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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Zhang, Wei Zhou, Yangjun Yi, Chenying Li, Shan Feng, Bin Huang, Weixiang Pan, Juntao Zhou, Yuanchao Wu, Lifang Guangxi Power Grid Co Ltd Elect Power Res Inst Guangxi Key Lab Intelligent Control & Maintenance Nanning Peoples R China Guangxi Power Grid Co Ltd Nanning Peoples R China Guangxi Power Grid Co Ltd Nanning Power Supply Bur Nanning Peoples R China

ISBN: (纸本)9798350375145;9798350375138

With the large-scale access of distributed generation, the power flow characteristics of distribution network have been greatly changed. The conventional fault localization methods for distribution networks are no longer applicable. To deal with the problems induced by large-scale access of distributed generation, a novel fault localization method based on improved particle swarm optimization algorithm is proposed in this paper. Aiming at the issues of potential misjudge, an objective function/ fitness value correction method is proposed to adapt to bidirectional power flow induced by distributed generation. Aiming at the issues of sluggish convergence and poor robustness in existing models, this paper introduces the Reverse-Local Learning based particle swarm optimization algorithm to improve the optimization efficiency and algorithm stability. Case study is performed on the IEEE-33 bus distribution network to demonstrate the accuracy and efficiency of the proposed fault localization method.

关键词： fault localization distributed generation bidirectional power flow particle swarm optimization algorithm

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A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm

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ENERGY REPORTS 2022年 8卷 14578-14594页

作者： Zhang, Liang Yin, Qingbo Zhang, Zhihui Zhu, Zheng Lyu, Ling Hai, Koh Leong Cai, Guowei Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control Renew Minist Educ Jilin 132012 Peoples R China State Grid Shanghai Energy Interconnect Res Inst Shanghai 201210 Peoples R China Global Energy Interconnect Dev & Cooperat Org Beijing 100006 Peoples R China Natl Metrol Ctr Singapore 138635 Singapore

A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system's wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents' travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Electric vehicle Reduce the wind curtailment Dynamic TOU tariffs Individual differential evolution quantum particle swarm optimization algorithm

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Optimized Test Data Generation for Path Testing Using Improved Combined Fitness Function with Modified particle swarm optimization algorithm

Optimized Test Data Generation for Path Testing Using Improv...

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IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Damia, AmirHossein Parvizimosaed, Mohammadreza Bakhshai, Alireza Salehi, Mojtaba KN Toosi Univ Technol Fac Comp Engn Tehran Iran Queens Univ Dept Elect & Comp Engn Kingston ON Canada Islamic Azad Univ Fac Comp Engn Khorramabad Branch Khorramabad Iran

ISBN: (纸本)9798350371635;9798350371628

Software testing is essential for assuring the reliability and excellence of software systems. Nevertheless, already used optimization techniques, such particle swarm optimization (PSO), sometimes get stuck in local optima during testing. This study suggests innovative improvements to the PSO algorithm to address and overcome this constraint. Initially, we propose a method in which every particle keeps track of a collection of superior particles and chooses a global best (gbest) at random. This approach helps to explore a wider range of solutions and reduces the likelihood of being stuck in local minima. Furthermore, we use an enhanced crowding method to specifically tackle the discrepancy between the exploration and exploitation stages. This approach prioritizes extensive exploration and exploitation during the early phases of the search, progressively shifting towards a strategy that focuses more on exploitation as the algorithm advances. We present a thorough explanation of these changes, specifically highlighting the modifications made to the pbest section and the use of a novel fitness function that enhances the search process in the given space. The method that we offer has the potential to improve software testing methods by optimizing PSO-based techniques, leading to better performance and efficiency. The experimental findings have shown that our method outperforms numerous existing evolutionary or meta-heuristic algorithms in terms of test data generation speed and achieves superior coverage with fewer evaluations. The algorithms being compared are the Adaptive Genetic algorithm (AGA), Dandelion Optimizer (DO), Chaotic Flower-Fruit Fly optimization algorithm (CFFFOA), Imperialist Competitive algorithm (ICA), Chaos Adaptive particle swarm optimization algorithm (CAPSO), particle swarm optimization algorithm with Empirical Balance Strategy (PSOEBS), and Teaching Learning-Based optimization (TLBO).

关键词： Software Test Test Data Generation particle swarm optimization algorithm exploration exploitation fitness function

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Research on the coupling characteristics of energy power density of cutting vibration and cutting heat based on the particle swarm optimization algorithm

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2022年第3期46卷 614-625页

作者： Li, Songyuan Li, Shuncai Li, Yuqing Vladimirovich, Petrov Anton Jiangsu Normal Univ Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China Jiangsu Normal Univ JSNU SPBPU Inst Engn Xuzhou 221116 Jiangsu Peoples R China Petersburg Polytech Univ Inst Adv Mfg Technol St Petersburg 1952151 Russia

Cutting heat and cutting vibration are important basic research topics in the field of machining. Many factors affect cutting heat and cutting vibration, and cutting heat and cutting vibration also affect each other. This papermainly studied the coupling characteristics between cutting vibration and cutting heat from the perspective of energy power density. A measurement system was built to collect the time-domain signals of cutting temperature and three-dimensional cutting vibration. Through Stefan-Boltzmann's law, the cutting thermal power density represented by the cutting temperature was obtained. Frequency domain analysis dealing with the self-power spectrum density was carried out on the three-dimensional vibration acceleration, and the operation of reducing the vibration dimension was carried out by principal component analysis. Based on the particle swarm optimization algorithm, two couplingmodels between cutting heat and cutting vibrationwere established. The research showed that the coupling correlation coefficient between cutting heat and cutting vibration was above 0.6. The coupling characteristics of cutting heat and cutting vibration were strong, and the impact of cutting vibration on cutting heat was more significant. The conclusions provide theoretical guidance for studying the coupling characteristics of cutting heat and cutting vibration from the energy perspective.

关键词： cutting heat cutting vibration coupling characteristics cutting energy power density particle swarm optimization algorithm

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Game-relationship-based remanufacturing scheduling model with sequence-dependent setup times using improved discrete particle swarm optimization algorithm

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2022年第4期30卷 424-441页

作者： Zhang, Shuai Xu, Huifen Zhang, Hua Yang, Sihan Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence 18 Xueyuan St Hangzhou 310018 Peoples R China

Remanufacturing has become a Frontier technology in sustainable manufacturing and enables end-of-life products to be restored to their new conditions. Although remanufacturing scheduling has been widely investigated, the relationship between remanufacturers and customers is rarely examined. Therefore, a new game-relationship-based remanufacturing scheduling model with sequence-dependent setup times is proposed herein. In the model, the relationship between the remanufacturer and customers is constructed as a non-cooperative game, and the interval due dates are set based on the uncertain product quality to achieve effective remanufacturing and improve customer satisfaction. Multiple remanufacturing lines differentiated based on the quality grade of products are integrated into the proposed model. In addition, sequence-dependent setup times are considered in the model, which depend on the similarity between two adjacent tasks processed on a reprocessing unit. An improved discrete particle swarm optimization algorithm is proposed to obtain Nash equilibrium solutions via an efficient global search structure and a local search strategy. The algorithm is embedded with the Nash equilibrium solution evaluation method and integrated with multiple genetic operators to update the particles. The performance of the proposed algorithm in solving the proposed model is verified via a comparison with three baseline algorithms for managing different problem instances.

关键词： remanufacturing scheduling game theory quality grade sequence-dependent setup times due date particle swarm optimization algorithm

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Fault diagnosis of electrical automatic control system of hydraulic support based on particle swarm optimization algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第9期14卷 12091-12097页

作者： Wang, Rui Sun, Wanting Jilin Inst Chem Technol Sch Aeronaut Engn Jilin 132102 Jilin Peoples R China

The hydraulic braking system of the hoist is an extremely important part of the hoist. According to incomplete statistics, its failure rate accounts for more than half of the total number of hoist failures. Once the brake fails, the brake cylinder is stuck, etc. The light ones will bring economic losses to the enterprise, and the serious ones are more likely to endanger the personal safety of the staff. Therefore, it is more and more important to improve the reliability of the braking system of the *** purpose of this paper is to study the fault diagnosis of hydraulic support electrical automatic control system based on particle swarm optimization algorithm. This paper first analyzes the manufacturing process of the oil seal skeleton and the working principle of the manipulator, summarizes the characteristics of the manipulator failure, analyzes the factors that cause the unstable operation of the manipulator and the possible failure types of the manipulator, and analyzes the possible types of failures of the manipulator. The fault characteristics and causes of stamping manipulators. The reasons for this were analyzed in depth. An adaptive particle swarm optimization algorithm is proposed to optimize the BP (back-propagation) neural network. The particle fitness value is better than the current best value. At the same time, the inertia factor is increased to stimulate the role of these better particles in the particle update, and conversely, the inertia factor is decreased to weaken the role of the poorer particles in the particle update, and the inertia and acceleration weighting factors are nonlinear throughout the iteration process. Dynamic fitting strategy to achieve a balance between global search and local search of particle swarms. The experimental results show that the intelligent diagnosis method proposed in this paper improves the efficiency of fault diagnosis of the transmission system, and the accuracy rate is increased by more than 50%. Provide ge

关键词： particle swarm optimization algorithm Hydraulic support Electrical automatic control system Fault diagnosis

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An image encryption scheme based on particle swarm optimization algorithm and hyperchaotic system

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SOFT COMPUTING 2022年第11期26卷 5409-5435页

作者： Luo, Yuling Ouyang, Xue Liu, Junxiu Cao, Lvchen Zou, Yanli Guangxi Normal Univ Sch Elect Engn Guilin Peoples R China

Protecting digital images is crucial, and image encryption schemes based on chaotic systems have been extensively studied where image permutation is a critical process. The updating processes of particle swarm optimization algorithm have the ability to change the rules of particles motion, and it can be employed to permutate image. In this work, an image encryption scheme based on the updating processes of particle swarm optimization algorithm and hyperchaotic system is proposed. Specifically, a key generation mechanism combined with secure hash algorithm 256 hash is first introduced to generate the initial values of hyperchaotic complex Lu system. Then, the plain image is confused by the position and velocity updating processes of particle swarm optimization algorithm. In addition, an adaptive diffusion mechanism is designed and applied to the confused image to obtain the final cipher image. Simulation results and security analysis show that the proposed scheme exhibits good performance of sophisticated dynamic behavior, high sensitivity to key, and effectively resisting the typical cryptanalysis attacks, and provides an alternative to real-time image encryption application.

关键词： Image encryption Hyperchaotic system particle swarm optimization algorithm Secure hash algorithm 256 hash

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Multi-objective Optimal Tourism Route Planning and Design Based on Improved particle swarm optimization algorithm

Multi-objective Optimal Tourism Route Planning and Design Ba...

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作者： Yan, Jingjing Henan Vocational College of Agriculture Henan Zhengzhou China

In the tourism industry, in order to attract more tourists, there must be a key link, which is route planning. However, the complex optimization problem in tourism route planning has always been a headache. Traditional single route planning often lacks flexibility and personalization and is not conducive to deep tourism. Therefore, this paper proposed a multi-objective optimal tourism route planning and design method based on the improved particle swarm optimization (PSO), which aimed to meet the needs of people for better tourism experience and hoped to further promote the development of tourism. The experimental results showed that this method can not only obtain the optimal solution in tourism routes, but also has advantages in algorithm efficiency and convergence speed. Compared with the previous improvement, the time for solving the optimal route has been reduced by 54%, and the algorithm's efficiency in solving the optimal route has also reached 93%. This showed that the improved PSO can be used as an effective multi-objective optimal tourism route planning method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Improved algorithm Multi-objective optimal design particle swarm optimization algorithm Tourism route planning

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An integrated energy system optimization strategy based on particle swarm optimization algorithm

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ENERGY REPORTS 2022年 8卷 679-691页

作者： Wu, Min Du, Pengcheng Jiang, Meihui Goh, Hui Hwang Zhu, Hongyu Zhang, Dongdong Wu, Thomas Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China Elect Power Res Inst Guangxi Power Grid Co Ltd Nanning 530000 Peoples R China

For the sake of increasing the energy efficiency of the city's energy supply, it is an effective approach to adopt the joint planning of the integrated energy system employing a group of cooling heat and power. However, the coupling matrix in the integrated energy system is quite difficult to formulate. In this paper, a standardized construction method for modeling Energy Hub is proposed. The construction of the model is divided into several steps. In addition, the difficult of solving the coupling matrix of integrated energy system can be availably reduced through this method. Considering the nonlinear problem caused by dispatch factors, this paper proposes a comprehensive energy system optimization strategy by using improved particle swarm optimization algorithm. Finally, using an example of a residential area with cooling, heating and electricity system to verify the effectiveness of the proposed modeling and calculation method . At the same time, the influence of demand response and energy storage on economy during operation is analyzed (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Integrated energy system Energy hub Coupling matrix particle swarm optimization algorithm

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Optimal design of helical flute of irregular tooth end milling cutter based on particle swarm optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第7期236卷 3323-3339页

作者： Yu, Haibin Zheng, Minli Zhang, Wei Nie, Wanying Bian, Tianchen Harbin Univ Sci & Technol Minist Educ Coll Mech & Power Engn Key Lab Adv Mfg & Intelligent Technol Harbin 150080 Peoples R China

Due to the variable pitch angle and helix angle of the irregular tooth end milling cutter, the mass of the integral end milling cutter is eccentric, and the high stability and precision design of the irregular tooth end milling cutter is still a challenge. Aiming at the influence of dynamic balance of irregular tooth end milling cutter which can not be ignored in high-speed milling, the parameterized design of radial section of irregular tooth end milling cutter was carried out. Based on the space transformation law of the centroid of helical flute, a new method for calculating the centroid coordinate of end milling cutter was put forward, and a general mathematical model of eccentricity of integral end milling cutter was given. It was proved that this model could accurately calculate the centroid position and eccentricity of the end milling cutter. The influence of pitch difference angle and helix difference angle on eccentricity of end milling cutter was studied and analyzed. The particle swarm optimization (PSO) algorithm was creatively applied to optimize the helical flute shape of the end milling cutter, the curvature radius of helical flute curve is optimized, so that the centroid coordinate is infinitely close to the origin of coordinate. The number of iterations was set to 200. In the 32nd iteration, the result approached to infinitesimal, the final function converged, and obtained the groove curvature radius of the milling cuter with the smallest eccentricity. The optimized eccentricity of the end milling cutter is infinitesimal, which can make the vibration damping performance of the end milling cutter be fully developed. On the basis of ensuring the same cutting performance, the cutting tool unbalance was effectively reduced and the dynamic performance of milling cutter was further improved.

关键词： Keyworks End milling cutter irregular tooth helical flute mass eccentricity particle swarm optimization algorithm

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