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检索条件"主题词=Particle Swarm optimization algorithm"

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EV Charging Scheduling for Flattening Load Profile Based on Improved particle swarm 36

EV Charging Scheduling for Flattening Load Profile Based on ...

引用

36th Chinese Control and Decision Conference (CCDC)

作者： Zhang, Zhipeng Li, Xiaohui Mao, Shanli Cai, Bin He, Jie Zhu, Jialu Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China Wuhan Netizen Technol Co Ltd Res Ctr Wuhan Peoples R China Shenglong Elect Grp Co Ltd Res Ctr Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The load profile can reflect the change of the grid load over time. By connecting electric vehicles to the grid for charging and discharging, the goal of peak shaving and valley filling is realized, thus flattening the load profile and improving the stability of grid operation. Aiming at this characteristic, a method for optimal scheduling of electric vehicle charging based on an improved particle swarm algorithm is proposed in this paper. First, the proposed method constructs the electric vehicle charging scenario by Monte Carlo. Then, the improved particle swarm algorithm is used to solve the optimal charging and discharging quantity of electric vehicles in each period to minimize the load variance. By comparing the improved particle swarm algorithm with other intelligent algorithms on the benchmark function test set, the improved particle swarm algorithm is verified that it has superiority in convergence ability and optimization accuracy. Simulation results show that the scheduling of electric vehicle charging using the improved particle swarm algorithm reduces the peak valley difference of the power grid by 6.31% and the load variance by 74432kW(2) compared with the classical particle swarm algorithm.

关键词： load profile electric vehicles Monte Carlo particle swarm optimization algorithm load variance

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Multi-objective reactive power optimization in power system based on improved PSO-OSA algorithm 14

Multi-objective reactive power optimization in power system ...

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14th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Song Bo Cao Yujie Zhang Xiang Wang Yelin Li Ning Xian Univ Technol Inst Water Resources & Hydroelect Engn Xian Shaanxi Peoples R China Xian Univ Technol Sch Automat & Informat Engn Xian Shaanxi Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Hubei Peoples R China Xian Kenuo Network & Informat Secur Technol Inst Xian Shaanxi Peoples R China

ISBN: (纸本)9781538694909

With the rapid development of Chinese economy, the power industry is playing a more and more important role in the development of Chinese economy. The optimal reactive power in the power system plays an important role in the voltage stability of the power system and reducing the loss of active power. The calculation results of particle swarm optimization algorithm, oriented search algorithm and PSO - OSA hybrid algorithm in reactive power optimization of power system is compared, and the performance of the proposed method has been tested on the IEEE 30-bus systems in this paper. The simulation results show that the proposed method is able to produce well distributed Pareto optimal solutions for the optimal reactive power problem.

关键词： Optimal reactive power Multi-objective probelem particle swarm optimization algorithm Oriented search algorithm

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A novel bio-inspired algorithm based on plant root growth model for data clustering 35

A novel bio-inspired algorithm based on plant root growth mo...

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第35届中国控制会议

作者： QI Xiangbo ZHU Yunlong ZHANG Hao ZHANG Dingyi WU Junwei Sheny ang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9781509009107

Inspired by the behaviors of plant root growth,an Artificial Root Mass(ARM) optimization algorithm based on an artificial root model is *** ARM algorithm simulates the plant's root growth strategies including proliferation and'intelligent' decisions about growth *** well-known benchmark functions are employed to validate its optimization *** is compared with other existingalgorithms,includinggenetic algorithm(GA),particle swarm optimization(PSO) and differential evolution(DE).The experimental results show that ARM seems much superior to other algorithms on the selected benchmark functions in multidimensional *** algorithm is used for data clustering on several benchmark *** performance of the ARM algorithm is compared with GA,PSO and DE on clustering *** simulation results show that the proposed ARM outperforms the otherthree algorithms in terms of accuracy and robustness onmost of selected *** proposed algorithm ARM provides a new reference for solving data clustering problems.

关键词： Artificial root mass optimization algorithm genetic algorithm particle swarm optimization algorithm

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Research on web service composition selection based on QoS metrics 15

Research on web service composition selection based on QoS m...

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15th International Conference on Advanced Computational Intelligence (ICACI)

作者： Liao, Libin Wang, Siwei Wu, Jianbin Guangzhou Inst Sci & Technol Guangzhou 510000 Peoples R China Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Zhejiang Peoples R China

ISBN: (纸本)9798350321456

Aiming at the hot and difficult issues in Web services and service composition, this paper analyzes and studies the selection of Web services and service composition from the functional and non functional attributes of Web services, and mainly makes the following work and innovation. First of all, the functional attributes of Web services are studied, Web services on various service provision platforms are collected, and Web services are classified. Secondly, aiming at the shortcomings of existing Web service QoS attribute evaluation models, an improved method is proposed, which introduces a comprehensive evaluation mechanism of variable weight vector. Based on the constant weight comprehensive evaluation method, the state variable weight vector is established to dynamically adjust the attribute weights of various service QoS indicators, so as to improve the accuracy and objectivity of the Web service QoS attribute evaluation. Finally, in view of the defects and deficiencies of traditional algorithms in the selection of Web service composition, the particle swarm optimization algorithm with linearly decreasing inertia weight and learning factor is introduced, which better balances the self cognition and social learning ability of particles, and improves the search speed and global search ability of particles. And a large number of experiments are carried out to compare it with the traditional algorithm, which verifies the effectiveness and superiority of the improved particle swarm optimization algorithm.

关键词： Web service Service quality Service portfolio Qos attribute weight particle swarm optimization algorithm

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Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on particle swarm Neural Network

Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis...

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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Han, Baoru Li, Jingbing Wu, Hengyu Hainan Software Profess Inst Dept Elect Engn Qionghai Peoples R China Hainan Univ Coll Informat Sci & Technol Haikou Hainan Peoples R China

ISBN: (纸本)9783037857243

This paper presents a tolerance analog circuit hard fault and soft fault diagnosis method based on the BP neural network and particle swarm optimization algorithm. First, select the mean square error function of BP neural network as the fitness function of the PSO algorithm. Second, change the guidance of neural network algorithms rely on gradient information to adjust the network weights and threshold methods, through the use of the characteristics of the particle swarm algorithm group's parallel search to find more appropriate network weights and threshold. Then using the adaptive learning rate and momentum BP algorithm to train the BP neural network. Finally, the network is applied to fault diagnosis of analog circuit, can quickly and effectively to the circuit fault diagnosis.

关键词： Fault diagnosis Tolerance analog circuits particle swarm optimization algorithm BP neural network

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Research on RFID Virtual Tag Location algorithm Based on Monte Carlo 13

Research on RFID Virtual Tag Location Algorithm Based on Mon...

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IEEE 13th International Conference on Computer Research and Development (ICCRD)

作者： Guan, Tingliang Wang, Ding Su, Yixin CNNP Nucl Power Operat Management Co Ltd Dept Purchasing Management Jaxing Peoples R China Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781665422604

Aiming at the problems of large positioning error and low positioning effect at the boundary of traditional RFID virtual tag positioning algorithm, this paper compares the influence of four different interpolation methods of virtual reference tag signal strength on positioning accuracy, and proposes a RFID virtual tag positioning algorithm based on Monte Carlo. The algorithm uses dynamic particles to replace the traditional static reference tag;introduces particle swarm optimization algorithm to update the Monte Carlo sample particle swarm, and gives different weights to the sampling particles based on the signal strength difference between the sampling particles and the undetermined tags, and finally completes the localization of the unknown tags through Monte Carlo resampling. The simulation results show that the algorithm can effectively improve the accuracy and stability of RFID positioning system compared with the traditional virtual tag positioning algorithm.

关键词： RFID VIRE algorithm Monte Carlo particle swarm optimization algorithm

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Handwritten Numeral Recognition with a Quantum Neural Network Model 2

Handwritten Numeral Recognition with a Quantum Neural Networ...

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2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Mao Yaxuan Kazuyuki Aihara Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo

ISBN: (纸本)9781509064151;9781509064144

Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks(QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. Backpropagation(BP) algorithm and particle swarm optimization(PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.

关键词： Handwritten numeral recognition Quantum neural network Back-propagation algorithm particle swarm optimization algorithm

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An Improved PSO-BP Neural Network and Its Application to Earthquake Prediction 28

An Improved PSO-BP Neural Network and Its Application to Ear...

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28th Chinese Control and Decision Conference

作者： Li, Cao Liu, Xiaoyu Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China

ISBN: (纸本)9781467397148

This paper presents a way of combining BP (Back Propagation) neural network and an improved PSO (particle swarm optimization) algorithm to predict the earthquake magnitude. It is known that the BP neural network and the normal PSO-BP neural network have some defeats, such as the slow convergence rate, easily falling into local minimum values. For improving the properties of PSO, some proposed the linear decreasing inertia weight strategy. Furthermore, this paper uses a nonlinear decreasing inertia weight in PSO to get a faster training speed and better optimal solutions. Compared with the linear decreasing strategy, the inertia weight in our nonlinear method has a faster declining speed in the early iteration, which can enhance the searching precision. In the late iteration, the inertia weight has a slower declining speed to avoid trapping in local minimum value. Then we apply the improved PSO to optimize the parameters of BP neural network. In the end, the improved PSO-BP neural network is applied to earthquake prediction. The simulation results show that the proposed improved PSO-BP neural network has faster convergence rate and better predictive effect than the BP neural network and the normal PSO-BP neural network.

关键词： BP neural network particle swarm optimization algorithm Inertia Weight Earthquake Prediction

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Photovoltaic Array Power Prediction Model Based on EEMD and PSO-KELM 47

Photovoltaic Array Power Prediction Model Based on EEMD and ...

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47th IEEE Photovoltaic Specialists Conference (PVSC)

作者： Ding, Kun Chen, Xiang Zhang, Jingwei Li Feng Hamelmann, Frank U. Weng, Shuai Hohai Univ Coll Mech & Elect Engn Changzhou Peoples R China Univ Appl Sci Bielefeld Solar Comp Lab Minden Germany Changzhou Key Lab Photovolta Syst Integrat & Prod Changzhou Peoples R China

ISBN: (纸本)9781728161150

In order to improve the accuracy of short-term photovoltaic (PV) power prediction, the PV array power prediction model based on ensemble empirical mode decomposition (EEMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization algorithm (PSO-KELM) is proposed in this paper. The EEMD algorithm is used to decompose the power sequence of the PV array into a series of intrinsic mode functions (IMFs) with different features, the IMFs with similar features are divided into new components by sample entropy (SE), and establishing the KELM power prediction model corresponding to the new component. The PSO is used to optimize the output weight of KELM, seeking the optimal solution of upper and lower limits, and improving the performance of KELM. The actual data is used to compare the proposed prediction model with the traditional back-propagation (BP) and extreme learning machine (ELM) prediction models. The results validate that the proposed model can predict power of PV array with good accuracy and calculation speed.

关键词： short-term PV power prediction ensemble empirical mode decomposition sample entropy particle swarm optimization algorithm kernel extreme learning machine

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ALLOCATION OF RES AND DSTATCOM FOR THE OPTIMAL DISTRIBUTION SYSTEM PLANNING 22

ALLOCATION OF RES AND DSTATCOM FOR THE OPTIMAL DISTRIBUTION ...

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22nd National Power Systems Conference (NPSC)

作者： Shukla, Madhu Jain, Prerna MNIT Jaipur Dept Elect Engn Jaipur India

ISBN: (纸本)9781665462020

The increasing demand of the load causes the voltage imbalance and increase in power loss. This is forcing the distribution companies to take the proper actions in the planning of distribution system. The allocation of Renewable Energy Source (RES) and Distribution Static Compensator (DSTATCOM) are considered in the distribution system planning to reduce the losses and maintain the voltage of the system within the required limit with the growing load demand. Due to the environmental concerns, the RES is becoming the must to consider as a part of the power system whereas the use of DSTATCOM is for the reactive power compensation to improve the power quality and the voltage profile. For the simultaneous allocation of RES and DSTATCOM in balanced Radial Distribution System (RDS), Loss Sensitivity Factor (LSF) used to get the candidate buses and particle swarm optimization (PSO) algorithm is used for optimal sizes. For the calculation of load flow of the system an efficient Backward-Forward Sweep Load Flow algorithm is used. The objective of the proposed methodology is to minimize the total cost which is the sum of total investment cost of RES and DSTATCOM, power purchase cost and pollution emission cost, keeping the voltage of each bus of the system within the limit. From the result obtained for the 38-bus balanced radial distribution system (RDS), it is clearly observed that the total active and reactive power losses are reduced and voltages of the system is improved with the optimal allocation of RES and DSTATCOM at minimum cost.

关键词： Renewable Energy Source Distribution Static Compensator Loss Sensitivity Factor particle swarm optimization algorithm Balanced RDS

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