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Multi-objective optimal design of passive power filter for aircraft starter/generator system application

JOURNAL OF ENGINEERING-JOE 2018年第13期2018卷 636-641页

作者： Zhao, Dan Liu, Weiguo Shen, Ke Zhao, Guodong Wang, Xin Northwestern Polytech Univ Sch Automat Inst REPM Elect Machines & Control Technol Xian Shaanxi Peoples R China

A multi-objective optimisation design method for passive power filter devices is proposed to solve the input current harmonic suppression problem of the aircraft starter/generator system. The parameters of the filter device are optimised by using the particle swarm optimisation algorithm. The proposed algorithm takes harmonic parameters, DC voltage drop, total volume and weight as the optimisation target. With these optimisations, the filter has the minimal volume and weight, while the current harmonic and the DC side voltage drop are able to meet the power quality requirements. This approach improves the filter design procedure with advantages of the reduced amount of calculation. Finally, theoretical analysis and computer simulation validate the effectiveness and viability of the presented algorithm.

关键词： starting passive filters power supply quality particle swarm optimisation aircraft power systems power harmonic filters minimal volume DC side voltage drop power quality requirements filter design procedure multiobjective optimal design multiobjective optimisation design method passive power filter devices input current harmonic suppression problem particle swarm optimisation algorithm harmonic parameters DC voltage drop optimisation target aircraft starter-generator system application

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Power system stabiliser PSS4B-W parameters optimisation and RTDS test verification

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 3135-3139页

作者： Guo, Siyuan Zhang, Shoushou Zhu, Weijun Li, Li Li, Hui State Grid Hunan Elect Power Co Ltd Res Inst Power Syst Technol Ctr Changsha Hunan Peoples R China Cent South Univ Forestry & Technol Bangor Coll Changsha Hunan Peoples R China

This study introduces the mathematical model of a new type of power system stabiliser PSS4B-W. The concept of crossbreed in genetic algorithm is introduced to particle swarm optimisation algorithm and applied to PSS4B-W parameter optimisation. With a PCS-9410 excitation regulator, the semi-physical simulation system is constituted on a real time digital simulator (RTDS) platform. By measuring the excitation system phase frequency characteristic without compensation, the PSS4B-W parameter optimisation is converted to a set of parameters optimisation process with inequality constraint. In the RTDS platform, the time-domain dynamic response of without PSS, adding PSS2B and PSS4B-W, is compared and analysed. Simulation results show that the optimised PSS4B-W can provide effective damping in different frequency bands, and have good inverse-regulation suppression effect.

关键词： power system stability dynamic response particle swarm optimisation genetic algorithms time-domain analysis optimisation inverse-regulation suppression effect time-domain dynamic response inequality constraint excitation system phase frequency characteristic genetic algorithm mathematical model power system stabiliser PSS4B-W parameter optimisation particle swarm optimisation algorithm parameters optimisation process RTDS platform semiphysical simulation system PCS-9410 excitation regulator RTDS test verification

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Improved grey wolf optimisation algorithms

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JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 615-619页

作者： Gao, Zheng-ming Zhao, Juan Hu, Yu-rong Jingchu Univ Technol Sch Comp Engn Jingmen Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen Peoples R China Jingchu Univ Technol Dept Sci & Technol Jingmen Peoples R China

The grey wolf optimisation (GWO) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nature. However, it still has its capability to be improved in the convergence ratio, stability, and reduce the errors. And it is also easily trapped in local optimum and converged slowly approaching the end, which is just the same defect appearing in other meta-heuristic algorithms such as the bat algorithm (BA), the particle swarm optimisation (PSO) algorithm, and the genetic algorithm (GA). Lots of improvements have been proposed before. In this paper, we propose an improved GWO algorithm inspired by the PSO algorithm to fasten the convergence ratio and reduce the errors. Empirical work and verifications are carried out;and results show its better performance than the standard GWO algorithm and other well-known meta-heuristic algorithms

关键词： grey systems particle swarm optimisation genetic algorithms meta-heuristic algorithms bat algorithm particle swarm optimisation algorithm genetic algorithm improved GWO algorithm PSO algorithm convergence ratio standard GWO algorithm improved grey wolf optimisation algorithm outstanding capabilities

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Trajectory tracking based on adaptive fast non-singular terminal sliding mode control

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2022年第1期16卷 24-34页

作者： Chen, Jiqing Liu, Xu Zhao, Chaoyang Mo, Rongxian Huang, Chunlin Cai, Ganwei Guangxi Univ Sch Mechatron Engn Nanning 530004 Guangxi Peoples R China Nanning ZhengTeng Agr Machinery Co Ltd Nanning 530004 Peoples R China

Aiming at the binocular vision system with dynamic errors and external disturbances, a trajectory tracking control method based on the adaptive fast non-singular terminal sliding mode is proposed. Firstly, the fast non-singular terminal sliding mode is adopted to ensure that the system converges in a finite time while avoiding singularity;Secondly, the adaptive radial basis function neural network (RBFNN) is used to approximate the dynamics errors, friction and external disturbance to eliminate the influence of uncertain factors;Thirdly, the particle swarm optimisation (PSO) algorithm is used to optimise the parameters to avoid the mapping failure caused by unreasonable parameter setting;Finally, the Lyapunov function is established according to the Lyapunov stability, and the theoretical stability of the system is proved. The experimental results show that the proposed trajectory tracking control method can effectively improve the convergence speed and tracking accuracy, enhance the robustness and weaken the chattering of the system.

关键词： trajectory tracking terminal sliding mode control radial basis function neural network particle swarm optimisation algorithm

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Vehicle chaos identification and intelligent suppression under combined excitation of speed bump and engine

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1595-1599页

作者： Hu, Qi-guo Yuan, Shuai-hui Han, Ao Chongqing Jiaotong Univ Coll Electromech & Automobile Engn Chongqing Peoples R China Chongqing Jiaotong Univ Coll Transportat Chongqing Peoples R China

For non-linear suspension, vehicle passes through a continuous speed bump, the chaos that may occur under the combined excitation of the speed bump and the engine. This study takes the five-degree-of-freedom vehicle model as the object of research, through the vehicle body poincare section and the maximum Lyapunov index to identify the chaos produced by the vehicle under joint excitation, and utilises the feedback control of the optimal feedback gain coefficient based on the particle swarm optimisation (PSO) algorithm to suppress vehicle chaos. The results indicate that the vehicle is in a chaotic state in all speed range. Under low and medium speeds, the route to the chaos of the vehicle is the system coupling vibration under multi-frequency excitation, whereas in the high-speed condition, the vehicle approaches the chaos through the bifurcation. The chaos of the vehicle can be effectively suppressed by feedback control with the global optimal feedback gain searched by PSO. This study reveals the chaotic characteristics of non-linear suspension vehicles under combined excitation, which provides a new method for intelligent suppression of chaos.

关键词： suspensions (mechanical components) Lyapunov methods chaos feedback particle swarm optimisation bifurcation vibrations vehicle dynamics road vehicles combined excitation bifurcation system coupling vibration Lyapunov index vehicle chaos speed bump global optimal feedback gain speed range particle swarm optimisation algorithm optimal feedback gain coefficient feedback control five-degree-of-freedom vehicle model intelligent suppression

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Fault recovery for distribution network with distributed generation

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The Journal of Engineering 2018年第13期2017卷 2384-2388页

作者： Wei Ba Yuming Yan Kangda Wang Bingqing Wu Zhenghong Chen Xueying Wang Department School of Electrical Engineering Xi'an Jiaotong University Xi'an People's Republic of China

Distributed generation (DG) has been largely used in distribution network in recent years, which brings more security, reliability, and economy, but some bad effects to the distribution network. In this study, recovering more loads, less switching actions and distribution network power loss are considered to establish the mathematical model of fault recovery in distribution network with DG. The particle swarm optimisation algorithm is used to solve the problem, and the fault recovery strategy is obtained. Besides, the impact of DG location on the fault recovery of distribution network is studied, and the simulation results show that when the DG is located dispersedly in the downstream of the distribution network, the distribution network recovery is in good condition. The results of this study provide a reference for the DG configuration of the distribution network.

关键词： power distribution faults distributed power generation particle swarm optimisation distributed generation distribution network power loss mathematical model particle swarm optimisation algorithm fault recovery strategy DG location

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Sports Image Feature Extraction Based on Machine Learning and Global Search algorithm

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第0期

作者： Li, Haiting Yan, Jinghao Henan Univ Anim Husb & Econ Sports Dept Zhengzhou 450046 Peoples R China Shanghai Cyonet Infortek Ltd Shanghai 201619 Peoples R China

This study utilizes a combination of machine learning and a global search algorithm to enhance the quality of feature extraction in sports images. A deep residual generative adversarial network is employed to deblur images and sharpen their clarity, while the optimized particle swarm optimization algorithm is utilized to extract image features with precision and identify critical information. According to the experimental results, the research method improves the peak signal-to-noise ratios in ball sports image deblurring by 12.45%, 13.91%, and 17.18%, respectively, and in track and field sports image deblurring by 11.71%, 12.91%, and 21.61%, respectively, when compared with the generative adversarial network, generative adversarial network incorporating the attention mechanism, and multi-scale convolution-based algorithm. The accuracy-recall curve of the particle swarm algorithm that has been optimised for research completely encircles the accuracy-recall curves of the other four algorithms, which verifies the efficacy of the research methodology. The research will offer a more comprehensive perspective on sports image processing.

关键词： Sports images feature extraction deblurring deep residual generative adversarial networks particle swarm optimisation algorithm

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Harmonic control optimisation model considering power generation of microgrid

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The Journal of Engineering 2018年第13期2017卷 1565-1569页

作者： Ning Wang Kai Wei Jie Yuan Qingquan Jia College of Electrical Engineering Yanshan University Qinhuangdao 066000 Hebei Province People's Republic of China

In a microgrid with a large amount of photovoltaic (PV), the harmonic pollution problem is mostly caused by the non-linear load in the PV system and the microgrid. The multi-function grid-connected inverter (MFGCI) and the active power filter (APF) used to control the harmonic pollution is integrated into the framework of the bi-layer planning model in this paper. The upper layer model of the bi-layer optimisation is based on the cost of the microgrid power generation with PV as the objective function. Consider the maintenance costs of PV power generation and MFGCI equipment, line loss costs, purchase expectations and sales period expectations. The lower objective function is the minimum root mean square of the harmonic distortion rate of each node of the microgrid satisfying the MFGCI and APF capacity allocation constraint. The improved particle swarm optimisation algorithm and Latin hypercube technology are used to solve the bi-layer planning model. The results show that in the microgrid, the integrated optimisation method based on the bi-layer planning model is optimised for MFGCI and APF. It has a significant effect on the efficient operation of the microgrid and the improvement of power quality level of the microgrid.

关键词： least mean squares methods particle swarm optimisation invertors power harmonic filters photovoltaic power systems distributed power generation objective function PV power generation line loss costs purchase expectations sales period expectations minimum root mean square bi-layer optimisation bi-layer planning model harmonic pollution APF active power filter MFGCI multifunction grid-connected inverter PV system microgrid power generation harmonic control optimisation model power quality level Latin hypercube technology particle swarm optimisation algorithm harmonic distortion rate

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Analysis for distribution network on hosting capacity of distributed wind turbines considering additional income under procedure conditions

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The Journal of Engineering 2018年第13期2017卷 1373-1377页

作者： Ye Wang Yuwei Yu Jie Zhang Anhui Huadian Engineering Consultating & Design CO Ltd No.122 Jinzhai Road Baohe District Hefei 230022 People's Republic of China CYG Sunri Co. Ltd Room 401 No. 2. Building Zin Entrepreneur R$D Centre No. 89 Shengli Road Jiangning District Nanjing 211100 People's Republic of China

An increasing number of wind turbines (WTs) are integrated into the distribution network to generate more electricity and reduce energy cost, which has a significant influence on the distribution network. So how to calculate the hosting capacity of WTs is of great importance. In this study, the analysis method was proposed to calculate the hosting capacity of WTs integrated into the distribution network under the procedure conditions. The authors firstly analysed several factors that imposed restrictions on the hosting capacity of the distribution network for WTs. Secondly, the authors built the optimisation model considering additional income, which aimed at reaching the maximum economic benefits by WT's access. Then the particle swarm optimisation algorithm, which could be applied to the problem, was given. In the calculation example, the authors analysed the different situation of WT's access to verify the proposed method. The installation points of WTs and the cost of investments were discussed to compare the economy of integration of WTs with the analysis results. The results showed that integrating WTs into the middle of lines could promote the economy and the decline in the cost of WTs also helped to enhance the hosting capacity of the distribution network for WTs.

关键词： wind turbines wind power plants distributed power generation power distribution economics power generation economics cost reduction particle swarm optimisation investment distribution network distributed wind turbines hosting capacity energy cost reduction particle swarm optimisation algorithm investments WT integration

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Research on Supply Chain optimisation Management Method Integrating Employee Behaviour Factors by Improving PSO algorithm

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2024年第1期23卷

作者： Li, Wenhui Wang, Can Zhengzhou Railway Vocat & Tech Coll Sch Transportat Management Zhengzhou 451460 Peoples R China Zhengzhou Railway Vocat & Tech Coll Sch Artificial Intelligence Zhengzhou 451460 Peoples R China

With the continuous advancement of the trend of economic globalisation and the in-depth development of personalised services, the manufacturing mode has begun to change to service-oriented manufacturing, and the focus of enterprises has gradually shifted from the industrial chain to the supply chain. However, at present, accidents often occur in the supply chain around products, such as changes in orders, lack of resources in a short period of time, etc. These interference events are difficult to control and cause great damage to the normal operation and economic interests of enterprises for a long time. Therefore, it is necessary to study the optimisation methods of enterprise supply chain. Therefore, it is necessary to study the optimisation methods of enterprise supply chain. The study uses system dynamics to analyses employee counterproductive behaviour, develops a disturbance management model incorporating employee behavioural factors, and solves it with an improved particle swarm optimisation (PSO) algorithm. The experimental results show that the maximum number of noninferior solutions obtained by the improved PSO algorithm is 14 and 12, respectively. Compared with the GA_TOM (Genetic algorithm_TOM), the improved algorithm is closer to the ideal pareto front. In the MS index, the average and minimum values obtained by the improved PSO algorithm are 0.57 and 0.609, respectively, which can cover more ideal pareto fronts. It shows that the algorithm effectively improves the stability and security of the supply chain, and provides a practical reference for the supply chain optimisation of manufacturing enterprises.

关键词： Anti-production behaviour particle swarm optimisation algorithm supply chain interference management

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