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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Zheng, Huaihua Huang, Hongsheng Yang, Shuai Pang, Yifan Wu, Zhi State Grid Jiaxing Electric Power Supply Company Jiaxing China Southeast University Southeast University - Monash University Joint Graduate School Suzhou China Southeast University School of Electrical Engineering Nanjing China

ISBN: (纸本)9798350328813

With the continuous access of large-scale distributed photovoltaics (PV) to low-voltage distribution networks, the three-phase imbalance problem of low-voltage distribution networks is becoming increasingly severe, and problems such as voltage over-limit are prone to occur when the PV output is excessive. To solve the above, this paper proposes a comprehensive treatment device based on the coordinated operation of small-capacity energy storage converters and reactors. By controlling each phase's active and reactive power, the voltage over-limit can be controlled, and the three-phase voltage imbalance can be alleviated. By using the improved particle swarm optimization algorithm, simulation analysis was carried out in an IEEE-20 node low-voltage distribution network, which verified the effectiveness of the proposed comprehensive treatment device in dealing with low-voltage distribution network voltage over-limit and three-phase imbalance. © 2023 IEEE.

关键词： distributed photovoltaic energy storage converter improved particle swarm optimization algorithm low-voltage distribution network multi-objective optimization reactor

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Research on Micro/Nano Surface Flatness Evaluation Method Based on improved particle swarm optimization algorithm

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2021年 9卷 775455页

作者： Shu, Han Zou, Chunlong Chen, Jianyu Wang, Shenghuai Hubei Univ Automot Technol Sch Mech Engn Shiyan Hubei Peoples R China

Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.

关键词： flatness error micro nano surface improved particle swarm optimization algorithm minimum zone method uncertainty AFM

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Economic and Trade Transformation and Upgrading Strategies Based on improved particle swarm optimization algorithm

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Procedia Computer Science 2024年 243卷 638-646页

作者： Litian Huang Chengdu College of University of Electronic Science and Technology of China Chengdu 611731 Sichuan China

Nowadays, world trade has been greatly impacted by the epidemic, and the level of foreign trade of enterprises is facing severe challenges. This article established an interest function with the goals of economic growth rate, industrial structure adjustment, and technological progress from three aspects: economic growth rate, industrial structure adjustment, and technological innovation. By adjusting the particle position, the transformation strategy can be modified to promote the optimization of China's economic and trade (E&T) structure, thereby achieving sustainable development of transformation and upgrading. It evaluated the promoting effect of the improved particle swarm optimization algorithm (PSOA) on economic and trade transfer by comparing the economic growth rate before and after its adoption. This article adopted a traditional strategy, with an economic growth rate of 6.2% in the eastern region and 5.8% in the western region in 2020. In 2021 and 2022, with the adoption of the improved PSOA strategy, the overall economic growth rate has significantly improved. The E&T transformation and upgrading strategy based on improved PSOA is conducive to promoting economic development and improving economic efficiency.

关键词： Economic and Trade Transformation and Upgrading Strategy improved particle swarm optimization algorithm Economic Growth Rate Industrial Structure Adjustment

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Fault Diagnosis of Power Electronic Circuits Based on improved particle swarm optimization algorithm Neural Network

ELECTRICA

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ELECTRICA 2022年第3期22卷 365-372页

作者： Xiao, Zuming Guo, Zhan Balyan, Vipin Jingdezhen Univ Dept Mech & Elect Jingdezhen Peoples R China Cape Peninsula Univ Technol Dept Elect Elect & Comp Engn Cape Town South Africa

In the rapid development of high and new technology, the intelligence and integration of modern equipment are constantly improving. Power electronics technology is one of the indispensable key technologies in any high and new technology. In this paper, a power electronics circuit fault diagnosis based on improved particle swarm optimization neural network is proposed, the algorithm design of particle swarm optimization algorithm neural network is introduced, and the improved PS0 algorithm, standard PS0 algorithm, and BP algorithm optimized neural network are applied to the fault diagnosis classification system of rectifier circuits. The results show that the parameters of the basic (particle swarm optimization) algorithm are as follows: the parameter value of the basic PSO algorithm is the number of particles is 30, W decreases from 0.9 to 0.4 linearly with the increase of iterations, and the number of iterations is 300. The BP algorithm uses the traingdx training function. The transfer functions of the hidden layer and the output layer are hyperbolic tangent sigmoid and Purelin function, respectively. The target error e = 0.01. The superiority and effectiveness of the neural network diagnosis model of the improved PS0 algorithm are shown in this paper. This method can solve the fault diagnosis problem of the double-bridge parallel rectifier circuit.

关键词： improved particle swarm optimization algorithm neural network power electronic circuit fault BP neural network

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Balance control system of grid connected inverter circuit based on the improved particle swarm optimization algorithm

Balance control system of grid connected inverter circuit ba...

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2021 International Conference On Electronics, Circuits And Information Engineering(2021年电子、电路与信息工程国际会议)

作者： Huixiu Li School of Electrical Engineering Dalian University of Science and Technology Dalian China

ISBN: (纸本)9781665448109

In order to improve the control efficiency of grid connected inverter circuit,this paper designs a grid connected inverter circuit balance control system based on improved particle swarm optimization *** terms of hardware,the signal acquisition and conditioning circuit is designed according to the basic electrical parameters of the grid connected inverter circuit balance control *** the aspect of software,an adaptive fuzzy controller is designed to search the circuit signal based on the improved particle swarm optimization algorithm,and the balance control strategy of grid connected inverter circuit is set to realize the balance control of grid connected inverter *** lest environments are set up in the simulation experiment,which is compared with the traditional grid connected inverter circuit balance *** experimental results show that the improved particle swarm optimization algorithm can get the optimal solution in a short time,and improve the real-time response performance of the system.

关键词： improved particle swarm optimization algorithm Grid connected inverter circuit Balance control system Signal acquisition

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Determine the optimal switching angles symmetrical cascaded multilevel inverter using improved particle swarm optimization algorithm

Determine the optimal switching angles symmetrical cascaded ...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Sajjad Ahmadi Mostafa Andalib Sahnesaraie Saeid Hojjati Talami Fakhteh Dini Mahnaz Mohebbi Zanganeh Yazdan Ashgevari Department of electrical engineering Islamic Azad University Ardabil Iran Alborz Electric Power Distribution Company Alborz Iran Department of electrical engineering Islamic Azad University Mehriz Iran Faculty of electrical & Computer engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9781728131498

ISBN: (纸本)9781728131504

With the increasing use of inverters in the industry, the need to reduce the output harmonics is felt more than ever. Interstellar multi-level structures have grown exponentially due to benefits such as harmonic reduction and loss reduction. In multilevel inverters, nonlinear and complex equations become more complex when the number of surfaces increases. One of the ways to find optimal keying angles is to reduce the harmonics of the whole Newton-Raphson method. One of the main disadvantages of the Newton-Raphson method is the strong dependence of the answers to the initial guesses. By choosing different initial guesses, different answers may be obtained or results will not converge. And their solution is not possible by conventional methods of numerical solution of nonlinear equations such as the Newton-Ruffson method. In this thesis, one of a variety of Intelligent algorithms is used to solve the Newton-Raphson method problem. The particle swarm algorithm has been selected based on the proper background in solving problems of high complexity to solve the problem of optimal keying angles in this dissertation. In this thesis, the particle swarm algorithm has been improved. Simulation results for various multilevel converters have been presented and represent the high efficiency of the improved type of particle swarm algorithm in determining the appropriate angle of fire for reducing the high harmonics and generating a waveform with very low harmonic distortion and near sinusoidal.

关键词： Multilevel Inverters Selective Harmonic Elimination particle swarm optimization algorithm improved particle swarm optimization algorithm particle swarm algorithm Inventors Newton-Raphson method Theses multilayers harmonics Angle Optimal keying loss reduction

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improved particle swarm algorithm Based Multi-Objective optimization of Diaphragm Spring of the Clutch

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MECHANIKA 2022年第5期28卷 410-416页

作者： Zhou, Junchao Liu, Yihan Yin, Jilong Gao, Jianjie Hou, Naibin Sichuan Univ Sci & Engn Sch Mech Engn Intelligent Policing Key Lab Sichuan Prov Zigong Sichuan 643000 Luzhou 646000 Sichuan Peoples R China Sichuan Univ Sci & Engn Sch Mech Engn Zigong 643000 Sichuan Peoples R China Tianjin Res Inst Water Transport Engn & MOT Natl Engn Lab Port Hydraul Construction Technol Tianjin 300456 Peoples R China Sichuan Police Coll Intelligent Policing Key Lab Sichuan Prov Luzhou 646000 Sichuan Peoples R China Qianxi China Nucl Photovolta Power Generat Co LTD Hebei064300 Tangshan Peoples R China

Considering that diaphragm spring is the core com-ponent of the mechanical clutch, the optimization to which plays practical roles in engineering practices, the multi -ob-jective optimization model for the diaphragm spring of the clutch is established in this article. Aiming at the difficulty in local extremum due to pre-maturity of inertia weight and treatment on nonlinear constraint condition of standard par-ticle swarm optimization (PSO), the improved particle swarm algorithm (improved PSO) based on dynamic weight and hierarchical penalty function in consideration of the de-gree of congestion is proposed in this article to improve the original particle swarm algorithm. According to the results of calculating examples, the improved particle swarm algo-rithm can achieve better global searching ability and con-vergence ability;when compared with the calculating re-sults of the penalty function algorithm, the genetic algorithm and the NSGA-II algorithm, the pressing force of the dia-phragm spring with the new algorithm is increased by 3.24%, and the steering separation force is decreased by 20.09%. The diaphragm spring has better pressing force sta-bility and operating lightness, verifying the correctness of the model and the algorithm proposed in this article.

关键词： clutch diaphragm spring improved particle swarm optimization algorithm nonlinear constraint multi-stage Fractional penalty function

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Multi-Objective optimization for Turning Process of 304 Stainless Steel Based on Dung Beetle Optimizer-Back Propagation Neural Network and improved particle swarm optimization

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JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE 2024年第8期33卷 3787-3800页

作者： Xue, Huan Li, Tao Li, Jie Zhang, Yansong Huang, Shiyao Li, Yongchun Yang, Chongwen Zhang, Wenqian Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China Hubei Guoan Special Steel Inspect & Testing Co Huangshi 435000 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430076 Peoples R China

Austenite stainless steel of type 304 is one of the most difficult materials to process. During the machining process, parts easily generate higher surface residual stress in cutting direction (CD) and production costs. To solve the problem, a multi-objective optimization method that combined Dung Beetle Optimizer-Back Propagation Neural Network (DBO-BPNN) and improved particle swarm optimization (IPSO) algorithm was adopted. Firstly, BPNN, Genetic algorithm-Back Propagation Neural Network (GA-BPNN) and DBO-BPNN were established to map the nonlinear relationship between surface residual stress in CD and turning parameters. Secondly, a dataset of surface residual stress in CD and material removal rate under different turning parameters was obtained through finite element method (FEM) turning simulation. The dataset was applied into neural network to establish a nonlinear mapping relationship between turning parameters and surface residual stress in CD. The turning parameters are used as variables, the surface residual stress in CD and material removal rate are applied as objective functions. The optimal Pareto solution set of the surface residual stress in CD and material removal rate was acquired by combining DBO-BPNN and IPSO. Finally, turning experiments were designed to verify the accuracy of the turning simulation. The study shows that the surface residual stress in CD decreased by 38.47%, and the material removal rate increased by 91.69%.

关键词： 304 stainless steel dung beetle optimizer algorithm improved particle swarm optimization algorithm multi-objective optimization residual stress turning process

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Modeling and optimization of trajectory deviation for compound directional drilling in coal mines

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NEUROCOMPUTING 2025年 618卷

作者： Li, Wangnian Yang, Xiao Lu, Chengda Li, Quanxin Fang, Peng Wu, Xiao Huang, Hengyu Fan, Haipeng Yao, Ningping Tian, Hongliang Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China China Univ Geosci Sch Future Technol Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China CCTEG Xian Res Inst Grp Co Ltd Xian 710077 Peoples R China

During the sliding mode of compound directional drilling process in coal mines, the inclined angle and azimuth angle need to be changed to minimize the trajectory deviation between the actual trajectory and the designed trajectory. The inclined angle and azimuth angle can be changed by adjusting the tool face angle. In this article, a modeling and optimization method is proposed to obtain optimal tool face angle for sliding mode. Firstly, the gaussian process regression method is employed to build calculation model of trajectory deviation. The optimization model of trajectory deviation consists of the established calculation model of trajectory deviation and the set constraints. Then, the optimal inclination angle and azimuth angle are obtained by solving the constitutive optimization model using an improved particle swarm optimization algorithm. Next, the calculation model of tool face angle is built to represent the relationship between inclination angle, azimuth angle and tool face angle. The optimal tool face angle is calculated based on optimal inclined angle and azimuth angle. Finally, an industrial case study based on the actual drilling data verifies that the proposed method is beneficial to track designed trajectory during drilling process. The proposed method meets the requirements of on-site construction accuracy and ensures the safety and stability of drilling.

关键词： Trajectory deviation Gaussian process regression Tool face angle improved particle swarm optimization algorithm Sliding mode Compound directional drilling

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An EMG Denoising Method Based on Flexible Wearable Sensors

IEEE DESIGN & TEST

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IEEE DESIGN & TEST 2025年第2期42卷 7-16页

作者： Yan, Erxun Lu, Ping Ni, Haibin Nanjing Univ Informat Sci Technol Nanjing 210044 Peoples R China Nanjing Elect Engn Res Inst Nanjing 210023 Peoples R China

Editor’s notes: This article proposes an EMG denoising method that uses particle swarm optimization with wearable sensors. Compared to traditional denoising methods, the flexible electromyography sensor offers superior fit and wearability. The article verifies the proposed approach using self-acquired signals showing benefits over prior methods. —Ganapati Bhat, Washington State University, USA —Pietro Mercati, Intel Labs, USA

关键词： Electromyography Circuits Noise Noise reduction Electrodes Muscles Interference wearable Electromyography sensor improved particle swarm optimization algorithm improved wavelet thresholding variational mode decomposition

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