With the aim of enhancing both the ride comfort and the safety of the vehicle, we propose a new type of suspension with an annular vibration-absorbing structure, and establish a 3-DOF 1/4 vehicle model. The structure ...
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With the aim of enhancing both the ride comfort and the safety of the vehicle, we propose a new type of suspension with an annular vibration-absorbing structure, and establish a 3-DOF 1/4 vehicle model. The structure parameters and time-delay feedback control parameters are determined by particle swarm optimization algorithms, which take the root mean values of body acceleration, suspension dynamic deflection, and tire dynamic displacement as their optimization objectives. We analyze the stability of the suspension control system to ensure the stability of the time-delay control system through the Routh-Hurwitz stability criterion, characteristic root method, and stability switching method. Then, we compare and analyze the response characteristics of conventional suspension, new suspension without time-delay feedback control, and new suspension with time-delay feedback control under simple harmonic excitation and random excitation. The results show that the new suspension with time-delay feedback control has a significant damping effect on the body under the premise of ensuring the stability of the system.
The effective deployment of wireless sensor networks (WSNs) is a crucial foundation for the intelligent development of power systems. To address the optimization of wireless sensor distribution in power systems, this ...
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ISBN:
(纸本)9798350350319;9798350350302
The effective deployment of wireless sensor networks (WSNs) is a crucial foundation for the intelligent development of power systems. To address the optimization of wireless sensor distribution in power systems, this paper proposes a novel method named the Distributed particle swarm optimization algorithm (D-PSO). This method mitigates the premature convergence issue of heuristic algorithms by introducing a regional operator. Additionally, considering the high-interference environment in power systems, relay node strategy (RNS) is incorporated to ensure communication quality. Simulation results validate the effectiveness and superiority of the D-PSO method and demonstrate the necessity of the RNS. Compared to some advanced particleswarmalgorithms, this method better balances energy consumption, coverage, and communication quality, thereby significantly enhancing the overall performance of the wireless sensor network.
In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is *** on the ob...
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In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is *** on the objective function of the best power quality management effect and the smallest investment cost of the management device,the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is *** on the balance between the economic demand of distribution network resources optimization and power quality management capability,the power quality of distribution network is considered *** proposed optimizationalgorithm for power quality management based on Matlab and OpenDSS is proposed and analyzed for port distribution *** simulation results show that the proposed optimizationmethod can maximize the power qualitymanagement capability of the port distribution network,and the proposed optimizationalgorithm has good convergence and global optimization finding capability.
With the frequent regional flood disasters in recent years, the study of automatic scheduling and control technology of pump gate clusters has become crucial in related academic circles. Here, an automatic flood contr...
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With the frequent regional flood disasters in recent years, the study of automatic scheduling and control technology of pump gate clusters has become crucial in related academic circles. Here, an automatic flood control and scheduling model for pump and gate clusters of water conservancy projects is constructed by combining hormone regulation and particleswarmalgorithm. The model can regulate the maximum discharge flow and maximum water level of reservoirs according to the actual situation in order to achieve flood prevention and consumption prevention. The model is validated using two reservoirs in city A as an example. The results show that the maximum dis-charge flow of the two reservoirs after the model treatment is 1,996.16 and 16,738.28 m3/s, which reduces the incidence of flooding in city A. In addition, after the optimal flood control scheduling, the maximum water levels of the once-in-a-century flood and once-in-two-centuries flood in city A become 80.41 and 81.66 m, respectively, which are in the safe water level range. The study is of great significance for the reduction of flooding.
The disorderly charging tactics of large-scale electric vehicle (EV) will lead to the increase of peak-valley load difference of the power grid and destroy the stability of the power system. In order to solve this pro...
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The disorderly charging tactics of large-scale electric vehicle (EV) will lead to the increase of peak-valley load difference of the power grid and destroy the stability of the power system. In order to solve this problem, considering the peak adjustment demand of the grid side and the different demands of the users on the charging amount and charging cost, this paper proposes a hierarchical optimization scheduling method based on the dynamic TOU time segment model. This method updates the load curve according to the load information of each EV when it is connected to the grid. Meanwhile, the dynamic time-of-use electricity price model proposed in this paper is used to dynamically update the peak-valley electricity price of the EV. Among them, the upper layer model takes the minimum variance of the total load of the grid as the objective function, and the lower layer model takes the minimum deviation of the agent scheduling plan, the maximum amount of charging and the minimum cost of charging as the objective function, and adopts the improved PSO algorithm to optimize the charging (discharging) tactics of each EV. Finally, the simulation analysis is carried out with a concrete example. The results show that this method can effectively reduce the peak-valley difference of load demand curve on the premise of ensuring the travel demand and economic benefits of EV owners.
The effectiveness and efficiency of enterprise knowledge management depends on the effectiveness and efficiency of the enterprise's implementation of knowledge management. Big data technology can collect, analyse ...
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The effectiveness and efficiency of enterprise knowledge management depends on the effectiveness and efficiency of the enterprise's implementation of knowledge management. Big data technology can collect, analyse and apply the massive amount of data in an organisation to support the implementation of knowledge management. Therefore, exploring the role of big-data knowledge management in the development of enterprise innovation will help enterprises to better implement knowledge management. Based on this, the study aims to propose a model for predicting big data knowledge management and enterprise innovation development for high-tech enterprises in China. The study firstly used Principal Component Analysis (PCA) to decrease the dimensionality of the model, and then used the particleswarmalgorithm to optimize BP neural network (PSO-BP). Network (PSO-BP) was used to evaluate enterprise knowledge management and enterprise innovation development. The results of the study show that the absolute values of the relative errors of the pre-processed model do not exceed the 5% threshold, and only the relative errors of some indicators are relatively large, such as X5 and X7, with values of 4.5% and -3.8%, indicating that the model has a good performance in predicting the innovation effect of enterprises.
A selective maintenance model for multistate systems that simultaneously considers the random uncertainty of the system mission period and mission breaks and the requirements of different system performance levels is ...
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A selective maintenance model for multistate systems that simultaneously considers the random uncertainty of the system mission period and mission breaks and the requirements of different system performance levels is proposed. Unlike traditional studies, the proposed model not only considers the random uncertainty of the mission period and mission breaks and the requirements of different system performance levels but also effectively manages the selective maintenance problems of multistate systems by using a heuristic algorithm. In this model, random variables that conform to a verified distribution are employed to characterize the randomness of the mission period and mission breaks, while the service age reduction model is utilized to describe the effective age of the system components after maintenance. To construct this method, the quantitative relationships between the system maintenance cost and the service age reduction factor and between the maintenance time and the service age reduction factor are established. Then, based on the multistate system reliability and general generating function technology, the mission completion rate models of the components and the system are established. Finally, the solution approach for making selective maintenance decisions for multistate systems based on the particleswarmoptimization (PSO) algorithm is presented. To validate the effectiveness of the proposed model as well as the solution algorithm, a coal transportation system at a thermal power station is studied, and satisfactory results are obtained.
A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading ***,the decentralized blockchain structure of the...
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A blockchain-based power transaction method is proposed for Active Distribution Network(ADN),considering the poor security and high cost of a centralized power trading ***,the decentralized blockchain structure of the ADN power transaction is built and the transaction information is kept in ***,considering the transaction needs between users and power suppliers in ADN,an energy request mechanism is proposed,and the optimization objective function is designed by integrating cost aware requests and storage aware ***,the particle swarm optimization algorithm is used for multi-objective optimal search to find the power trading scheme with the minimum power purchase cost of users and the maximum power sold by power *** experimental demonstration of the proposed method based on the experimental platform shows that when the number of participants is no more than 10,the transaction delay time is 0.2 s,and the transaction cost fluctuates at 200,000 yuan,which is better than other comparison methods.
Currently, drones have been gradually applied in the field of agriculture, and have been widely used in various types of agricultural aerial operations such as precision sowing, pesticide spraying, and vegetation dete...
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Currently, drones have been gradually applied in the field of agriculture, and have been widely used in various types of agricultural aerial operations such as precision sowing, pesticide spraying, and vegetation detection. The use of agricultural UAVs for pesticide spraying has become an important task in the agricultural plant protection process. However, in the crop spraying process of agricultural UAVs, it is necessary to traverse multiple spray points and plan obstacle avoidance paths, which greatly affects the efficiency of agricultural UAV crop spraying operations. To address the above issues, traditional particleswarmoptimization (PSO) algorithms have strong solving capabilities, but they are prone to falling into local optima. Therefore, this study proposes an improved PSO algorithm combined with the A* algorithm, which introduces a nonlinear convergence factor balancing algorithm for global search and local development capabilities in the traditional PSO algorithm, and adopts population initialization to enhance population diversity, so that the improved PSO algorithm has stronger model solving capabilities. This study designs two scenarios for agricultural UAV crop spraying path planning: one without obstacles and one with obstacles. Experimental simulation results show that using the PSO algorithm to solve the obstacle-free problem and then using the A* algorithm to correct the path obstructed by obstacles in the obstacle scenario, the agricultural UAV crop spraying trajectory planning based on the PSO-A* algorithm is real and effective. This research can provide theoretical basis for agricultural plant protection and solve the premise of autonomous operation of UAVs.
This article presents a new two-axis solar tracker based on an online optimizationalgorithm so as to track the position of the sun without using its movement *** this research,four well-known optimizationalgorithms ...
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This article presents a new two-axis solar tracker based on an online optimizationalgorithm so as to track the position of the sun without using its movement *** this research,four well-known optimizationalgorithms are employed to find the two unknown parameters named azimuth and zenith angles,which determine the position of the *** magnitude of the sunray is considered as the cost function of all ***,several experiments are carried out to find the best optimizationalgorithm with optimal population size,number of iterations,and also the best initialization *** initialization leads to faster convergence compared to random *** results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms,with a convergence time of less than 40 *** average fitness value or voltage received by the tracker is 2.4 Volts in this method,which is higher than other *** also performs well with a population size of 15 and 7 ***,the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from *** of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network *** performance of the proposed ANN model is evaluated using regression plots,demonstrating a strong correlation between predicted and target ***,the outcomes reveal the feasibility of using online optimizationalgorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.
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