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36th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Zhipeng Li, Xiaohui Mao, Shanli Cai, Bin He, Jie Zhu, Jialu Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China Wuhan Netizen Technol Co Ltd Res Ctr Wuhan Peoples R China Shenglong Elect Grp Co Ltd Res Ctr Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The load profile can reflect the change of the grid load over time. By connecting electric vehicles to the grid for charging and discharging, the goal of peak shaving and valley filling is realized, thus flattening the load profile and improving the stability of grid operation. Aiming at this characteristic, a method for optimal scheduling of electric vehicle charging based on an improved particle swarm algorithm is proposed in this paper. First, the proposed method constructs the electric vehicle charging scenario by Monte Carlo. Then, the improved particle swarm algorithm is used to solve the optimal charging and discharging quantity of electric vehicles in each period to minimize the load variance. By comparing the improved particle swarm algorithm with other intelligent algorithms on the benchmark function test set, the improved particle swarm algorithm is verified that it has superiority in convergence ability and optimization accuracy. Simulation results show that the scheduling of electric vehicle charging using the improved particle swarm algorithm reduces the peak valley difference of the power grid by 6.31% and the load variance by 74432kW(2) compared with the classical particle swarm algorithm.

关键词： load profile electric vehicles Monte Carlo particle swarm optimization algorithm load variance

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Trajectory optimization algorithm Based on Time-Optimal Cooperative Six-Axis Robot 8

Trajectory Optimization Algorithm Based on Time-Optimal Coop...

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8th International Conference on Robotics, Control and Automation (ICRCA)

作者： He, Chunlai Liu, Yunfei Liu, Jinglong Wang, Weijun Sun, Wei Liu, Shujian Cheng, Shuo China Elect Technol Robot Co Ltd Robot Engn Ctr Shanghai Peoples R China

ISBN: (纸本)9798350344738;9798350344721

The trajectory optimization problem of cooperative six-axis robot based on time optimization is to optimize the trajectory of the robot under the premise of satisfying the kinematic constraints, dynamic constraints and task constraints of the robot, so that the time of movement in the working process is the shortest. After completing the kinematics solution of the six-axis cooperative robot, based on the 5-order B-spline interpolation trajectory, the particle swarm optimization algorithm is used to optimize the time interval between the trajectory nodes, and the particle swarm optimization algorithm is used to solve the robot trajectory with the lowest energy consumption. Finally, a suitable experiment is designed to prove the feasibility of the optimization scheme.

关键词： quintic B-spline time optimal trajectory planning particle swarm optimization algorithm robot obstacle avoidance

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Modified Pi Sigma artificial neural networks for forecasting

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GRANULAR COMPUTING 2023年第1期8卷 131-135页

作者： Egrioglu, Erol Bas, Eren Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkey

Pi Sigma artificial neural networks are a type of high-order neural network used in time series forecasting problems. In the Pi Sigma artificial neural networks, the weights between the hidden layer and the output layer are taken as constant and one, and the biases as constant and zero. Although this feature of the Pi Sigma artificial neural networks enables it to work with fewer parameters, it can also be seen as an obstacle to obtaining better forecasting performance. In this study, unlike classical Pi Sigma artificial neural networks, a modified Pi Sigma artificial neural network is proposed by taking the weights and biases as variables between the hidden layer and the output layer of the network. Thus, direct processing of the information coming to the output layer is prevented and the information coming to the output layer is weighted using different weights and bias values. The process of optimizing all the weights and bias values between the input and hidden layer, the hidden layer, and the output layer of the network is carried out together with the particle swarm optimization method. The proposed modified Pi Sigma artificial neural networks are compared with some other artificial neural networks in the literature by analyzing much well-known time series. As a result of the applications, it is seen that the forecasting performance of the modified Pi Sigma artificial neural networks is better than both the classical Pi Sigma artificial neural networks and many other artificial neural networks.

关键词： Pi Sigma artificial neural networks particle swarm optimization algorithm Forecasting

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Research on Power Grid Data Stability Model under Distributed Generation System Access 7

Research on Power Grid Data Stability Model under Distribute...

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7th International Conference on Automation Electronics and Electrical Engineering

作者： Wang, Yating Lei, Hui Wen, Jinmei State Grid Gansu Elect Power Co Digital Business Div Lanzhou 730050 Gansu Peoples R China

ISBN: (纸本)9798350377040;9798350377033

The study studied the optimal load balancing control method after the distributed power source was connected to the grid to improve the stability of the network operation. Starting from the perspective of the power market, this project studied the mechanism of the effect of multiple power access on the stability of the power network. Factors affecting the stability of the power grid operation were proposed. The particle swarm algorithm was used to achieve load balancing control. It was proposed to achieve power balance at each node in the network and reduce the degree of fluctuation of the system. A power network stability analysis method that takes into account the characteristics of distributed power sources was established and simulated. Through experimental verification, the research results of this project can effectively improve the stability of the power network under the condition of distributed power access and reduce the safety hazards of the power system. The research results of the project will lay a theoretical and technical support for the coordinated development of new energy and large power grids, and promote the construction of smart grids in China.

关键词： Distributed generation system power grid data stability model load power distribution control strategy particle swarm optimization algorithm model simulation

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A Route Self-Repair Method for Wireless Sensor Networks 24

A Route Self-Repair Method for Wireless Sensor Networks

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International Conference on Computer and Multimedia Technology (ICCMT)

作者： Guo, Chen Su, Mohan Cui, Fang China Mobile Grp Device Co Ltd Beijing 100053 Peoples R China

ISBN: (纸本)9798400718267

With the evolution of AI technology, several emerging applications (such as computational offload and image recognition) place higher demands on the quantity and quality of data transmitted by wireless sensor networks (WSNs). Existing routing algorithms typically choose the transmission path with fewer hops and shorter distance. For energy-constrained WSN, this will lead to rapid energy loss and loss of several nodes, which will shorten the network life cycle. In this paper, a WSN routing method based on the ant colony optimization algorithm and the particle swarm optimization algorithm is proposed to deal with the above-mentioned problems. By establishing the multidimensional pheromone model of node distance, hop number and energy, the nodes can dynamically adjust the routing according to the residual energy in the data transmission process, so as to ensure the balanced decrease of node energy and prolong the lifetime of the WSN network. In order to improve the convergence rate of ant colony algorithm and reduce the energy consumption caused by reconstruction, particle swarm optimization is applied;the fitness function of particle swarm optimization algorithm is improved to ensure that the path can quickly find the optimal solution of the path when multiple self-repairs or repair failures occur. The simulation shows that this method can improve the network lifetime by 33% by ensuring high transmission effectiveness.

关键词： Wireless sensor network Ant colony algorithm particle swarm optimization algorithm Network routing

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Prediction of gas utilization ratio based on singular spectrum analysis, improved PSO and KELM 36

Prediction of gas utilization ratio based on singular spectr...

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36th Chinese Control and Decision Conference (CCDC)

作者： Li, Zhuang An, Jianqi Guo, Yunpeng Zhao, Guoyu Wu, Min China Univ Geosci Sch Automat Wuhan Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan Peoples R China China Univ Geosci Sch Future Technol Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The gas utilization ratio (GUR) in a blast furnace is directly linked to the efficiency, cost-effectiveness and environmental impact on the blast furnace ironmaking process. However, The stability of the published GUR time series prediction models need to be improved. This paper presents an improved particle swarm optimization (PSO) incorporating linearly decreasing inertia weights (LDIW) to optimize the kernel-based extreme learning machine (KELM) for single-step prediction. This paper uses singular spectrum analysis (SSA) to preprocess the data and extract the key components from the GUR time series to solve the problem of high volatility of the GUR time series. In addition, this paper introduces LDIW to improve the optimization ability of particle swarm optimization algorithm, which enhances the stability of a single-step prediction model. Then this paper uses the improved PSO algorithm to extract the optimal parameters of KELM, and establishes a single-step GUR prediction model based on the improved PSO-KELM. Finally, this paper uses the actual production process data of blast furnace to verify the prediction model. The results show that the prediction accuracy of GUR and the overall stability of the model are significantly improved, providing important guidance for the blast furnace ironmaking process.

关键词： Gas utilization rate particle swarm optimization algorithm extreme learning machine singular spectrum analysis prediction model

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Comparative Analysis of Different Prediction Models of Inorganic Fertilizers N, P, and K Under Different Soil Fertility Conditions 36

Comparative Analysis of Different Prediction Models of Inorg...

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36th Chinese Control and Decision Conference (CCDC)

作者： Luo, Yuzhu Yu, Xiaoyu Zhou, Yue Bai, Bing Dong, Huan Peng, Xiuyuan Liaoning Acad Agr Sci Inst Informat Shenyang Peoples R China Liaoning Acad Agr Sci Inst Plant Nutr & Environm Resources Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Nutrient management is a key measure to achieve the target crop yield. Choosing appropriate predictive models and adjusting nutrient supply according to actual conditions can effectively reduce nutrient loss, improve crop yield and product quality. Therefore, we conducted research on the prediction models of inorganic fertilizers N, P, and K in greenhouse tomatoes based on the target yield. In this paper, we analyzed four different neural network prediction models: the neural network prediction model based on ant, colony optimization algorithm, the neural network prediction model based on sparrow search algorithm, neural network prediction model based on genetic algorithm and neural network prediction model based on particle swarm optimization algorithm, and compared and analyzed the prediction results of inorganic fertilizer N, P and K under different soil fertility conditions to understand which neural network prediction model will produce the best prediction effect. The simulation results showed that under medium soil fertility, the neural network prediction model based on genetic algorithm had the best prediction effect of inorganic fertilizer N, P, and K. The verification results showed that its mean square error (MSE) and coefficient of determination (R-2) were the best, which were 0.0031 and 0.8200 respectively. However, under low soil fertility and high soil fertility, the neural network prediction model based on the sparrow search algorithm had the best prediction performance, and its MSE and R-2 were the best.

关键词： ant colony optimization algorithm sparrow search algorithm genetic algorithm particle swarm optimization algorithm inorganic fertilizer prediction

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Equilibrium customer and socially optimal balking strategies in a constant retrial queue with multiple vacations and N-policy

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JOURNAL OF COMBINATORIAL optimization 2022年第4期43卷 870-908页

作者： Wang, Zhen Liu, Liwei Zhao, Yiqiang Q. Guilin Univ Elect Technol Sch Math & Comp Sci Guilin 541004 Guangxi Peoples R China Nanjing Univ Sci & Technol Sch Sci Nanjing 210094 Jiangsu Peoples R China Carleton Univ Sch Math & Stat 1125 Colonel Dr Ottawa ON K1S 5B6 Canada

In this paper, equilibrium strategies and optimal balking strategies of customers in a constant retrial queue with multiple vacations and the N-policy under two information levels, respectively, are investigated. We assume that there is no waiting area in front of the server and an arriving customer is served immediately if the server is idle;otherwise (the server is either busy or on a vacation) it has to leave the system to join a virtual retrial orbit waiting for retrials according to the FCFS rules. After a service completion, if the system is not empty, the server becomes idle, available for serving the next customer, either a new arrival or a retried customer from the virtual retrial orbit;otherwise (if the system is empty), the server starts a vacation. Upon the completion of a vacation, the server is reactivated only if it finds at least N customers in the virtual orbit;otherwise, the server continues another vacation. We study this model at two levels of information, respectively. For each level of information, we obtain both equilibrium and optimal balking strategies of customers, and make corresponding numerical comparisons. Through particle swarm optimization (PSO) algorithm, we explore the impact of parameters on the equilibrium and social optimal thresholds, and obtain the trend in changes, as a function of system parameters, for the optimal social welfare, which provides guiding significance for social planners. Finally, by comparing the social welfare under two information levels, we find that whether the system information should be disclosed to customers depends on how to maintain the growth of social welfare.

关键词： Multiple vacations Equilibrium strategies Balking strategies particle swarm optimization algorithm Information accuracy

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An Improved Mathematical Model for Estimating Polarization Parameters of Transformer Oil-Paper Insulation

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第4期17卷 2517-2525页

作者： Zhang, Tao Mandala, Antonio Tome Zuo, Qian Zhang, Ning Jiang, Shijie China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China Xiaogan Power Supply Co State Grid Hubei Elect Power Co Xiaogan Peoples R China

The parameters of dielectric response based on extended Debye circuit model can reflect oil-paper insulation state. The model using the initial slop of recovery voltage to estimate circuit parameters, serve as good quality consistency with the measured data. However, the model can be applicable for large power transformers but not for distribution transformers. This paper proposed an improved mathematical model using recovery voltage peak, peak time and initial slope characteristics to solve dielectric response equivalent circuit parameters. The identification of equivalent circuit parameters is converted into a mathematical optimization problem. And then particle swarm optimization algorithm is used for solving the problem. The improved mathematical model can decrease sampling data of recovery voltage. To check the validity of the estimated parameters, the on-site measured data of RVM experiments on actual transformers in various capacities is applied. The calculated result shows that the recovery voltage curve calculated and measured recovery voltage curve have good consistency, which can be advantageous to diagnose the oil-paper insulation status of transformer. It illustrates that the improved method proposed in this paper is feasible and effective.

关键词： Transformer Oil-paper insulation Recovery voltage method Improved mathematical model particle swarm optimization algorithm

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A Novel Model Based on PSO-GA-RBF Approach for Predicting Roll Gap During Unsteady Processes 3

A Novel Model Based on PSO-GA-RBF Approach for Predicting Ro...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Zhang, Ruixiao Mao, Chaoxing Hu, Jinkai Liu, Zhao Guo, Hengda North China University of Science and Technology Tangshan China

ISBN: (纸本)9798350380989

To enhance the control accuracy of strip thickness in the non-stationary process of cold continuous rolling, a PSO-GA-RBF based prediction model for the non-stationary roll gap is proposed. This model combines big data algorithms with actual production data, including roll gap, rolling force, and rolling speed. A 1450mm series cold rolling production line is selected as the research object. Pearson analysis is utilized to perform a correlation analysis on the original data indicators, while principal component analysis is employed to reduce the dimensionality of the sample data. The optimization process of particle swarm optimization is enhanced by introducing crossover and mutation operators from genetic algorithms. This optimization aims to improve the parameter selection mechanism of the radial basis function neural network, thereby enhancing the model's prediction accuracy and generalization performance. The performance of different prediction models is analyzed, comparing them based on evaluation indicators such as mean square error, average absolute error, and average absolute error percentage. The results indicate that the PSO-GA-RBF based roll gap prediction model demonstrates good predictive ability and generalization performance. To further validate the effectiveness of the prediction model, a comparison is drawn between the prediction model and the roll gap data obtained from engineering practice. The experimental results show a close correspondence in trend and period between the two, verifying the strong prediction performance of the roll gap prediction model. The validated model offers benefits such as reducing the workload of the automatic control system and improving the control accuracy of strip steel thickness. © 2024 IEEE.

关键词： Genetic algorithm particle swarm optimization algorithm Radial basis function neural network Roll gap model Thickness control

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