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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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A New particle swarm optimization algorithm to Hierarchy Multi-objective optimization Problems and Its Application in Optimal Operation of Hydropower Stations

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JOURNAL OF COMPUTERS 2012年第8期7卷 2039-2046页

作者： Yang, Junjie Zhanjiang Normal Univ Sch Informat & Technol Zhanjiang Peoples R China

Some engineering optimization problems, such as multi-objective daily generation scheduling for the hydropower stations, the objective functions has obvious hierarchy and priority, simultaneously, the constraints is very complex. It is difficult to solve the operation problems by adopting traditional optimal technique. In this paper, a new particle swarm optimization algorithm solving hierarchy multi-objective timization problems is proposed. The algorithm can handle the level multi-objective optimization problem effectively. By adopting the adaptive inertia weight algorithm (AWA) and mutative scale local search algorithm (MSLSA), the convergence performance of the algorithm is improved. Then, A multi-objective daily generation scheduling model for the hydropower stations is established, in which two objective functions including maximization of peak-energy capacity benefits and maximization of power generation are involved. Finally, Multi-objective daily generation scheduling problem of the Three Gorges cascade hydropower system during low-flow period is studied with proposed algorithm to obtain the maximum peak-energy capacity benefits, as well as power generation benefits of three gorges cascade stations.

关键词： multi-objective optimization particle swarm optimization algorithm daily generation scheduling

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Research on error analysis and calibration method of 3-PUU parallel robot

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2024年

作者： Chen, Mingfang Liang, Hongjian He, Chaoyin Zhang, Yongxia Huang, Liang'en Kunming Univ Sci & Technol Fac Mech & Elect Engn 727 Jingming South RdWujiaying St Kunming 650500 Yunnan Peoples R China

Kinematic calibration is necessary for the pose accuracy improvement of the parallel robots. However, difficulties occur in traditional calibration methods, such as exceeding a large number of error parameters and error accumulation. In this paper, a 3-PUU parallel robot is taken as the research object, and the closed-loop vector method and differential theory are employed to establish an error model. Through quantitative analysis of geometric parametric errors, redundant parameters within the calibration algorithm are meticulously eliminated. By treating the dominant error terms as optimization variables for the mechanism's parameters, the calibration problem is transformed into a nonlinear system optimization challenge, which effectively avoids the problem of multiple error parameters and accumulation of errors in the traditional method. Further, using the particle swarm algorithm to compute the minimum value of the objective function, one can obtain the actual structural parametric errors of the robot. These errors are then utilized to correct the kinematic model, enabling the calibration of the mechanism's parameters and ultimately enhancing operation accuracy. Lastly, simulation and experimental validation of the algorithm are carried out. The simulation results indicate that the end-effector position error converges to zero infinitely;and the experimental results indicate that the maximum error of the end-effector in the x, y, z directions and the maximum position error are reduced from 8.53, 11.67, 3.29, and 12.56 mm to 1.09, 1.32, 0.98, and 1.75 mm, respectively. The standard deviation of the position error is reduced from 2.43 to 0.32 mm. The mean error is reduced from 7.76 to 1.02 mm. To sum up, the operation accuracy and stability of the robot are greatly improved.

关键词： Parallel robot error model kinematic calibration particle swarm optimization algorithm pose accuracy

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Application of particle swarm optimization in parameter identification of power electronic circuits 5

Application of particle swarm optimization in parameter iden...

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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Lai, Guosheng Jiangxi Univ Engn Xinyu 338029 Peoples R China

ISBN: (纸本)9781665423144

Power electronic circuit parameter identification is the main means to judge the state of the circuit. Through identification, we can know whether there is a fault in the circuit, so the parameter identification of power electronic circuit is very important. However, there are some defects in the previous identification work, which can only work on part of the device, but it is difficult to identify the other part of the device, so the characteristic parameters of this kind of device can not be obtained, which shows that the previous power electronic circuit parameter identification methods need to be optimized. In view of this, the relevant fields put forward the optimization scheme of particle swarm optimization algorithm. It is believed that the algorithm can make up for the defects of previous power electronic circuit parameter identification. This paper will also study this problem, elaborate the specific application mode of the algorithm, and carry out simulation and physical experiments on the application results to verify the effectiveness of the algorithm.

关键词： particle swarm optimization algorithm Power Electronic Circuit Parameter Identification Parameter Identification Pptimization

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Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval optimization

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ENERGIES 2024年第15期17卷 3623页

作者： Su, Lei Wu, Wenxiang Feng, Wanli Qin, Junda Ao, Yuqi State Grid Hubei Elect Power Res Inst Wuhan 430074 Peoples R China State Grid Smart Grid Res Inst Co Ltd State Key Lab Adv Power Transmiss Technol Beijing 102209 Peoples R China

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method.

关键词： data center uncertainty interval optimization collaborative planning particle swarm optimization algorithm gravitational search algorithm

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optimization of External Characteristic Parameters of Multiple Motors for Electric Vehicles Based on Optimal Torque Distribution Coefficient 25

Optimization of External Characteristic Parameters of Multip...

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25th International Conference on Electrical Machines and Systems (ICEMS)

作者： Su, Zhaorui Du, Jinhua Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Peoples R China

ISBN: (纸本)9781665493024

In this paper, an optimization method based on optimal torque distribution coefficient is proposed for the optimization of external characteristic parameters of electric vehicles driven by multiple motors. Firstly, according to the performance requirements of a dual-motor driven electric vehicle, the original motor was matched. Then the law of motor efficiency Map changing with external parameters is described. An optimization algorithm is used to optimize the torque distribution coefficient of the electric vehicle in all working conditions. particle swarm optimization algorithm is used to optimize the external characteristic parameters of the two motors. Finally, a vehicle Simulink simulation platform is built to verify the correctness of the optimization results and the effectiveness of the optimization ideas.

关键词： motor efficiency Map optimal torque distribution coefficient particle swarm optimization algorithm Simulink

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Constant Force Control Method of Grinding Device 15th

Constant Force Control Method of Grinding Device

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15th International Conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

作者： Duo, Jia Wen Feng, Jiang Zi Yu, Dai Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9783031138355;9783031138348

At present, the traditional robot grinding has some shortcomings in output constant force control. As a result, the output force on the grinding object is frequently instable. Improper force can damage the object during grinding and lead huge economic loss. Therefore, how to improve the accuracy of the output force of robot grinding, has become an urgent problem to be solved. In this paper, aim to improve the grinding force control accuracy, a new control framework which is suitable for cylinder driven grinding device is proposed. The control framework is applied to control the cylinder output force of the grinding device, thereby improving the control ability of the high-precision grinding process robot. In the framework, a PID controller with nonlinear differential gain parameters is used, and parameters are optimized by using the particle swarm optimization algorithm (PSO). The proposed control method, based on the model of the actual cylinder driven grinding device, is verified in MATLAB. The results show that it controls the actual force of the grinding object near the ideal force accurately. The overshoot of the output force on the grinding object is zero and the system stability is very good.

关键词： Grinding device Nonlinear differential gain PID controller particle swarm optimization algorithm

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Short-Term Subway Inbound Passenger Flow Prediction Based on AFC Data and PSO-LSTM Optimized Model

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URBAN RAIL TRANSIT 2022年第1期8卷 56-66页

作者： Liu, Jiaxin Jiang, Rui Zhu, Dan Zhao, Jiandong Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Minist Transport Key Lab Transport Ind Big Data Applicat Technol C Beijing 100044 Peoples R China

Making accurate predictions of subway passenger flow is conducive to optimizing operation plans. This study aims to analyze the regularity of subway passenger flow and combine the modeling skills of deep learning with transportation knowledge to predict the short-term subway passenger flow in the scenarios of workdays and holidays. The processed data were collected from two months of Automated Fare Collection (AFC) data from Xizhimen station of Beijing metro. The data were first cleaned by the established cleansing rules to delete malformed and abnormal logic data. The cleaned data were used to analyze the spatial characteristics in passenger flow. Second, a short-term subway passenger flow prediction model was built on the basis of long short-term memory (LSTM). Determining that the error will be relatively high in peak hours, we proposed gradual optimizations from data input by dividing one whole day into different time periods, and then used particle swarm optimization (PSO) to search for the optimal hyperparameters setting. Finally, inbound passenger flow of Beijing Xizhimen subway station in 2018 was selected for numerical experiments. Predictions of the LSTM-based model had higher accuracy than the traditional machine learning support vector regression (SVR) model, with mean absolute percentage error (MAPE) of 21.97% and 4.80% in the scenarios of workdays and holidays, respectively, which are both lower than those of the SVR model. The optimized PSO-LSTM model has been verified for its effectiveness and accurateness by the AFC data.

关键词： Short-term subway passenger flow prediction Deep learning Long short-term memory Automated Fare Collection (AFC) data particle swarm optimization algorithm

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An effective analysis of online education model based on artificial intelligence

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期40卷 3535-3546页

作者： Peng, Lihua Langfang Normal Univ Langfang 065000 Hebei Peoples R China

With the development of artificial intelligence in education, online education has been recognized by the society as a new teaching method. It can make full use of the advantages of the network across regions, and make full use of the advantages of network technology to share the resources of colleges and universities, which is a promising educational method. In response to the demand of online education for learner information, this paper proposes the learner model Neighbor Mean Variation Multi-Objective particle swarm optimization-Genetic algorithm (NMVMOPSO-GA). This model includes the learner's learning interest sub-model, the learner's cognitive ability sub-model and the learner's knowledge sub-model. The modelling techniques of the three sub-models are discussed separately, and their status and role in the online education system are analyzed. At the same time, for the knowledge model that reflects the learner's learning progress and knowledge mastery, a learner knowledge sub-model constructed with Bayesian networks is proposed. The neighbor mean mutation operator is introduced to optimize the multi-objective particle swarm optimization algorithm and improve the convergence performance and stability of the multi-objective particle swarm optimization algorithm. We study the application of multi-objective particle swarm optimization algorithm in online course resource generation service. Through simulation experiments, it is verified that the multi-objective particle swarm optimization algorithm can improve the performance and stability of online course resource generation.

关键词： Online education artificial intelligence intelligent learning particle swarm optimization algorithm

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Abnormal Heart Sound Detection using Time-Frequency Analysis and Machine Learning Techniques

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 90卷

作者： Nia, Parastoo Sadeghi Hesar, Hamed Danandeh Sahand Univ Technol Fac Biomed Engn Tabriz Iran

Phonocardiogram (PCG) signals contains valuable information pertaining to heart valve functionality, rendering them potentially useful for early detection of cardiovascular diseases. Automated classification of heart sounds harbors great promise for identifying cardiac pathologies. This paper introduces a novel automated approach to classify normal and abnormal heart sounds. Our methodology involves partitioning heart sounds into four segments: S1, S2, systolic, and diastolic, followed by extraction of time-frequency and time-statistical features. Prior to data classification, we employ two techniques - particle swarm optimization (PSO) and Sequential Forward Feature Selection (SFFS) - for efficient feature selection. We assess the performance of the proposed method on the Physionet Challenge 2016 database, utilizing the 10-fold cross-validation method. To address the issue of dataset imbalance, we apply the synthetic minority over-sampling technique (SMOTE) to create balanced datasets. Our approach surpasses existing methods in the literature, as evidenced by its superior accuracy, sensitivity, and specificity metrics. Specifically, our method achieves an accuracy of 98.03%, a sensitivity of 97.64%, and a specificity of 98.43% in distinguishing normal from abnormal heart sounds on the Physionet database. These findings outperform the results obtained by previously established methods evaluated on the Physionet 2016 challenge database.

关键词： Phonocardiogram particle swarm optimization algorithm Feature Selection Classification

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