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The Intelligent Sizing Method for Renewable Energy Integrated Distribution Networks

ENERGIES 2024年第22期17卷 5763-5763页

作者： Yang, Zhichun Yang, Fan Liu, Yu Min, Huaidong Zhou, Zhiqiang Zhou, Bin Lei, Yang Hu, Wei State Grid Hubei Co Ltd Elect Power Res Inst Wuhan 430037 Peoples R China State Grid Hubei Elect Power Co Ltd Wuhan 430037 Peoples R China

The selection of the optimal 35 kV network structure is crucial for modern distribution networks. To address the problem of balancing investment costs and reliability benefits, as well as to establish the target network structure, firstly, the investment cost of the distribution network is calculated based on the determined number of network structure units. Secondly, reliability benefits are measured by combining the comprehensive function of user outage losses with the System Average Interruption Duration Index (SAIDI). Then, a multi-objective planning model of the network structure is established, and the weighted coefficient transformation method is used to convert reliability benefits and investment costs into the total cost of power supply per unit load. Finally, by using the influencing factors of the network structure as the initial population and setting the minimum total cost of the unit load as the fitness function, the DE algorithm is employed to obtain the optimal grid structure under continuous load density intervals. Case studies demonstrate that different load densities correspond to different optimal network structures. For load densities ranging from 0 to 30, the selected optimal network structures from low to high are as follows: overhead single radial, overhead three-section with two ties, cable single ring network, and cable dual ring network.

关键词： network structure investment cost differential evolution algorithm network structure planning reliability benefit

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Optimal Voyage and Power Dispatch of All-Electric Ship Using Improved Piecewise Linearization Method 14

Optimal Voyage and Power Dispatch of All-Electric Ship Using...

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14th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Gao, Jinglie Lan, Hai Yin, He Li, Dan Lu, Fang Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China Harbin Engn Univ Yantai Res Inst Yantai Peoples R China

ISBN: (纸本)9781665467384

A joint scheduling model of load-side voyage and power generation is proposed in this paper in order to realize energy efficiency and emission reduction for all-electric ships. The optimization model contains several nonlinear constraints, such as propulsion power fluctuations caused by sailing speed variations and equipment lifespan losses caused by energy storage dispatch. The piecewise linearization method is an effective way to reduce model nonlinearity, but a large number of integer and binary variables introduced weakens the model convexity and increases the computational burden. Therefore, this study proposes an improved piecewise linearization method based on differential evolution to balance the number of decision variables and computational accuracy. In addition, the degradation loss caused by irregular charging and discharging of energy storage is quantified as the economic cost to optimize the energy storage operation strategy. Furthermore, the impact of greenhouse gas emission limitations on the power generation schedule is considered. Finally, the simulation of the proposed method is validated based on an all-electric ship.

关键词： Dispatch optimization differential evolution algorithm battery degradation all-electric ship greenhouse gas emission

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Abnormal traffic detection method for customer service cloud service data center based on improved Transformer

Abnormal traffic detection method for customer service cloud...

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2024 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Gong, Lihua Wang, Dongze Zhang, Xia Ren, Haiyang Fu, Ruidong Liu, Yang Customer Service Center of State Grid Corporation of China Tianjin 300309 China Beijing China-Power Information & Technology Co. Ltd. State Grid Information & Telcommunication Group Beijing 100052 China

ISBN: (纸本)9781510689275

In the cloud computing environment, customer service cloud service data centers face a large amount of data traffic, which may hide abnormal behaviors such as network attacks and data leaks. At present, abnormal traffic detection methods may be difficult to adapt to this big data environment due to issues such as low detection accuracy and efficiency. Therefore, this paper proposes a method for detecting abnormal traffic in customer service cloud service data centers based on an improved Transformer. Extracting traffic characteristics from customer service cloud service data centers in cloud computing, constructing an improved Transformer based abnormal traffic detection model for customer service cloud service data centers, and solving the problem of sequence to sequence detection. On this basis, differential evolution algorithm is introduced to optimize the abnormal traffic detection model of customer service cloud service data center, achieving abnormal traffic detection of customer service cloud service data center. The experimental results show that the average MAE value of the proposed method is below 4.0, and the abnormal traffic detection time is only 66ms, which can effectively improve the efficiency and accuracy of data traffic anomaly detection in customer service center. © 2025 SPIE.

关键词： Abnormal traffic detection cloud computing Customer service cloud service Data center differential evolution algorithm Improving Transformer

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A hybrid improved moth-flame optimization with differential evolution with global and local neighborhoods algorithm for pose optimization on a space manipulator

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MEASUREMENT SCIENCE AND TECHNOLOGY 2019年第12期30卷 125012-125012页

作者： Li, Yaru Wang, Zhongyu Cheng, Yinbao Tang, Yingqi Shang, Zhendong Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing 100081 Peoples R China Henan Univ Sci & Technol Sch Mechatron Engn Luoyang 471003 Peoples R China

To improve the location accuracy of the vision system of a space manipulator, a new hybrid improved moth-flame optimization based on a differential evolution with global and local neighborhoods algorithm (HIMD) is proposed to optimize the pose of a target relative to a camera. Firstly, the non-linear optimization model is established according to the imaging rule and space geometry transformation principle of the vision system. Secondly, the initial population of pose parameters is generated by the moth-flame optimization (MFO) algorithm, and the population is updated by the improved MFO (BIM). Finally, the new population is crossed, mutated and selected by the differential evolution with global and local neighborhoods (DEGL) algorithm, the population is iterated and updated continuously and the optimwn pose can be obtained. The proposed algorithm is applied to the precision test in the measurement system of a space manipulator. The experimental results show that the average synthetic errors are 2.67nun for chaotic harmony search algorithm (CHS), 1.80nun for differential evolution with particle swarm optimization (DEPSO), 2.94mm for the particle swarm optimization and gravitational search algorithm (PSOGSA), 2.13 mm for the DELI, algorithm, 2.56mm for the MFO algorithm and 0.53mm for the HIMD algorithm. This means that the accuracy of the HIMD algorithm is about four times higher than that of the MFO, PSOGSA and CHS algorithm and about three times higher than that of the DEGL and DEPSO algorithms. Therefore, the HIMD algorithm is superior to the other five algorithms for the non-linear optimization model of the pose.

关键词： space manipulator pose optimization heuristic algorithm moth-flame optimization differential evolution algorithm

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Simulating the Vegetation Gross Primary Productivity by the Biome-BGC Model in the Yellow River Basin of China

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WATER 2024年第23期16卷 3468页

作者： Jia, Lige Zhang, Bo Inner Mongolia Normal Univ Coll Tourism Hohhot 010028 Peoples R China Northwest Normal Univ Coll Geog & Environm Sci Lanzhou 730070 Peoples R China

In terrestrial ecosystems, the quantification of carbon absorption is primarily represented by the gross primary productivity (GPP), which signifies the initial substances and energy acquired by the ecosystem. The GPP also serves as the foundation for the carbon cycle within the entire terrestrial ecosystem. The Biome-BGC model is a widely used biogeochemical process model for simulating the stocks and fluxes of water, carbon, and nitrogen between ecosystems and the atmosphere. However, it is the abundance of eco-physiological parameters that lead to challenges in calibrating the model. The parameter optimization method of coupling the differential evolution algorithm (DE) with the Biome-BGC model was used to calibrate and validate the eco-physiological parameters of the seven typical vegetation types in the Yellow River Basin (YRB). And then we used the calibrated parameters to simulate the GPP by way of grid-based simulation. Finally, we conducted model adaptability testing and spatiotemporal analysis of GPP variations in the YRB. The results of the validation (R2, RMSE) were: temperate grasses (0.94, 24.33 g C m-2), alpine meadows (0.94, 18.13 g C m-2), shrubs (0.94, 29.20 g C m-2), evergreen needle leaf forests (0.96, 27.88 g C m-2), deciduous broad leaf forests (0.94, 32.09 g C m-2), one crop a year (0.96, 16.19 g C m-2), and two crops a year (0.90, 38.15 g C m-2). After adaptability testing, the average R2 value between the simulated GPP values and the GPP product values in the YRB was 0.85, and the average RMSE value was as low as 50.92 g C m-2. Overall, the model exhibited strong simulation accuracy. Therefore, after calibrating the model with the DE algorithm, the Biome-BGC model could effectively adapt to the ecologically complex YRB. Moreover, it was able to accurately estimate the GPP, which establishes a foundation for analyzing the spatiotemporal trends of the GPP in the YRB. This study provides a reference for optimizing Biome-BGC model parameters and

关键词： terrestrial ecosystems gross primary productivity Yellow River Basin Biome-BGC model differential evolution algorithm

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Wireless sensor localization based on distance optimization and assistance by mobile anchor nodes: a novel algorithm

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2179页

作者： Yang, Hui Lanzhou Univ Arts & Sci Sch Media Engn Lanzhou Gansu Peoples R China

Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node's coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.

关键词： Wireless sensor networks Localization algorithms Target tracking differential evolution algorithm Positioning accuracy

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Stochastic resource leveling in projects with flexible structures

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COMPUTERS & OPERATIONS RESEARCH 2024年 169卷

作者： Li, Hongbo Zheng, Linwen Chen, Rui Zhang, Xianchao Shanghai Univ Sch Management Shanghai 200444 Peoples R China Jiaxing Univ Key Lab Med Elect & Digital Hlth Zhejiang Prov Jiaxing 314001 Peoples R China

In project management, efficient utilization of resources plays a key role in project success, and resource leveling is an effective technique to optimize resource usage. During project execution, there are often uncertainties that complicate resource leveling. Furthermore, existing research on resource leveling typically assumes a fixed project structure. However, this is not always the case in practice, because there may be a variety of optional technical solutions for some activities, leading to a flexible project structure. Therefore, considering both stochastic activity durations and flexible project structures, we propose and study the stochastic resource leveling problem with flexible project structures (SRLP-PS). The solution of the SRLP-PS is in the form of a scheduling policy. We design two algorithms for solving the NP-hard SRLP-PS: (a) an exact algorithm based on stochastic programming, in which we formulate a scenario-based non-linear stochastic programming model and linearize it into an equivalent deterministic mixed-integer linear programming model that can be directly solved by CPLEX;and (b) an improved differential evolution algorithm, which is equipped with several problem-specific components, such as two mutation operators balancing exploration and exploitation, initialization, and local improvement search. Extensive computational experiments on a large number of benchmark instances are performed to validate our algorithms, which are also compared with state-of-the-art meta-heuristics. The computational results reveal the effectiveness and competitiveness of our algorithms. We also analyze the value of stochastic information based on the exact algorithm and the meta-heuristics, respectively.

关键词： Project scheduling Resource leveling Flexible project structures Stochastic activity durations Stochastic programming differential evolution algorithm

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Energy Minimization for IRS-assisted UAV-empowered Wireless Communications 18

Energy Minimization for IRS-assisted UAV-empowered Wireless ...

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18th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Liao, Yangzhe Liu, Jiaying Han, Yi Yu, Quan Ai, Qingsong Liu, Quan Zhai, Xiaojun Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China Hubei Univ Sch Comp Sci & Informat Engn Wuhan Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

ISBN: (纸本)9781665464574

Non-terrestrial wireless communications have evolved into a technology enabler for seamless connectivity and ubiquitous computing services in the beyond fifth-generation (B5G) and sixth-generation (6G) networks, aiming to provision reliable and energy efficient communications among aerial platforms and ground mobile users. This paper considers intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV)-empowered wireless communication, which exploits both the high mobility of UAV and passive beamforming gain brought by IRS. The energy minimization of rotary-wing UAV is formulated by jointly considering numerous quality of service (QoS) constraints with intricately coupled variables. To tackle the formulated challenging problem, a heuristic algorithm is proposed. First, we decouple it into several subproblems. Moreover, we jointly investigate offloading decisions of Internet of Thing (IoT) devices by the proposed enhanced differential evolution algorithm. Then, minorization-maximization algorithm (MMA) is utilized to solve the optimization of IRS phase shiftvector. Moreover, ant colony optimization (ACO) algorithm is proposed to optimize UAV flight route indicator matrix. Numerical results validate the effectiveness of the proposed algorithm. The results show that the proposed solution can remarkably decrease UAV flight distance while improving the network energy efficiency in comparison with numerous advanced algorithms.

关键词： intelligent reflecting surface unmanned aerial vehicle quality of service differential evolution algorithm ant colony optimization

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A Fully Distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic algorithm in Smart Grid

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1325-1335页

作者： Chentao Xu Xing He the Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China

A fully distributed microgrid system model is presented in this *** the user side,two types of load and plug-in electric vehicles are considered to schedule energy for more *** charging and discharging states of the electric vehicles are represented by the zero-one variables with more *** solve the nonconvex optimization problem of the users,a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm is designed and its convergence speed is faster.A distributed algorithm with a new approach to deal with the inequality constraints is used to solve the convex optimization problem of the generators which can protect their *** results and comparative experiments show that the model and algorithms are effective.

关键词： differential evolution algorithm distributed algorithm electric vehicle neural network zero-one variable.

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Digital twins in bridge engineering for streamlined maintenance and enhanced sustainability

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AUTOMATION IN CONSTRUCTION 2024年 168卷

作者： Franciosi, M. Kasser, M. Viviani, M. HEIG VD HES SO Univ Appl Sci & Arts Western Switze Route Cheseaux 1 CH-1401 Yverdon Switzerland

Digital twins are evolving to oversee the entire construction life cycle, with a strong emphasis on sustainability across environmental, financial, regulatory, and administrative dimensions. This paper introduces a methodology for managing existing bridges through an adaptable digital twin. The aim of this research is to develop a framework for constructing digital twins that, by enabling structural analysis and "what-if" scenario simulations, supports more reliable maintenance decision-making. Such type of digital twin ensure safety, extend lifespan, and provide a precise database for managing end-of-life processes within a circular "cradle to cradle" framework. This methodology also addresses obsolescence issues related to software evolution and the longer lifespan of a bridge compared to its creator. A case study demonstrates the methodology's effectiveness, showing that digital twins can be flexible, cost-effective tools for managing all types of bridges, including small and existing ones.

关键词： Digital twin models Bridge management Degradation phenomena Maintenance differential evolution algorithm

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