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2023 IEEE Sustainable Power and Energy Conference, iSPEC 2023

作者： Zhang, Peng Mo, Zhonghua Mastoi, Muhammad Shahid Yang, Yinghai Cai, Shaorong Hassan, Mannan Wang, Delin The Southwest Branch of State Grid Corporation Sichuan Chengdu610102 China School of Electrical Engineering Southwest Jiaotong University Sichuan Chengdu611756 China

ISBN: (纸本)9798350322699

This paper proposes an AGC frequency control strategy using response-compensated distributed model predictive control (DMPC) when large-scale wind power is incorporated into the automatic generation control (AGC) of power systems to optimize the response speed of frequency modulation control systems based on model predictive control. It examines the influence of AGC system parameters on wind power penetration rate based on frequency control models for conventional units and doubly-fed wind turbines. A response compensation link is added to DMPC, and the particle swarm optimization algorithm is used to set the control parameters of the optimization link and analyze the stability of the system after the link is added. A frequency control strategy based on response compensation DMPC is then proposed based on system frequency deviation. Simulations are conducted to verify the AGC model of a regional interconnected power system with large-scale wind power. Results indicate that the proposed control strategy can improve system frequency stability and speed up frequency control response times. © 2023 IEEE.

关键词： automatic generation control frequency stability large-scale wind power model predictive control particle swarm optimization algorithm

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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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MECHANICAL PROPERTY PREDICTION MODEL OF STRIP BASED ON PSO-BP NEURAL NETWORK

MECHANICAL PROPERTY PREDICTION MODEL OF STRIP BASED ON PSO-B...

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2007中国宁波国际计算机科学与技术学术大会

作者： Wang Xiaolin Wang Pengfei Liu Hongshen Huang Zhenyi Department of Computer Science & Technology Anhui University of Technology Department of Materials Science & Engineering Anhui University of Technology

ISBN: (纸本)9780972147903

Mechanic property prediction of hot rolled strip has enormous economic benefits and vast applied *** neural network is used almost in Mechanic property prediction of hot rolled strip in *** BP has local infinitesimal *** with PSO(particle swarm optimization) global optimization algorithm,a new PSO-BP neural network is *** PSO-BP algorithm takes on advantages of global optimization ability,the rapid constringency of BP rapid training algorithm,the ability of nonlinear approach of multilayer feedforward network,and improves the performance of Neural ***-BP neural network model has good application foreground.

关键词： Mechanical property prediction particle swarm optimization algorithm BP neural network PSO-BP

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Improving performance of open-pit mine production scheduling problem under grade uncertainty by hybrid algorithms

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Journal of Central South University 2020年第9期27卷 2479-2493页

作者： Kamyar TOLOUEI Ehsan MOOSAVI Amir Hossein BANGIAN TABRIZI Peyman AFZAL Abbas AGHAJANI BAZZAZI Department of Petroleum and Mining Engineering South Tehran BranchIslamic Azad UniversityTehranIran Department of Mining Engineering University of KashanKashanIran

One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s *** the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit *** and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this *** the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling *** the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational *** the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade *** is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the ***,the Lagrangian coefficients are updated by using PSO and *** presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian *** results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid ***,it is more effectual than the conventional ***,the time and cost of computation are diminished by the proposed hybrid *** CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.

关键词： open-pit mine long-term production scheduling grade uncertainty augmented Lagrangian relaxation particle swarm optimization algorithm bat algorithm

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Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

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Journal of Central South University 2021年第8期28卷 2333-2344页

作者： JI Ya-feng SONG Le-bao SUN Jie PENG Wen LI Hua-ying MA Li-feng School of Mechanical Engineering Taiyuan University of Science and TechnologyTaiyuan 030024China State Key Laboratory of Rolling and Automation Northeastern UniversityShenyang 110819China School of Materials Science and Engineering Taiyuan University of Science and TechnologyTaiyuan 030024China

To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip ***,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple ***,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a ***,the prediction performance comparisons of the three models are ***,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence ***,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μ***,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.

关键词： strip crown support vector machine principal component analysis cuckoo search algorithm particle swarm optimization algorithm

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Optimal Scheduling Method for Cogeneration System Based on Heat Storage Device

Optimal Scheduling Method for Cogeneration System Based on H...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhang Xu Pang Xinfu Zhao Wenjing Liu Li Zhang Yifan Mou Zurong Shenyang Inst Engn Key Lab Energy Saving & Controlling Power Syst Li Shenyang 110136 Peoples R China Shenyang Inst Engn Sch Elect Power Shenyang 110136 Peoples R China Shenyang Inst Engn Sch Automat Shenyang 110136 Peoples R China State Grid Liaocheng Elect Power Supply Co Liaocheng 252000 Shandong Peoples R China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158556

In the north of China, the problem of wind abandonment is very serious. In order to improve the ability of wind power consumption, a cogeneration system with an electric boiler is proposed. First, the mathematical model of the minimum power generation cost of the traditional unit and the optimized model are established. Second, the model is solved by using the particle swarm optimization algorithm. In addition, a power structure of combined heat and power system (CHP) is constructed for simulation experiments. Through experimental analysis, proving the feasibility of the model. Finally, Simulation results show that making the electric boiler work in the period of wind abandonment can effectively alleviate the wind abandonment phenomenon. It also can provide more space for the wind power and enhance the wind power consumption.

关键词： Combined heat and power generation Wind abandonment Electric boiler Wind power particle swarm optimization algorithm

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swarm intelligence optimization and its application in geophysical data inversion

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Applied Geophysics 2009年第2期6卷 166-174页

作者： Yuan Sanyi Wang Shangxu Tian Nan CNPC Key Laboratory of Geophysical Exploration Key Laboratory of Earth Prospecting and Information Technology China University of Petroleum Beijing 102249 China

The inversions of complex geophysical data always solve multi-parameter, nonlinear, and multimodal optimization problems. Searching for the optimal inversion solutions is similar to the social behavior observed in swarms such as birds and ants when searching for food. In this article, first the particle swarm optimization algorithm was described in detail, and ant colony algorithm improved. Then the methods were applied to three different kinds of geophysical inversion problems：（1） a linear problem which is sensitive to noise, （2） a synchronous inversion of linear and nonlinear problems, and （3） a nonlinear problem. The results validate their feasibility and efficiency. Compared with the conventional genetic algorithm and simulated annealing, they have the advantages of higher convergence speed and accuracy. Compared with the quasi-Newton method and Levenberg-Marquardt method, they work better with the ability to overcome the locally optimal solutions.

关键词： swarm intelligence optimization geophysical inversion multimodal particle swarm optimization algorithm

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Micro-grid Dynamic Modeling Based on RBF Artificial Neural Network

Micro-grid Dynamic Modeling Based on RBF Artificial Neural N...

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International Conference on Power System Technology (PowerCon)

作者： Cai Changchun Wu Min Deng Lihua Deng Zhixiang Zhang Jianyong Hohai Univ Changzhou Jiangsu Peoples R China Hohai Univ Coll IOT Engn Changzhou Jiangsu Peoples R China Hohai Univ Dept Mathes & Phys Engn Changzhou Jiangsu Peoples R China

ISBN: (纸本)9781479950324

A simplified equivalent model of microgrid, based on the RBF Artificial Neural Network, is present in this paper. The proposed model is suitable for the dynamic studies of microgrids. Nonlinear mapping of RBF neural network describes the dynamic characteristics of the Point of Common Couple(PCC) of micro-grid under the connected mode. The development model is evaluated using the voltage, current and power of the PCC as the input and output of the RBF neural network in the train process. The PSO algorithm is used for the parameter optimization of RBF and improved the generalization of the dynamic model. The simulation results show the proposed modeling method in this paper is suitable and effective, and the RBF neural network based dynamic model can describe the dynamic characteristics of micro-grid accurately.

关键词： RBF neural network Equivalent Modeling particle swarm optimization algorithm Micro-grid

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Structures and stabilities of UPbn (n ≤ 18) clusters: A first-principles global optimization calculation

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MATERIALS TODAY COMMUNICATIONS 2023年 36卷

作者： Gao, Chun Guo, Guina Hu, Shuxian Liu, Haitao Zou, Wenli Zhang, Ping Yan, Jun Inst Appl Phys & Computat Math Lab Computat Phys Beijing 100088 Peoples R China Northwest Univ Inst Modern Phys Xian 710127 Shaanxi Peoples R China Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China Shaanxi Key Lab Theoret Phys Frontiers Xian 710127 Shaanxi Peoples R China Peking Univ Ctr Appl Phys & Technol Beijing 100871 Peoples R China

Endohedrally doped clusters have received much attention because they can act as superatoms and have great potential as the building blocks for cluster-assembled materials. We have carried out a comprehensive study based on the combination of the particle swarming optimization algorithm and the relativistic density functional theory, to obtain geometric structures and stabilities of lead (Pb) clusters doped with one uranium (U) atom. A gradual evolution pattern was observed with the increasing number of Pb atoms, from exohedrally doped structures to quasi-endohedral structures, and finally to endohedrally doped structures. At least 12 and 11 Pb atoms were necessary to encapsulate the U atom completely in the calculation without and with the spin-orbit coupling (SOC) effect respectively. The UPb16 cluster was represented as a highly stable endohedral cage structure with the U atom around its center. In addition, the crucial role of the SOC effect was emphasized. We hope that our findings will provide a fundamental understanding of actinide-doped lead clusters, which may be quite different from their light element analogues.

关键词： Intermetalloid cluster Endohedrally doped cluster particle swarm optimization algorithm Density functional theory Relativistic effect

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Intelligent optimization algorithm-based electricity pricing strategy for smart building clusters

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Wang, Hui Liao, Xu Ji, Xiu Gong, Shanggao Meng, Xiangping Wang, Jiarui Changchun Inst Technol Sch Elect Engn & Informat Technol Changchun Peoples R China Natl & Local Joint Engn Res Ctr Smart Distribut Ne Changchun Peoples R China State Grid JILIN Elect Power Res Inst Changchun Peoples R China

With the continuous infusion of renewable energy sources, smart buildings have evolved from single-load characteristics into dual characteristics with both electric energy production and consumption capability. Concurrently, the peak and off-peak periods of electricity consumption are influenced by climatic factors, which leads to complexity and deviation from the time-of-use tariffs set by electricity markets, which consequently result in a loss of revenue from grid-based electricity sales. Thus, adopting an innovative pricing mechanism to offset the revenue deficit in the grid assumes paramount significance. Built upon a dual-layer framework that employs intelligent optimization algorithms, this study proposes a pricing strategy for introducing the retail electricity provider into smart building clusters with peer-to-peer power sharing as the core. First, the independent operation model of intelligent buildings and electric energy sharing model without the participation of retail power suppliers are respectively established. Subsequently, with the aim to minimize alliance costs, a novel energy sharing pricing model involving retail electricity suppliers is developed, and a combination of particle swarm optimization and alternating direction multiplier methods is used for distributed solutions within a representative model. This approach yields optimal energy sharing transaction volumes and pricing while ensuring the confidentiality of each participating entity. Lastly, from the perspectives of the power grid, retail electricity suppliers, and multi-building smart alliances, this study conducts simulation analyses of key parameters that influence the bargaining effectiveness of retail electricity suppliers. These parameters encompass the upper limit of pricing, market supervision coefficient, and discount coefficient associated with the grid-based electricity sales to suppliers. Through these analyses, the study further validates the efficacy of the proposed strate

关键词： smart buildings peer-to-peer power sharing retail electricity provider market bargaining particle swarm optimization algorithm alternating direction multiplier method

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