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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Teng, Yuanxin Wang, Guan Su, Xiaomeng Wu, Meiying Shandong Univ Sch Elect Engn Jinan Peoples R China Guangdong Power Grid Guangzhou Power Supply Bur Guangzhou Peoples R China

ISBN: (纸本)9798350339345

The content of dissolved gas in transformer oil is an important characteristic to reflect the operating condition of transformers. To overcome the problems that the traditional transformer oil chromatography gas prediction model cannot effectively use the older transformer oil chromatography data and it is difficult to track the transformer oil chromatography data under abnormal conditions and it can only rely on experience to adjust the model parameters, we introduce Gated Recurrent Unit (GRU), Attention Mechanism (APM) and particle swarm optimization (PSO) to construct the Attention-PSO-GRU prediction model. Firstly, the transformer oil chromatography monitoring data are pre-processed, and the attention mechanism is introduced into the time channel to realize the redistribution of the input data weights. Then, the hyperparameters in the GRU model are optimality-seeking iterated by PSO to realize the adaptive tuning of the model. Finally, the optimized GRU neural network is used to extract spatiotemporal features from transformer oil chromatography monitoring data and construct the Attention-PSO-GRU transformer oil chromatography gas prediction model. The experimental results show that the Attention-PSO-GRU model can reach 97.9% accuracy in predicting the normal transformer oil chromatography data and has a better tracking ability for the abnormal transformer oil chromatography data. Therefore, the Attention-PSO-GRU model proposed in this paper can effectively improve the accuracy of transformer oil chromatography gas prediction, which has practical significance for preventing transformer faults and ensuring the safe and stable operation of power systems.

关键词： oil chromatography gas content prediction attention mechanism particle swarm optimization algorithm Gated recurrent neural network

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Application Research of Wireless Sensor Network in Structural Health Monitoring of Civil Engineering 2

Application Research of Wireless Sensor Network in Structura...

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2nd IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Li, Songtao Gao, Yanlong Wang, Baolin Tian, Ruxue Zhengzhou Railway Vocational & Technical College College of Railway Engineering Zhengzhou China Henan Jiaoyuan Engineering Technology Group Co. Ltd Zhengzhou China Zhengzhou Road Inspection Engineering Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9798350368208

Aiming at the needs of structural health monitoring in civil engineering, this paper deeply studies the application of wireless sensor networks in it, and focuses on the design and optimization of intelligent sensor networks. First, this paper outlines the important role of wireless sensor networks in civil structural health monitoring, including real- time data acquisition, remote monitoring and fault warning. Subsequently, a structural health monitoring method based on modal recognition technology is introduced in detail, which evaluates the health status of the structure by analyzing its dynamic response. This paper introduces a particle swarm optimization algorithm to optimize the layout and parameter settings of the sensor network to improve the accuracy and reliability of the monitoring system. The effectiveness of the proposed method is verified through system simulation. The simulation results show that the optimized sensor network can more accurately capture the abnormal behavior of the structure and maintain stable performance under various working conditions. The research results of this paper provide an efficient technical solution for the structural health monitoring of civil engineering, which has important theoretical significance and practical application value. © 2024 IEEE.

关键词： Civil structure health monitoring intelligent sensor network modal recognition particle swarm optimization algorithm

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Numerical Research on Suppression of Thermally Induced Wavefront Distortion of Solid-state Laser Based on Neural Network

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CURRENT OPTICS AND PHOTONICS 2022年第5期6卷 479-488页

作者： Liu, Hang He, Ping Wang, Juntao Wang, Dan Shang, Jianli China Acad Engn Phys Inst Appl Elect Mianyang 621900 Sichuan Peoples R China China Acad Engn Phys Grad Sch Beijing 100088 Peoples R China China Acad Engn Phys Key Lab Sci & Technol High Energy Laser Mianyang 621900 Sichuan Peoples R China

To account for the internal thermal effects of solid-state lasers, a method using a back propagation (BP) neural network integrated with a particle swarm optimization (PSO) algorithm is developed, which is a new wavefront distortion correction technique. In particular, by using a slab laser model, a series of fiber pumped sources are employed to form a controlled array to pump the gain medium, allowing the internal temperature field of the gain medium to be designed by altering the power of each pump source. Furthermore, the BP artificial neural network is employed to construct a nonlinear mapping relationship between the power matrix of the pump array and the thermally induced wavefront aberration. Lastly, the suppression of thermally induced wavefront distortion can be achieved by changing the power matrix of the pump array and obtaining the optimal pump light intensity distribution combined using the PSO algorithm. The minimal beam quality beta can be obtained by optimally distributing the pumping light. Compared with the method of designing uniform pumping light into the gain medium, the theoretically computed single pass beam quality beta value is optimized from 5.34 to 1.28. In this numerical analysis, experiments are conducted to validate the relationship between the thermally generated wavefront and

关键词： Back propagation neural network particle swarm optimization algorithm Pump array Lasers diode -pumped

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Micro-amplitude vibration suppression of a bistable nonlinear energy sink constructed by a buckling beam

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NONLINEAR DYNAMICS 2022年第4期108卷 3185-3207页

作者： Zeng, You-cheng Ding, Hu Du, Rong-Hua Chen, Li-Qun Shanghai Univ Sch Mech & Engn Sci Shanghai Inst Appl Math & Mech Shanghai Key Lab Mech Energy Engn Shanghai 200444 Peoples R China Changsha Univ Sci & Technol Coll Automot & Mech Engn Changsha 410114 Peoples R China

Effective reduction of micro-amplitude vibration has always been a serious challenge. The nonlinear energy sink (NES) has been proven to be able to reduce vibration at a wide frequency. However, the energy threshold of the NES prevents it from suppressing micro-vibrations. In this paper, a bistable nonlinear energy sink (BNES) based on a buckling beam oscillator is constructed. The threshold of the NES is lowered by the nonlinear dynamic behavior of jumping between wells of the bistable oscillator. The motion equations of the discrete-continuous system are derived by using Hamilton's principle. The approximate analytical solution is obtained and verified numerically. The results show that even when the primary system has a micro-amplitude resonance, the nonlinear cross-well vibration of the BNES can still reduce the vibration. The robustness of the BNES is stronger than that of the tuned mass damper (TMD). The optimal parameters of the BNES are obtained with particle swarm optimization (PSO) algorithm. The result of parameter optimization shows that the energy threshold of the nonlinear energy sink can be effectively lowered. In short, the method based on nonlinear dynamics in this paper provides an effective reduction strategy for micro-vibration in engineering. [GRAPHICS]

关键词： Buckling beam Bistable Micro-amplitude vibration particle swarm optimization algorithm Harmonic balance method

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Real-time implementation and evaluation of gorilla troops optimization-based control strategy for autonomous microgrids

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IET RENEWABLE POWER GENERATION 2022年第14期16卷 3071-3091页

作者： Maged, Nourhan A. Hasanien, Hany M. Ebrahim, Essamudin A. Tostado-Veliz, Marcos Jurado, Francisco Elect Res Inst Power Elect & Energy Convers Dept Cairo Egypt Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo 11517 Egypt Univ Jaen Super Polytech Sch Linares Dept Elect Engn Linares Spain

The present work develops a novel gorilla troops optimization algorithm (GTO) for optimal control of an autonomous microgrid. The proposed proportional-integral controller parameters are properly designed by the GTO algorithm. It is based on the integral squared error to minimize the system objective function. The suggested islanded microgrid system operates with several decentralized, electronically interfaced energy resources and their local loads. The new optimal controller is applied to regulate its voltage at the different points of common coupling and its frequency despite the load variation. The performance of the new proposal is compared with the PI controller that was designed by a particle swarm optimization algorithm. It is observed that the microgrid functioning operation is improved even more by applying a GTO-based than the other. The control strategy is thoroughly tested using a Matlab/Simulink simpower-system environment under various microgrid operating situations. It is also implemented with OPAL 4510 real-time hardware in the loop and a rapid control prototyping module. The different benchmark experiments serve to show the superiority of the new optimizer.

关键词： PI control power engineering computing power generation control distributed power generation islanded microgrid system real-time implementation PI controller optimal controller microgrid functioning operation GTO algorithm power system simulation optimal control particle swarm optimisation proportional-integral controller parameters rapid control prototyping module autonomous microgrid particle swarm optimization algorithm power distribution control power distribution faults integral squared error load variation OPAL 4510 real-time hardware gorilla troops optimization-based control strategy system objective function voltage control interfaced energy resources Matlab-Simulink simpower-system environment

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Design of beamforming metasurface based on fully connected neural networks 14

Design of beamforming metasurface based on fully connected n...

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14th International Symposium on Antennas Propagation and EM Theory

作者： Li, Shi Sheng, Lili Wen, Jinfang Cao, Weiping Lu, Lucong Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China

ISBN: (纸本)9798350353129;9798350353136

In order to solve the problem that the period of designing metasurface with specific functions is too long by using electromagnetic simulation software, this paper proposes a metasurface design method based on artificial neural network, which can accurately predict the far-field pattern. In the training process, the root-mean-square error decreases with the increase of the number of iterations, the minimum root-mean-square error is 0.01, and the training time is 21 s. After the training is completed, the model can quickly predict the far-field pattern according to the coding matrix. The single prediction time of the neural network is 0.0065 s, while the single simulation time of the full-wave simulation software is 4 min, which is far more than that of the neural network. The accuracy of the predicted data is verified by comparing the predicted results with the results of the full-wave simulation. By combining the neural network with particle swarm optimization algorithm, the metasurface coding matrix corresponding to the target pattern can be designed. Compared with the traditional metasurface design method, this design method greatly reduces the design time and solves the problems of time consuming and cost in the traditional design method.

关键词： Neural network particle swarm optimization algorithm Beamforming metasurface Coding matrix Far-field pattern

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Cooperative Location Method of Multi-UAV Based on TDOA and FDOA

Cooperative Location Method of Multi-UAV Based on TDOA and F...

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International Conference on Autonomous Unmanned Systems (ICAUS)

作者： Liu Mengnan Zhang Yiwen Jin Ying Huan Yingxing Wang Jing Avic Chengdu Aircraft Ind Grp Co Ltd Chengdu Sichuan Peoples R China

ISBN: (纸本)9789819904815;9789819904792;9789819904785

Aiming at the nonlinear optimization problems in the process of locating radiation source by using moving multiple UAVs in passive detection system. This paper presents a joint location technology of TDOA/FDOA based on particle swarm optimization algorithm to achieve high-precision positioning of the target. The linearly decreasing inertia weight and natural selection mechanism are combined with this new method, which will effectively avoid positioning results falling into local optimal values, increase population diversity and improve the quality of the solution. This method can approximate the global optimal solution without initial target position estimation. In order to be applied to practical engineering applications, the WGS-84 geodetic coordinate system will be introduced to describe the earth model. From the simulation results, the proposed method has higher locating accuracy compared to standard particle swarm optimization algorithm. In the end of this paper, the GDOP positioning accuracy of the target reconnaissance area is analyzed by using different UAV formation layout, which provides some reference value for practical engineering application.

关键词： Passive location Time difference of arrival Frequency difference of arrival particle swarm optimization algorithm Unmanned aerial vehicle

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Variable transmission ratio design of a steer-by-wire system for intelligent vehicles

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第16期236卷 9341-9353页

作者： Liu, Zhenyu Xu, Xing Xie, Ju Wang, Feng Su, Pengwei Jiangsu Univ Automot Engn Res Inst 301 Xuefu Rd Zhenjiang 212013 Jiangsu Peoples R China

Intelligent vehicles require the ability to switch freely between manual and automatic driving modes, which opens up more possibilities for steer-by-wire. Among them, the design of transmission ratio for both driving modes can improve the steering performance of vehicle. In the manual driving mode, this paper optimizes the yaw rate gain upon the particle swarm optimization (PSO) algorithm and designs a variable steering transmission ratio, which can reduce the driver's burden and improve the vehicle's handling stability. Further, the steering wheel angular rate information and fuzzy control are used to design the high-speed transmission ratio to ensure the safety of the vehicle in high-speed emergency situations. In the automatic driving mode, the transmission ratio values at various vehicle speeds are optimized offline based on the PSO algorithm, and a variable transmission ratio curve is designed to enhance the trajectory tracking ability and the vehicle stability. Finally, the hardware-in-the-loop test results show that the variable transmission ratio designed in the manual driving mode reduces the burden of the driver at low speed and improves the vehicle stability at high speed, especially in emergency situations. It also proves that the optimized design of variable ratio in the automatic driving mode promotes the path-following ability and the vehicle stability.

关键词： Steer-by-wire variable transmission ratio automatic driving particle swarm optimization algorithm hardware-in-the-loop test

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Research on the maximum power point tracking method of photovoltaic based on Newton interpolation-assisted particle swarm algorithm

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Clean Energy 2022年第3期6卷 496-502页

作者： LiMing Wei KaiKai Li School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchunJilin130118China

Solar energy has attracted a lot of attention because it is clean and has no ***,due to the partially shaded condition,the photovoltaic array cannot work uniformly at the maximum power point,resulting in a large power *** improve this problem,the research of the maximum power point tracking(MPPT)algorithm is discussed by *** this paper,an improved particle swarm optimization(PSO)algorithm is proposed to achieve the goal of MPPT,which uses Newton interpolation-assisted conventional *** tracking to the maximum power point,the Newton interpolation method is used to calculate the maximum power point,reduce the number of iterations of the conventional PSO algorithm and reduce the steady-state *** simulation is carried out in MATLAB^(■)/Simulink^(■)and compared with conventional *** results show that the improved PSO has better tracking ac-curacy and speed than the conventional algorithm,and the initial tracking speed is increased by>30%.

关键词： photovoltaic array maximum power point tracking particle swarm optimization algorithm Newton interpolation method

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Fault Diagnosis of Chemical Processes Based on PSO-GCN

Fault Diagnosis of Chemical Processes Based on PSO-GCN

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2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Luo, Chen Yang, Jin School of Aeronautical Engineering Civil Aviation University of China China School of Transportation Science and Engineering Civil Aviation University of China China

ISBN: (纸本)9798331520816

As industrial production advances, ensuring process safety and minimizing failure-related losses is crucial. Detecting abnormal conditions efficiently is key. To address the complexity, high dimensionality, nonlinearity, and time-varying nature of chemical process data, this paper proposes a fault diagnosis method based on particle swarm optimization (PSO) and Graph Convolutional Networks (GCN). The method denoises the data, constructs a graph using a sliding window, and uses PSO to optimize the diagnostic model's parameters and extract features. These features are then classified via a fully connected layer and classifier. Experimental results show that the method outperforms other machine learning and deep learning approaches in accuracy and precision, and excels at detecting minor faults in chemical processes. © 2024 IEEE.

关键词： Chemical Processes Fault Diagnosis Graph Convolutional Networks particle swarm optimization algorithm

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