the factory to the maintenance site through just-in-time production. Spare parts production and worker allocation are integrated to optimize two objectives: customer satisfaction and total service cost. To solve the problem, we first construct a mathematical model. Subsequently, a modified non-dominated neighbor immune algorithm (MNNIA) is proposed, in which a knowledge-based initialization rule, an idle time insertion method, and two problem-specific local search operators are developed to enhance exploitation. Extensive experiments and comparisons are conducted to demonstrate the superiority of MNNIA. Furthermore, compared with that of the separate decisionmaking mode of spare parts production and worker arrangement, the effectiveness of the integrated scheduling mode is verified.

关键词： Maintenance Collaborative scheduling Spare parts production Service workers optimization algorithm

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