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Parameter identification of direct-drive permanent magnet synchronous generator based on EDMpso-EKF

IET RENEWABLE POWER GENERATION 2022年第5期16卷 1073-1086页

作者： Xiao, Qianghui Liao, Kaixian Shi, Chuandong Zhang, Yang Hunan Univ Technol Coll Elect & Informat Engn Zhuzhou Hunan Peoples R China

Aiming at the problem that extended Kalman filter (EKF) is difficult to determine the appropriate system and measurement noise matrices in parameter identification of direct-drive permanent magnet synchronous generator (D-PMSG), a parameter identification method of D-PMSG based on mean particle swarm optimization with extreme disturbance (EDMpso)-EKF is proposed. Firstly, by analysing the principle of EKF, a dual-thread identification model is established, then the particle swarm optimization (pso) algorithm is improved to jump out of the local optimum by adding extremum interference and taking average extremum, and a suitable fitness function is designed. Finally, the improved pso algorithm is applied to the adaptive optimization of EKF system noise matrix and measurement noise matrix, and the system performs the parameter identification after obtaining the optimal noise matrix. The simulation and experiment results show that the proposed method has better convergence speed and can more accurately identify the parameters of stator resistance, inductance and flux linkage, and it has better identification accuracy and generalization ability than the traditional method.

关键词： measurement noise matrix extremum interference stators power generation control improved pso algorithm taking average extremum appropriate system dual-thread identification model measurement noise matrices machine control adaptive optimization particle swarm optimisation permanent magnet generators mean particle swarm optimization parameter estimation synchronous generators Kalman filters identification accuracy particle swarm optimization algorithm direct-drive permanent magnet synchronous generator optimal noise matrix extreme disturbance-EKF generalization ability parameter identification method EDMpso-EKF nonlinear filters

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Prediction of bending parameters and automated operation planning for sheet-metal bending orientated to graphical programming

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第5-6期126卷 2191-2204页

作者： Xu, Fengyu Ding, Dawei Fan, Baojie Yang, Sen Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Jiangsu Engn Res Ctr IOT Intelligent Robots Nanjing Peoples R China

The core of graphical programming for sheet-metal bending is operation planning based on parameter identification, which can directly determine the bending quality and efficiency. Given this, the proposed graphical programming system and method can predict the inner arc radius of bending oriented to programming demands and optimize multi-step bending operations. At first, a neural network model based on multi-layer perceptron (MLP) was established, considering the number of neurons and hidden layers. In the bending process a fitness function was defined, and the discrete particle swarm optimization (Dpso) was improved using the adaptive inertia weight and the genetic algorithm (GA). Finally, the operation planning of 10-step and 20-step bending was simulated, and bending tests were conducted on sheet-metal workpieces for verification. The results show that the algorithm's maximum positive error and minimum negative error in predicting the arc radius are 0.12 mm and - 0.16 mm, respectively. When there are 10 bending steps, the particle swarm optimization-genetic algorithm (pso-GA) converges after only ten evolutions;when the bending steps increase to 20, the fitness value finally stabilizes at 1.31. The optimal operations of the experimental six-step bending part are 6 -> 5 -> 4 -> 3 -> 1 -> 2. This result indicates that the workpiece is turned over three times and turned around once in the sheet-metal bending process, which is consistent with the simulation, thus verifying the bending efficiency and accuracy.

关键词： Sheet-metal bending Graphical programming Operation planning Parameters prediction improved pso algorithm

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Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization

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AEROSPACE SCIENCE AND TECHNOLOGY 2019年 92卷 722-737页

作者： Tao, Jun Sun, Gang Fudan Univ Dept Aeronaut & Astronaut Shanghai 200433 Peoples R China Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA

In the present work, a multi-fidelity surrogate-based optimization framework is proposed, and then applied to the robust optimizations for airfoil and wing under uncertainty of Mach number. DBN (deep belief network) is employed as the low-fidelity model, and the k-step contrastive divergence algorithm is used for training the network. By virtue of the well trained DBN model and high-fidelity data, a linear regression multi-fidelity surrogate model is established. Verification results indicate that the multifidelity surrogate model obtains more accurate predictions than the DBN model and is highly reliable as a prediction model. The multi-fidelity surrogate model is embedded into an improved pso (particle swarm optimization) algorithm framework, and is updated in each iteration of the robust optimization processes for both airfoil and wing. Comparisons between multi-fidelity surrogate predictions and CFD results indicate that, the multi-fidelity surrogate predictions tend to approach the CFD results as the iteration number increases. The robust optimization results of airfoil and wing demonstrate that, the multi-fidelity surrogate model performs very well as a prediction model, and improves the optimization efficiency obviously. (C) 2019 Elsevier Masson SAS. All rights reserved.

关键词： Multi-fidelity surrogate model Deep learning Deep belief network improved pso algorithm Aerodynamic design optimization

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A new localization method based on improved particle swarm optimization for wireless sensor networks

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IET SOFTWARE 2022年第3期16卷 251-258页

作者： Yang, Qiaohe Shanghai Univ Sch Commun & Informat Engn Key Lab Special Fiber Opt & Opt Access Networks Shanghai Peoples R China

Wireless sensor network (WSN) node localisation technology based on received signal strength indication (RSSI) is widely used as it does not need additional hardware devices. The ranging accuracy of RSSI is poor, and the particle swarm optimisation (pso) algorithm can effectively improve the positioning accuracy of RSSI. However, the particle swarm diversity of the pso algorithm is easy to lose quickly and fall into local optimal solution in the iterative process. Based on the convergence conditions and initial search space characteristics of the pso algorithm in WSN localisation, an improved pso algorithm (improved self-adaptive inertia weight particle swarm optimisation [ISApso]) is proposed. Compared with the other two pso location estimation algorithms, the ISApso location estimation algorithm has good performance in positioning accuracy, power consumption and real-time performance under different beacon node proportions, node densities and ranging errors.

关键词： improved pso algorithm WSN localisation node densities improved particle swarm optimization particle swarm diversity ranging accuracy wireless sensor networks sensor placement ranging errors mobile radio particle swarm optimisation additional hardware devices initial search space characteristics ISApso location estimation algorithm different beacon node proportions localization method received signal strength indication self-adaptive inertia weight particle swarm optimisation pso location estimation algorithms positioning accuracy particle swarm optimisation algorithm search problems RSSI wireless sensor network node localisation technology local optimal solution

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Study on Charging Strategy of Electric Vehicle Parking Lot Based on improved pso

Study on Charging Strategy of Electric Vehicle Parking Lot B...

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第28届中国控制与决策会议

作者： Peng-fei ZHANG Wei-hui SHAO Hai-ni QU Wei-sheng XU Zhi-yu XU Shanghai Electric Power Company State Grid Corporation of China School of Electronics and Information Engineering Tongji University

ISBN: (纸本)9781467397155

Electric vehicle parking lot is a kind of profit service facilities nearby commercial office buildings for electric vehicles to park and charge.A V2 P model is built to describe electric vehicles charging in the parking lot based on V2G(vehicle to grid) *** the conditions of real-time pricing,improved particle swarm optimization(pso)algorithm is applied to study on charging strategies of electric ***-time electrovalency data of PJM and the parameters of mainstream electric vehicles are used in *** of the simulations show that the proposed model is reasonable and the improved pso algorithm is both efficient and effective.

关键词： Real-time Pricing Charging Strategy Electric Vehicle Parking Lot improved pso algorithm

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Particle swarm optimization method applied in total life cycle materials allocation of electricity engineering projects in the green and low-carbon supply chain

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 2390-2396页

作者： Si, Jiandong Shao, Xuwei Wu, Jianfeng Zhao, Xin Ye, Weijing Wang, Jing State Grid Jinhua Power Supply Co Jinhua 321000 Zhejiang Peoples R China State Grid Zhejiang Elect Power Co LTD Hangzhou 310007 Peoples R China State Grid Zhejiang Informat & Telecommun Co Hangzhou 310007 Peoples R China

Power grid enterprises face the problem of unbalanced supply and demand of power materials and unequal distribution;intelligent allocation of power materials can optimize the distribution and use of power resources through advanced technology and algorithms to ensure the efficient use of energy and stable supply. In this paper, the whole life cycle cost model of power supply chain is constructed and solved by improved particle swarm optimization (pso). This algorithm combines the advantages of standard pso and genetic algorithm (GA), avoids their disadvantages effectively, and realizes the effect of complementary advantages. This paper proposes using the sum of the costs associated with warehousing, transportation, and service penalties in the supply chain of electrical power projects as the objective function, thereby achieving the goal of optimal supply chain costs. In this study, the improved pso algorithm is used to optimize the objective function, and the optimization results are compared with those of standard pso algorithm and GA, revealing that its convergence is superior to the other two algorithms. Considering the influence of different factors on the cost, the superiority of the improved pso algorithm is further verified.

关键词： power supply supply chain configuration total life cycle improved pso algorithm

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Scheduling multi-mode resource-constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm

IET COLLABORATIVE INTELLIGENT MANUFACTURING

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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2021年第2期3卷 93-104页

作者： Xiao, Xiangjie Pan, Yaohui Lv, Lingling Shi, Yanjun Dalian Univ Technol Mech Engn Dept Dalian Peoples R China

A modified particle swarm optimization (pso) approach is presented for the multi-mode resource-constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of the AGV system are analysed, and the types and quantities of AGVs as allocable resources are considered. The multiple-AGV combined distribution mode and its impact on distribution tasks is also considered. Finally, a multi-mode resource-constrained task scheduling model is established for which the object is to minimise material delivery time. Based on the above model, the discrete particle swarm optimization algorithm that improved the basic pso was proposed. The simulation results with the test set in PSPLIB standard library showed the effectiveness of the improved pso algorithm.

关键词： automatic guided vehicles scheduling particle swarm optimisation distribution tasks resource allocation allocable resources multimode resource-constrained task scheduling model improved pso algorithm AGV system minimisation automated guided vehicle tasks material delivery time minimisation multiple-AGV combined distribution mode discrete particle swarm optimization algorithm goods distribution improved particle swarm optimization algorithm

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2-Staged Reconstruction Optimization for High Voltage Distribution Network Based pso algorithm

2-Staged Reconstruction Optimization for High Voltage Distri...

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1st International Conference on Energy and Environmental Protection (ICEEP 2012)

作者： Yuan, Guihua Li, Lin Li, Jun Hu, Zhijian Li, Xiaobo Zhang, Chengxue Shandong Elect Power Grp Co Dezhou Power Supply Co Dezhou Peoples R China Wuhan Univ Sch Elect Engn Wuhan Peoples R China

ISBN: (纸本)9783037854150

High voltage distribution network is an important link between transmission and middle voltage distribution network. If a high voltage distribution network fault occurs, the economy and reliability of the whole city distribution system will be seriously affected. To solve this problem, a traditional reconstruction optimization model is improved and a 2-staged reconstruction optimization model is proposed based on an improved particle swarm optimization (pso) algorithm. The first stage reconstruction only considers the economic goal and the second stage reconstruction needs consider the boundary constrains. The 2-staged method considers the economic benefit and the voltage quality simultaneously. It can achieve the multi goals: meeting actual boundary operation conditions, optimizing the combination of network losses and the number of operation switching. The fault isolation and reconfiguration of an actual distribution network system in Dezhou power grid were simulated. Simulation results validate the rationality and feasibility of the proposed method.

关键词： High voltage distribution network improved pso algorithm 2-staged reconstruction optimization

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Fault Diagnosis for Jointless Track Circuit Based on Intrinsic Mode Function Energy Moment and Optimized LS-SVM 5

Fault Diagnosis for Jointless Track Circuit Based on Intrins...

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IEEE International Conference on High Voltage Engineering and Application (ICHVE)

作者： Wang, Zicheng Guo, Jin Zhang, Yadong Luo, Rong Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Peoples R China Beijing Urban Construct Design Dev Grp Co Ltd Beijing Peoples R China

ISBN: (纸本)9781509004966

The track circuit electrical insulated joint faults will lead to the false acceptance of control information for trains and it will affect the safe operation of trains. In this paper the locomotive signal induced voltage model is established based on uniform transmission line theory and the amplitude envelopes of the induced voltage when the electrical insulated joint has malfunctioned are simulated. The fault features extraction is achieved through empirical mode decomposition (EMD) method because of its adaptive advantage. The induced voltage amplitude envelope signals are decomposed into several intrinsic mode functions (IMFs) and the IMF energy moments are used as fault characteristics. The least squares support vector machines (LS-SVMs) are built to realize the multi-class classification. Moreover, the optimal parameters of LS-SVM model are obtained by using the improved pso algorithm. The experiment shows that the fault diagnosis method for track circuit proposed in this paper is effective and the accuracy is higher than the conventional track circuit fault diagnosis approaches.

关键词： track circuit fault diagnosis EMD IMF energy moment improved pso algorithm LS-SVM

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improved Particle Swarm Optimization algorithm of Inverse Heat Conduction

Improved Particle Swarm Optimization Algorithm of Inverse He...

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11th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Qi Jingjing Guo Yucheng Zhou Ming Zhang Shesheng Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Peoples R China Wuhan Univ Technol Dept Stat Wuhan 430070 Peoples R China

ISBN: (纸本)9780769548180

Inverse Heat Conduction Problem (IHCP) has the characteristics of non-linearity, ill-posedness and heavy computation, because of which, there haven't been any theories or algorithms with broad application scope to solve it yet. In this paper, we use standard Particle Swarm Optimization (pso) algorithm to solve fine-mesh 2 Dimensions (2D) IHCP. The experimental data shows that the standard pso algorithm is not suitable for solving fine-mesh 2D IHCP. Considering the particular application fine-mesh 2D IHCP, the paper gives an improved strategy for the standard pso algorithm. Results show that the improved pso algorithm has a great improve in global search capability and solution precision.

关键词： 2D IHCP Fine-mesh pso algorithm improved pso algorithm

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