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Wide Range Reactive Power Compensation for Voltage Unbalance Mitigation in Electrical Power Systems

ELECTRIC POWER COMPONENTS AND SYSTEMS 2022年第6-7期49卷 715-728页

作者： Ghaeb, Jasim A. Alkayyali, Malek Tutunji, Tarek A. Philadelphia Univ Fac Engn Alternat Energy Technol Dept Amman Jordan Clemson Univ Dept Mech Engn Clemson SC USA Al Hussein Tech Univ Sch Engn Technol Amman Jordan

In this work, a wide range of reactive power compensation is achieved for voltage unbalance mitigation in 500 km electrical power systems. An Optimal Control Technique (OCT) is proposed to encompass the unbalance load changes at a wide range of Voltage Unbalance Factor (VUF), between 3.33% and 12.4601%, and to minimize it to an acceptable value (at average less than 2%). The technique uses a combination of Particle Swarm optimization (PSO) and Artificial Neural Networks (ANN) in three stages. In the first stage, the PSO finds the optimal firing angles of the Thyristor Controlled Reactor (TCR) and the optimal number of bank capacitors for the Thyristor Switched Capacitor (TSC) to restore the voltage balance. In the second stage, the voltage unbalance evaluations obtained by the PSO algorithm are used to train the ANN. In the third stage, the ANN is connected to the system to control and overcome the voltage unbalance problem accurately and quickly. Results are compared with other techniques available in the literature to confirm the superiority of the OCT performance. Furthermore, a laboratory model for the electrical power system is built and the proposed OCT for real voltage unbalance mitigation is validated.

关键词： TCR-TSC compensators power quality electric power system optimization algorithms ANN

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A New Prediction Strategy for Dynamic Multi-objective optimization Using Diffusion Model

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Feng Xie, Jinsong Zhou, Aimin Tang, Ke Wuhan University School of Computer Science Hubei430072 China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing The Department of Computer Science and Technology Shanghai200241 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

To solve dynamic multi-objective optimization problems (DMOPs), the optimization algorithms are required to track the movement of the Pareto set after the environmental changes effectively. Many prediction-based dynamic multi-objective evolutionary algorithms (DMOEAs) have been proposed to address this challenge by utilizing environmental information for population reinitialization. However, when environmental changes are complex, irregular, and severe, the solutions and information during the evolution process often contain noise, making it difficult for prediction-based DMOEAs to accurately predict and reinitialize the population. To address this issue, we propose a novel dynamic multi-objective evolutionary algorithm (DM-DMOEA) which uses a diffusion model based prediction strategy. In DM-DMOEA, to improve the prediction accuracy, the diffusion model is introduced to extract the relationships of high-quality solutions and reinitialize the population, and a PS estimation method is employed to integrate both historical and new environmental information, providing a set of high-quality solutions for diffusion model training. To speed up the response time, a variational autoencoder (VAE) is used to map the decision space to a latent space, which can reduce the diffusion model size and accelerate the diffusion process. To evaluate the effectiveness of the proposed DM-DMOEA on DMOPs, comprehensive experiments are conducted on several benchmarks and a practical problem. The results show that the DM-DMOEA outperforms other four state-of-the-art DMOEAs in most cases. © 1997-2012 IEEE.

关键词： optimization algorithms

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Research on Vehicle Collaborative Algorithm for Optimizing Intelligent Charging Pile of Electric Vehicles in the Internet of Vehicles Environment

Research on Vehicle Collaborative Algorithm for Optimizing I...

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2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Wang, Jinsheng Dai, Jiarong Lv, Yuanyuan Zheng, Rui Chen, Fengyue Zhang, Xiaorong University of Engineering Shandong Vocational and Technical Shandong Jinan China

ISBN: (纸本)9798331504205

This paper proposes a collaborative optimization algorithm for intelligent charging piles of electric vehicles based on the Internet of Vehicles environment. The algorithm combines the real-time information interaction between vehicles to establish a charging demand prediction model, and designs an intelligent allocation strategy based on multi-objective optimization to improve the utilization efficiency of charging resources. Various influencing factors are considered, including the distribution density of charging piles, the driving trajectory of electric vehicles, and the spatiotemporal distribution of charging demand. In addition, the model simulation is carried out based on MATLAB, and the algorithm performance is analyzed through experimental data to verify the effectiveness of the proposed algorithm in optimizing the utilization rate of charging piles and shortening user waiting time. The simulation results show that compared with the traditional charging pile allocation method, the algorithm can significantly reduce the average waiting time (by about 25%) and improve the overall charging efficiency (by about 15%). Therefore, this study provides strong support for the optimization of charging resources for electric vehicles in the Internet of Vehicles environment, which is conducive to promoting the popularization of electric vehicles and the integrated application of Internet of Vehicles technology. © 2025 IEEE.

关键词： optimization algorithms

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An iterated greedy algorithm based on NSGA-II for distributed hybrid flow shop scheduling problem 3

An iterated greedy algorithm based on NSGA-II for distribute...

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3rd International Conference on Communications, Information System, and Data Science, CISDS 2024

作者： Liu, Dandan Liang, Xu Zou, Zhiyuan School of Computing Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The distributed hybrid flow shop scheduling problems (DHFSP) widely exist in various industrial production processes, and thus have received widespread attention. However, studies on HFSP considering green objective in distributed production environment are quite limited. Therefore, this paper investigated a distributed hybrid flow shop scheduling problem with objectives of minimization the makespan and total energy consumption (TEC). To solve it, an iterated greedy algorithm based on NSGA-II (NSGAIG) is developed. In the proposed algorithm, a random initialization strategy is used to generate the initial solution. Then, starting from the population initialization, a multi-objective local search is carried out for the current optimal solution in the population to obtain the global optimal solution. Next, random variation method is used to increase the exploration space of the algorithm. Finally, the proposed NSGAIG algorithm is compared with other multi-objective optimization algorithms. Experimental results indicate that the proposed NSGAIG outperforms its compared algorithms in solving this problem. © 2025 SPIE.

关键词： optimization algorithms

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Research on Scheduling algorithms for Multi-UAV Collaborative Search 2nd

Research on Scheduling Algorithms for Multi-UAV Collaborat...

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2nd International Conference on the Frontiers of Robotics and Software Engineering, FRSE 2024

作者： Yu, Bocheng Tan, Qilin Zhang, Yang Wu, Na Xing, Liang Xi’an Aeronautics Computing Technique Research Institute AVIC Xi’an China

ISBN: (纸本)9789819630127

The unique wide-area surveillance capability of UAVs has led to their increasing use in environmental search tasks. This paper studies the use of SUAVs to assist UAVs in collaborative searches, aiming to expand the search area of UAVs while reducing search costs. Considering practical constraints such as UAV endurance, fuel consumption, and cost of detection sensor, a mathematical optimization model is constructed to minimize the whole costs. The mathematical problem is NP-hard, and an optimization algorithm is proposed to solve the problem. Simulation experiments demonstrate that the proposed algorithm can effectively reduce costs compared to conventional algorithms, and the computation time of the algorithm is significantly shorter than that of conventional algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Multi-Subpopulation Artificial Bee Colony Algorithm Based on Individual Classification 7

Multi-Subpopulation Artificial Bee Colony Algorithm Based on...

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7th International Conference on algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Li, Sumin Li, Menghan Pan, Xiuqin Wang, Jia Minzu University of China Beijing China

ISBN: (纸本)9798331529314

The artificial bee colony algorithm faces difficulties in insufficient search performance when tackling intricate optimization tasks. To improve this problem and enhance the algorithm's ability to effectively balance the exploration and exploitation, we propose a multi-subpopulation artificial bee colony algorithm that utilizes individual classification to enhance performance(DZABC). In DZABC, the whole bee population was segmented into three distinct sub-populations based on varying levels, and each sub-population adopted strategies with different characteristics as candidate strategies according to its unique population characteristics. This diversity of strategies enables algorithms to leverage the strengths of different strategies to optimize the overall search performance. The introduction of multi-subpopulation mechanism not only aids in preserving population diversity, but also ensures that the algorithm achieves good convergence performance in the search process. In addition, we design a new judgment mechanism in the scout bee phase to further enhance the algorithm's convergence efficiency. To validate the efficacy of the DZABC algorithm, we apply it to 16 test functions, and demonstrate its robust competitiveness through comparative analysis with other algorithms. Experimental outcomes indicate that the DZABC algorithm outperforms other comparative algorithms to a certain extent, which shows its potential and advantage in solving optimization tasks. © 2024 IEEE.

关键词： optimization algorithms

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Fairness in Monotone k-submodular Maximization: algorithms and Applications

Fairness in Monotone k-submodular Maximization: Algorithms a...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Zhu, Yanhui Basu, Samik Pavan, A. Iowa State University Department of Computer Science Ames United States

ISBN: (纸本)9798350362480

Submodular optimization has become increasingly prominent in machine learning, and fairness has drawn much attention. In this paper, we propose to study the fair k-submodular maximization problem and develop a 1/3-approximation greedy algorithm with a running time of O(knB). Our theoretical guarantee matches the best-known k-submodular maximization results without fairness constraints. In addition, we have developed a faster threshold-based algorithm that achieves a (1/3 ϵ) approximation with O (kn/ϵ log B/ϵ) evaluations of the function-f. Furthermore, for both algorithms, we provide approximation guarantees when the k-submodular function is not accessible but only can be approximately accessed. We have extensively validated our theoretical findings through empirical study and examined the practical implications of fairness. The experimental results show that the fairness constraints do not significantly undermine the quality of solutions. © 2024 IEEE.

关键词： optimization algorithms

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Optimizing Distribution of Print Media Using Metaheuristic algorithms 1

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Congress on Smart Computing Technologies, CSCT 2023

作者： Tekade, Vinayak Jeyanthi, R. Amrita Vishwa Vidyapeetham Bengaluru560035 India

ISBN: (数字)9789819750818

ISBN: (纸本)9789819750801

Efficient distribution of newspapers and magazines is crucial to ensure punctual and cost-effective delivery. Presently, the distribution of print media lacks organization. In this study, we introduce an innovative approach to enhance this process by leveraging advanced metaheuristic algorithms. Beyond conventional methodologies, we explore the potential of Genetic algorithms, Hill Climbing algorithms, and Simulated Annealing, providing a comparative analysis of their efficacy. Our research demonstrates that these advanced techniques can significantly improve traditional strategies, offering valuable insights into the transformation of distribution methods. When comparing various optimization techniques, our findings indicate that simulated annealing stands out as the most suitable method for addressing our problem statement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： optimization algorithms

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Formulation and application of SMU: An open-source MATLAB package for structural model updating

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ADVANCES IN STRUCTURAL ENGINEERING 2022年第4期25卷 698-715页

作者： Otsuki, Yu Lander, Peter Dong, Xinjun Wang, Yang Georgia Inst Technol Sch Civil & Environm Engn 790 Atlantic Dr NW Atlanta GA 30332 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

This paper describes a MATLAB package for structural model updating, named SMU. The SMU package updates parameter values of a finite element model by solving optimization problems utilizing modal properties obtained from sensor measurements. In particular, the package offers three model updating formulations, namely, (1) the modal assurance criterion value approach, (2) the eigenvector difference approach, and (3) the modal dynamic residual formulation. The first two belong to the family of modal property difference formulations. For each formulation, the analytical Jacobian derivative of the objective function is derived and implemented in SMU. Since the formulated optimization problems are generally nonconvex, the global optimality of the solution cannot be guaranteed using off-the-shelf optimization algorithms. In order to increase the chance of finding a better local minimum, the SMU package can perform gradient search from randomly generated starting points. Several examples for the model updating of as-built structures are included in the GitHub package. This paper demonstrates the SMU functionality through model updating of an 18-DOF model and a concrete building frame model.

关键词： finite element model updating open-source software nonconvex optimization optimization algorithms modal analysis

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Improving the Performance of Bilevel Evolutionary algorithms using Variable Associations 13

Improving the Performance of Bilevel Evolutionary Algorithms...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Bing Singh, Hemant Kumar Ray, Tapabrata School of Engineering and Technology The University of New South Wales CanberraACT2600 Australia

ISBN: (纸本)9798350308365

Given a bilevel optimization problem, it is often implicitly assumed that all lower level variables participate in the lower level objective/constraint evaluations. However, in theory, there can be scenarios where this assumed variable-to-objective relation is misleading. This means that some variables purportedly associated with lower level objectives might not actually contribute to their calculation. Some lower level variables may only participate in upper level objective calculation, making hard to determine their desired values through the lower-level optimization process. When such solutions are passed to the upper level for evaluation, the upper level search can easily be misled by these inferior values. This property of certain problems poses a significant challenge to the solution methodologies, and this aspect has been generally overlooked in the literature. In this paper, we propose a simple pre-processing method aimed at identifying the association between variables and objectives through a causal relation test. To ascertain if an LL variable contributes to UL/LL objective(s), it is perturbed from a baseline value, and the resulting impact on the objective(s) is observed to make the inference. Then, an improved search scheme is formulated to allocate the evaluations effectively based on the identified variable-to-objective relations. Empirical experiments demonstrate that the proposed algorithm exhibits competitive performance across various problem settings, and are particularly beneficial for so-called deceptive bilevel problems. © 2024 IEEE.

关键词： optimization algorithms

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