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

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Prediction of gas utilization ratio based on singular spectrum analysis, improved PSO and KELM 36

Prediction of gas utilization ratio based on singular spectr...

引用

36th Chinese Control and Decision Conference (CCDC)

作者： Li, Zhuang An, Jianqi Guo, Yunpeng Zhao, Guoyu Wu, Min China Univ Geosci Sch Automat Wuhan Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan Peoples R China China Univ Geosci Sch Future Technol Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The gas utilization ratio (GUR) in a blast furnace is directly linked to the efficiency, cost-effectiveness and environmental impact on the blast furnace ironmaking process. However, The stability of the published GUR time series prediction models need to be improved. This paper presents an improved particle swarm optimization (PSO) incorporating linearly decreasing inertia weights (LDIW) to optimize the kernel-based extreme learning machine (KELM) for single-step prediction. This paper uses singular spectrum analysis (SSA) to preprocess the data and extract the key components from the GUR time series to solve the problem of high volatility of the GUR time series. In addition, this paper introduces LDIW to improve the optimization ability of particle swarm optimization algorithm, which enhances the stability of a single-step prediction model. Then this paper uses the improved PSO algorithm to extract the optimal parameters of KELM, and establishes a single-step GUR prediction model based on the improved PSO-KELM. Finally, this paper uses the actual production process data of blast furnace to verify the prediction model. The results show that the prediction accuracy of GUR and the overall stability of the model are significantly improved, providing important guidance for the blast furnace ironmaking process.

关键词： Gas utilization rate particle swarm optimization algorithm extreme learning machine singular spectrum analysis prediction model

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Advanced Applications Using Bio-Inspired optimization algorithms for Adaptive Digital Filters Designed with FIR and IIR Lattice-Ladder Structures 52

Advanced Applications Using Bio-Inspired Optimization Algori...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Hussain, M. Jenkins, W. K. Radhakrishnan, C. Penn State Univ Sch Elect & Comp Engn University Pk PA 16802 USA Univ Illinois Dept Elec & Comp Engr Urbana IL 61801 USA

ISBN: (纸本)9781728133201

Recently published results demonstrated that the Levy Flight Firefly algorithm (LFFA) can be effectively applied to IIR adaptive filter structures designed with parallel or cascade second order sections. Recently published results also demonstrated that the LFFA can be effectively used for IIR adaptive filters designed with second order coupled form sections. This paper now proposes that bio-inspired optimization algorithms can be applied to FIR and IIR adaptive lattice-ladder structures, which have been well known throughout the literature for many past decades. Experimental results are presented to demonstrate that the LFFA performs effectively on several different types of adaptive lattice-ladder structures.

关键词： IIR adaptive filters bio-inspired optimization L'evy Flight Firefly algorithm particle swarm optimization algorithm lattice-ladder filters

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Optimal Relay Placement in Microgrids Considering Critical Clearing Time

Optimal Relay Placement in Microgrids Considering Critical C...

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IEEE 7th International Power Engineering and optimization Conference (PEOCO)

作者： Mirazimi, S. J. Salehi, B. Tadayon, M. Karshenas, H. R. Univ Southern Queensland Fac Engn & Surveying Toowoomba Qld 4350 Australia Isfahan Univ Dept Elect & Comp Engn Esfahan Iran Distribut Network Dept Behrad Consulting Engn Esfahan Iran

ISBN: (纸本)9781467350747;9781467350723

This paper is concerned with the optimal placement of protection devices in a microgrid using particle swarm optimization algorithm. One of the main advantages of Distributed Generation (DG) scheme and microgrids in modern distribution systems is the reduction of number of outages and the associated damages caused by them. This task is accomplished by supplying a feeder from multiple sources. In order to prevent generator instability in DGs connected to utility, it is necessary to improve the protective schemes of traditional distribution systems and also to use proper relaying and setting for DGs. All of the downstream overcurrent (OC) relays of each DG are coordinated together and also should be coordinated with OC relay that is installed on the Point of Common Coupling (PCC) which is set at Critical Clearing Time (CCT) as a definite time, to have a desirable performance on each outage. In this paper, by the use of graph theory, various branches of a feeder are identified and the constraints for using particle swarm optimization algorithm to optimize the location of protective equipment are derived. In the proposed algorithm, the location, type and direction of relays are optimized simultaneously.

关键词： Microgrid particle swarm optimization algorithm Protection-Coordination Graph theory

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Short Term Load Forecasting and Early Warning of Charging Station Based on PSO-SVM

Short Term Load Forecasting and Early Warning of Charging St...

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International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Liao Xiaoqun Kang Xiaofan Ma Li Cao Nanlan Xian Univ Sci & Technol Xian 710054 Shaanxi Peoples R China

ISBN: (纸本)9781728113074

In this paper, a short-term load forecasting model and a load early warning model for charging station based on PSO-SVM are proposed. particle swarm optimization (PSO) is used to optimize the parameters of support vector machine (SVM) model, and the PSO-SVM load forecasting model for the optimal nuclear parameters of charging station is established according to the normalized root mean square error (NRMS). On the basis of it, a load warning model of charging station is established and verified by an example. Experiments show that the short-term load forecasting model based on PSO-SVM and the load forecasting model of charging station meet the requirements of forecasting and forecasting accuracy.

关键词： Load Forecasting Optimal Kernel Parameters Support Vector Machine particle swarm optimization algorithm Load Early Warning

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Biomechanical study on the nonlinear stiffness behavior of the calf muscle and ground reaction force estimation during running 5

Biomechanical study on the nonlinear stiffness behavior of t...

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5th RSI International Conference on Robotics and Mechatronics (IcRoM)

作者： Jalali, Peyman Varahram, Sina Hassannejad, Reza Ettefagh, Mir Mohammad Univ Tabriz Dept Mech Engn Tabriz Iran

ISBN: (纸本)9781538657034

Understanding the biomechanical behavior of the human body in different conditions of locomotion can be very advantageous for many purposes, such as developing the humanoid robots. With this approach, the aim of this study is investigating the nonlinear stiffness behavior of the calf muscle and the exerted ground reaction force (GRF) during running. For this purpose, a new active model of human body is proposed, which the distinctive features of this model are as follows: 1) The simulation model is considered to be five degrees of freedom (5-DOF) by using the standard solid model to simulate the calf muscle;2) The optimum values of the considered mechanical parameters are obtained using the particle swarm optimization (PSO) algorithm;3) The stiffness of the calf muscle is considered to be nonlinear and active, which is appropriately formulated with regard to the viscoelasticity nature of the calf muscle. This model is tested for two types of hard and soft shoes, and validated by the results of previous studies. Then the effects of different conditions of body mass distribution, touchdown velocities, and different types of shoes on the GRF are investigated and discussed. The results of this study can be used in producing and development of humanoid robots, artificial limbs, etc.

关键词： Calf muscle Ground Reaction Force Active Mass-Spring-Damper model particle swarm optimization algorithm

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Frequency Stability of Microgrids Based on H∞ Methods 35

Frequency Stability of Microgrids Based on H∞ Methods

引用

第35届中国控制会议

作者： ZHAO Jian WANG ChaoLi School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology

ISBN: (纸本)9781509009107

The widespread use of renewable energy in microgrid power systems would cause frequency *** in this work,is the frequency stability of the microgrid consisting of wind power systems,photovoltaic systems,micro-turbines,electrolyzers and fuel cells with the variation of load power.A novel robust controller is designed for the microgrid to suppress frequency fluctuation by tuning the output of the micro-turbine,electrolyzer and fuel *** generation-rate constraint of the micro-turbine is *** integrating the switching states of the fuel cell and electrolyzer with the micro-turbine power output in order to prevent too large changing rate of the micro-turbine power and improve the utilization of renewable *** is well known that the key point of design a H∞ mixed sensitivity controller is how to select appropriate weighting ***,there are no effective rules to do *** this reason,the particle swarm optimization（PSO） algorithm is used to optimize the weighting functions to enable the system to achieve optimal *** results show that the mentioned frequency control strategy can effectively stabilize the frequency fluctuation and improve the utilization of renewable energy,and is still effective for the case of large-scale renewable energy penetration.

关键词： microgrid frequency control H∞ control particle swarm optimization algorithm weighting functions

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Reactive Power Management of MV Distribution Grid with Inverter-based PV Distributed Generations using PSO algorithm 45

Reactive Power Management of MV Distribution Grid with Inver...

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45th Annual Conference of the IEEE Industrial Electronics Society (IECON)

作者： Moondee, Wittaya Srirattanawichaikul, Watcharin Chiang Mai Univ Dept Elect Engn Fac Engn Chiang Mai Thailand Chiang Mai Univ Grad Sch Chiang Mai Thailand Prov Elect Author Bangkok Thailand

ISBN: (纸本)9781728148786

This paper presents the reactive power management approach for a medium voltage (MV) distribution grid with inverter-based photovoltaic (PV) distributed generations using a particle swarm optimization (PSO) algorithm. The objective of this research is to control the reactive power flow to support the distribution grid and voltage regulation. The control algorithm is based on centralized reactive power management at the MV distribution grid for allocating the optimal reactive power of each PV distributed generation. The system case consists of a PV system connected to the simple radial feeders in the distributed grid. Simulation studies of the proposed system are built through MATLAB and DIgSILENT PowerFactory software. The simulation results show that the proposed control algorithm is useful for mitigating voltage rise and reverse power flow in the MV distribution grid with the PV generation systems.

关键词： Reactive power management medium voltage distribution grid inverter-based photovoltaic distributed generation voltage regulation particle swarm optimization algorithm

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optimization Design of Wind Turbine Blades Based On BLADED

Optimization Design of Wind Turbine Blades Based On BLADED

引用

2nd International Conference on Advanced Engineering Materials and Technology (AEMT)

作者： Zheng, Jiahong Ma, Haoran Shaanxi Univ Sci & Technol Inst Elect & Mech Engn Xianyang Peoples R China

ISBN: (纸本)9783037854471

The optimization of wind turbine blades can increase the generator power and annual output of electricity. Illustrated by the case of 2MW wind turbine, optimizing the blade chord and twist angle by POS algorithm. Modeling by BLADED, analysis the change of lift and drag coefficients, the power coefficient, maximum power of wind turbine, minimum power of wind turbine, wind turbine generating capacity before and after optimization. The results show: the aerodynamic efficiency of optimized blade increased by 4.833 than that before optimization. In the wind speed of 12 m/s (that is lower and normal speed), the average power coefficient is improved by 0.05. The minimum power of the wind turbine increased by 4% -15%.The maximum power of the wind turbine increased by 3% -9%. And the annual production of power increased by 0.25%.

关键词： Wind turbine blades particle swarm optimization algorithm optimization design BLADED

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A Speech Recognition System Based on Fuzzy Neural Network trained by Artificial Bee Colony algorithm

A Speech Recognition System Based on Fuzzy Neural Network tr...

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IEEE International Conference on Electronics, Communications and Control (ICECC)

作者： Ning, Aiping Zhang, Xueying Taiyuan Univ Technol Coll Informat Engn Taiyuan Peoples R China

ISBN: (纸本)9781457703218

Training fuzzy neural network (FNN) is an optimization task which is desired to find optimal centers of the membership function and weights. Traditional training algorithms have some drawbacks such as getting stuck in local minima and computational complexity. This work presents FNN trained by artificial bee colony (ABC) optimization algorithm which has good exploration and exploitation capabilities. FNN trained by this algorithm is applied to speech recognition system and compares its performance with particle swarm optimization (PSO) algorithm and back-propagation (BP) algorithm. The experimental results prove that ABC algorithm has better recognition results and convergence speed than FNN trained by BP algorithm and has similar recognition results and convergence speed than FNN trained by PSO.

关键词： Artificial Bee Colony algorithm particle swarm optimization algorithm Fuzzy Neural Network Speech Recognition

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Identifying New Prognostic Features for Remaining Useful Life Prediction Using particle Filtering and Neuro-Fuzzy System Predictor 15

Identifying New Prognostic Features for Remaining Useful Lif...

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IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

作者： Boukra, Tahar Univ 20 Aout 1955 Lab Electrotech Skikda LES Dept Genie Elect Skikda Algeria

ISBN: (纸本)9781479979929

An accurate prediction of the remaining useful life (RUL) from a prognosis system relies on a good selection of prognosis features. The latter should well capture the trend of the fault progression. In situation where the development of degradation model is difficult, we must be addressed to the identification of new features having an obvious trending quality. in this context, This paper present a new selection method based upon a particle swarm optimization algorithm to identify the advanced prognosis feature and a particle filtering for the prediction of the remaining useful life. The fault growth model is integrated to the particle filter using a Neuro-Fuzzy System with its process noise. This method was validated on a set of experimental data collected from bearings run-to-failure tests.

关键词： Prognostics Remaining Useful Life particle swarm optimization algorithm Feature selection particle Filter Neuro-Fuzzy System

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