In order to cope with the increase in carbon emissions caused by the excessive use of fossil energy, the world's energy structure needs to be adjusted. Distributed photovoltaic (PV) hydrogen production system, as ...
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ISBN:
(纸本)9798350377477;9798350377460
In order to cope with the increase in carbon emissions caused by the excessive use of fossil energy, the world's energy structure needs to be adjusted. Distributed photovoltaic (PV) hydrogen production system, as an integrated energy system, is an important pathway to the zero-carbon goal. But distributed PV is prone to face the shading situation, while the PV module power output curve will show the phenomenon of multiple peaks. The traditional maximum power tracking methods are prone to fall into the local optimal situation, and the intelligent tracking methods are inherently more complex. The hydrogen production electrolyzer puts forward the requirement of stability on the output power of the PV power supply. Existing research focuses on the maximum power tracking of PV system, or the control of electrolyzer, lacking the coupled modeling control research of distributed PV hydrogen production system. In order to fill the gap of the existing research, this paper proposes a composite algorithm which combines the Improved particleswarmalgorithm (IPSO) with the Improved Perturbation Observation (IP&O) method. The initial population and termination conditions of the particleswarm search method (PSO) are improved to enhance the convergence speed of the algorithm and reduce the oscillations during the convergence process of the algorithm, and the IP&O method is utilized to provide stable inputs for the electrolytic tanks. The performance of the algorithm is verified through the comparative experiments with other algorithms.
In order to make the hybrid girder cable-stayed bridge force reasonable, in line with the reasonable bridge formation state, the adaptive particle swarm optimization algorithm is used to optimize the cable-stayed brid...
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ISBN:
(纸本)9783031761010;9783031761027
In order to make the hybrid girder cable-stayed bridge force reasonable, in line with the reasonable bridge formation state, the adaptive particle swarm optimization algorithm is used to optimize the cable-stayed bridge of hybrid girder cable-stayed bridge, the adaptive particle swarm optimization algorithm is written by using MATLAB software, and the finite element model is established by using Midas software for the analysis and calculation, and the objective function is constructed by using the influence matrix, and the value of the fitness is calculated, and the adaptive particle swarm optimization algorithm is used to find the global optimal solution. The results show that the vertical displacement of the main beam and the lateral displacement of the main tower are obviously reduced after optimization, and the bending moment of the main tower is obviously improved.
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 lon...
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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.
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 reduci...
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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.
Software testing is essential for assuring the reliability and excellence of software systems. Nevertheless, already used optimization techniques, such particleswarmoptimization (PSO), sometimes get stuck in local o...
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ISBN:
(纸本)9798350371635;9798350371628
Software testing is essential for assuring the reliability and excellence of software systems. Nevertheless, already used optimization techniques, such particleswarmoptimization (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 optimizationalgorithm (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).
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. ...
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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.
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, ...
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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.
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 b...
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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 particleswarms. 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
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 particleswarm optimiz...
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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.
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. Traditiona...
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