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检索条件"主题词=Improved Particle Swarm Optimization Algorithm"

共 95 条记录，以下是61-70 订阅

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Comprehensive planning method of new energy considering multi-type and different grid-connected modes 3

Comprehensive planning method of new energy considering mult...

引用

3rd IEEE International Electrical and Energy Conference (CIEEC)

作者： Qi, Xiaoguang Liu, Rui Zhao, Haidong An, Jiakun Xi, Peng Shao, Hua Wang, Ying Zhang, Zhang Zhang, Qianmao State Grid Hebei Elect Power Co Ltd Inst Econ & Technol Shijiazhuang Hebei Peoples R China State Grid Baoding Power Supply Co Baoding Hebei Peoples R China

ISBN: (纸本)9781728116754

Based on the statistical analysis of the timing characteristics of new energy sources, such as wind power and photovoltaics, the grid-connected positions and installation scale of new energy is optimized synthetically by considering the power types (wind power or photovoltaics) and grid-connected mode (concentration or distribution). The multi-objective optimization model is constructed by considering promoting new energy consumption, minimizing network loss and decreasing purchasing cost of the grid company. The separating process of discrete variables and continuous variables in the proposed optimization model is carried out and the improved particle swarm optimization algorithm is used to solve model. Taking an area in Hebei Province as an example, the proposed model is simulated. The results show the validity of the model.

关键词： new energy planning multi-objective optimization improved particle swarm optimization algorithm

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Fault Diagnosis of Power Transformer Based on improved Neural Network 5

Fault Diagnosis of Power Transformer Based on Improved Neura...

引用

5th International Conference on Computer Engineering and Application (ICCEA)

作者： Ma, Hailong Song, Huaning Meng, Chengjv Wang, Renli Hechi Univ Sch Artificial Intelligence & Mfg Hechi Peoples R China

ISBN: (纸本)9798350386783;9798350386776

To detect the latent fault of a power transformer before it is too late and improve the accuracy of fault diagnosis, this paper proposes a fault diagnosis method for power transformers based on a BP neural network with improved particle swarm optimization (IPSO). First of all, to solve the problem that particle swarm optimization (PSO) is prone to premature and oscillatory phenomena, an improved particle swarm optimization is proposed by randomly varying the inertia weight of PSO and nonlinearly changing the learning factor of PSO so that it can jump out of the local optimum. Then, IPSO is used to globally optimize the initial weights and thresholds of the BP neural network, and the optimized BP neural network is applied to power transformer fault diagnosis. Finally, by training and testing the transformer fault sample data of substations and power plants and comparing it with BPNN and PSO-BPNN models, further verification is carried out by using the data of six groups of power grid companies that have clearly defined the cause of transformer failure. The results show that the IPSO-BPNN model performs better than BPNN and PSO-BPNN in power transformer fault diagnosis. The method presented in this paper has important reference significance for intelligent fault diagnosis of power transformers and other high-voltage electrical equipment.

关键词： Power transformer improved particle swarm optimization algorithm Neural network Fault diagnosis model

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Course Keeping Control of Underactuated Surface Vessels Based on improved PSO algorithm

Course Keeping Control of Underactuated Surface Vessels Base...

引用

Chinese Intelligent Automation Conference (CIAC)

作者： Li, Guangyu Guo, Chen Li, Yanxin Dalian Maritime Univ Nav Coll 1 Lingshui Rd Dalian Peoples R China Dalian Jiaotong Univ Software Inst 216 XingFa Rd Dalian Peoples R China

ISBN: (纸本)9783642385247;9783642385230

Based on the relationship between parameter speed nu(max) and inertia factor omega in the particle swarm optimization (PSO) algorithm, the improved PSO algorithm that is time-varying nonlinear trigonometric function to control PSO algorithm parameters is proposed to prevent the particles fall into local optimum, and the improved PSO algorithm is used to optimize parameters of PID controller which is used in course control for underactuated surface vessels (USV). Two different PSO algorithms for optimizing parameters of PID controller are compared in this paper, and the results of simulation experiments show that, according to dynamic characteristics changes of USV, the controller which used improved PSO algorithm can optimize the adaptive parameters well and automatically, and has a high tracking speed, small overshoot and strong immunity etc.

关键词： Underactuated surface vessels improved particle swarm optimization algorithm PID controller Course keeping control Automatic rudder

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Multi-objective trajectory optimization algorithm for cooperative six-axis manipulator 7

Multi-objective trajectory optimization algorithm for cooper...

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7th International Conference on Mechatronics, Robotics and Automation

作者： Liu, Shujian Sun, Wei Liu, Yunfei Liu, Jinglong Fan, Jiyong Liu, Huarui Luo, Zhilong Potevio Logist Technol Co Ltd Potevio Shanghai R&D Ctr Shanghai Peoples R China

ISBN: (纸本)9798350352481;9798350352474

The integration of cooperative six-axis manipulator in industrial automation requires it to achieve efficient and accurate motion under the premise of ensuring safety. In order to meet this demand, this paper proposes a multi-objective trajectory optimization algorithm for the cooperative six-axis manipulator, which takes into account the motion time, energy consumption and path smoothness. By establishing a multi-objective optimization model, the improved particle swarm optimization algorithm is used to solve the problem, which ensures that the optimal planning of the trajectory is realized under the premise of satisfying the kinematics and dynamics constraints of the manipulator. An adaptive weight adjustment mechanism is introduced in the algorithm to dynamically balance the conflicts between different optimization objectives. The simulation results show that the proposed algorithm can significantly reduce the time for the manipulator to complete the task, reduce the energy consumption and improve the smoothness of the motion path. This study provides strong technical support for the efficient, energy-saving and safe operation of cooperative manipulators in complex industrial environments.

关键词： Cooperative six-axis manipulator multi-objective optimization improved particle swarm optimization algorithm trajectory planning

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Co-simulation and Parameters Identification algorithm Research of Servo System Based on Dynamic Module 4

Co-simulation and Parameters Identification Algorithm Resear...

引用

The Fourth International Conference On Control, Automation and Information Sciences

作者： Yang, Weiwei Shan, Liang Qu, Yuanxin Yang, Binbin Sun, Jiafeng Nanjing Univ Sci & Technol Sch Automat Nanjing Jiangsu Peoples R China China Satellite Maritime Tracking & Control Dept Wuxi Peoples R China Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479998920

Simulations of traditional servo systems are mostly based on the Matlab software, and a number of nonlinear characteristics of actual controlled object are simplified. Meanwhile, co-simulation based on dynamic model of moving platform is more effective when simulating servo system. In Matlab/Simulink, this paper designs and models PMSM block, coordinate transformation block from dq to abc, three phase current source inverter controller blocks and three PID controller blocks, including position block, speed block and current block. Combined with vehicle dynamic model which is established by multi-body dynamic software, RecurDyn, three-closed-loop simulation model is introduced. Additionally, in order to identify the parameters of the PID controllers, an improved particle swarm optimization algorithm method is proposed, which combines the genetic algorithm and the inertia factor. Simulation results verify the effectiveness and feasibility of the proposed control method, which can be used for designing other AC synchronous servo systems.

关键词： servo system co-simulation RecurDyn improved particle swarm optimization algorithm parameters identification

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Coordinative Allocation Model and Method of Equipment Maintenance Materiel 8

Coordinative Allocation Model and Method of Equipment Mainte...

引用

8th International Conference on Advanced Computational Intelligence (ICACI)

作者： Wang, Tiening Yu, Shuangshuang Li, Ning Xu, Shengliang Yun, Chao Wang, Qinqin Acad Armored Force Engn Dept Tech Support Beijing Peoples R China PLA Unit 78416 Chongqing Peoples R China Mil Representat Off 618 Factory Beijing Peoples R China Ctr Management Beijing Mil Commun Network Technol Beijing Peoples R China PLA Unit 72726 Jinan Peoples R China

ISBN: (纸本)9781467377829

As to solve the shortcomings such as waste of resources, high cost of support, low support efficiency as a result of the over-stocked materiel and under-stocked materiel existing in equipment maintenance materiel storage support, the coordinative allocation was put forward to alleviate the problem of imbalanced resource storage, in which the over-stocked materiel was well used to satisfy the units lack of materiel. Based on three targets such as support cost, support time and materiel utilization ratio, the multi-objective optimization decision-making model of equipment maintenance materiel coordinative allocation was established, and simplified to a single-objective model adopting constraint method;the improved particle swarm optimization algorithm was designed based on guiding factor to solve the model, and verified through simulation experiment. The result shows that through coordinative allocation the storage resources in the support system can be balanced effectively and the support efficiency of equipment maintenance materiel is enhanced as well.

关键词： equipment maintenance materiel coordinative allocation decision-making multi-objective optimization model improved particle swarm optimization algorithm

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Optimal Design of Loss of Permanent Magnet Synchronous Motor Based on particle swarm optimization 1

Optimal Design of Loss of Permanent Magnet Synchronous Motor...

引用

1st IEEE Student Conference on Electric Machines and Systems (IEEE SCEMS)

作者： Jin, Yong Xing Wang, Ai Yuan Wang, Tao Sun, Jian Wang, Ming Xing Shanghai Dian Ji Univ Inst Elect Shanghai Peoples R China

ISBN: (纸本)9781538673485

the improved particle swarm optimization algorithm is introduced to optimize the multi-objective of permanent magnet synchronous motor, reduce loss and improve efficiency. In order to reduce the loss of the permanent magnet synchronous motor, the four main parameters that affect the loss (magnetic pole thickness, air gap length, slot width, groove depth,) are optimized. By using orthogonal experiment to select a certain number of samples, using support vector machine (SVM) to fitting the data to get the regression equation, using particle swarm optimization algorithm to optimize the regression equation, get the optimal solution, Maxwell model in ANSYS simulation analysis into the motor. The simulation results show that the loss reduction and efficiency of PMSM are improved by 2.64% compared with that before optimization.

关键词： permanent magnet synchronous motor loss support vector machine improved particle swarm optimization algorithm

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Improving PSO-SVM for Fatigue Recognition 8th

Improving PSO-SVM for Fatigue Recognition

引用

8th International Symposium on Artificial Intelligence and Robotics (ISAIR)

作者： Chai, Pan Wang, Mei Chen, Xing Yang, Yangliu Xian Univ Sci & Technol Xian 715100 Peoples R China

ISBN: (纸本)9789819991082;9789819991099

Excessive fatigue can cause harm in an individual's life. Therefore, the challenge is how to effectively detect fatigue. To this end, this paper uses an EEG multi-feature fusion method based on Linear Discriminant Analysis (LDA) dimensionality reduction, and uses the dimensionality reduction features as the input of Support Vector Machine (SVM) for classification. In order to improve the classification accuracy of SVM, an improved particle swarm optimization (PSO) algorithm was proposed to optimize the SVM. The improved PSO-SVM algorithm is combined with other classification methods to classify the EEG signals of the subjects in the fatigued and awake states. The experimental results show that the improved PSO-SVM algorithm has achieved the best classification performance, the average accuracy rate reached 84.56%.

关键词： EEG signal Feature extraction improved particle swarm optimization algorithm Support Vector Machine

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Multi-time Scale Unit Commitment optimization under Hybrid Uncertainties 10

Multi-time Scale Unit Commitment Optimization under Hybrid U...

引用

10th International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Zhou, Min Wang, Bo Watada, Junzo Nanjing Univ Sch Management & Engn Nanjing Jiangsu Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781538693896

Recent years, the popularity of wind power and the widely use of diversified loads have increased the uncertainty of power systems in both supply and demand sides. This paper develops a multi-time scale unit commitment optimization model under wind power and future load uncertainties. First, day-ahead wind power and electric load forecast is obtained by long short-term memory network, based on which the on/off status and first-period output of units are determined. Then rolling economic dispatch is applied when real time data is collected from the system. To solve the above unit commitment and economic dispatch model, an improved particle swarm optimization algorithm is proposed. Finally, several experiment were performed to demonstrate the effectiveness of this research.

关键词： Multi-time scale unit commitment long and short-term memory network rolling economic dispatch improved particle swarm optimization algorithm

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optimization Control of Ice Storage Air-conditioning System

Optimization Control of Ice Storage Air-conditioning System

引用

3rd International Conference on Advances in Materials Manufacturing (ICAMMP 2012)

作者： Wu, Ting Wu, Gang Bao, Zhejing Yan, Wenjun Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9783037856482

Ice storage air-conditioning system can bring benefits to power supplier and consumers for its advantage of shifting power consumption at peak hours during day to the off-peak hours at night. In this paper, we adopted an improved particle swarm optimization algorithm to develop an optimal control strategy for ice storage air-conditioning system with the aim of minimizing operation cost subject to various operational constrains. Comparing with the usual chiller-priority and ice-storage-priority control strategy, the proposed control scheme can not only meet the building cooling load but also achieve the minimum operation cost.

关键词： Ice Storage Air-conditioning System improved particle swarm optimization algorithm Chiller-priority Ice-storage-priority

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