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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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optimization and CONTROL of a HEV

Optimization and CONTROL of a HEV

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International Conference on Electrical, Electronics, and optimization Techniques (ICEEOT)

作者： Farahat, Mohammad Esmaeil Farahat, Said Islamic Azad Univ Aliabad Katoul Branch Dept Elect Engn Aliabad Katoul Iran Univ Sistan & Baluchestan Dept Mech Engn Zahedan Iran

ISBN: (纸本)9781467399395

Vehicles play an important role in transportation. Using fossil fuels as their source of energy, they are a source of pollution. HEVs are improving and a day will come that they will take over the role of conventional vehicles. In the path of improving HEVs, optimizing components sizes of the vehicle and better strategy controls are vital methods to reach the desired goals. In this path practical tools for optimization are evolutionary algorithms and fuzzy controllers as a strategy control decision maker. In this paper, accompanied with these tools extraordinary improvements are made. On the UDDS cycle using PSO algorithm we reached 57%, 68%, 34% and 8% decrease respectively in fuel consumption, emission, economic and acceleration and we reached 30% increase in maximum speed gained. On the HWFET cycle we reached 74%, 79%, 34% and 8% decrease respectively in fuel consumption, emission, economic and acceleration and we reached 30% increase in maximum speed gained. Acquiring a Pareto Front for two fitness functions we can choose a specific vehicle with special capabilities according to our priorities.

关键词： Hybrid vehicles particle swarm optimization algorithm power management strategy sizing drive cycle

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Optimal location and sizing of STATCOM using Fuzzy-PSO approach 6

Optimal location and sizing of STATCOM using Fuzzy-PSO appro...

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6th IEEE International Conference on Power Systems (ICPS)

作者： Rana, Bharat Singh Srivastava, Laxmi Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

ISBN: (纸本)9781509001286

In this paper, an evolutionary computing technique comprising of Fuzzy logic and particle swarm optimization (PSO) algorithm has been proposed for optimal location and sizing of STATCOM to improve the voltage stability and to minimize the total voltage deviation in a power system. The proposed Fuzzy-PSO algorithm has been applied using two steps. As the first step, the weakest buses selected for placement of STATCOM using modal analysis. In the second step Fuzzy-PSO algorithm has been applied for selecting optimal location of STATCOM considering these selected weakest buses. The proposed approach has been applied on the standard IEEE 30-bus system considering the most severe single line outage contingencies under normal loading and under stressed condition. The proposed Fuzzy-PSO approach has been found to be quite satisfactory.

关键词： fuzzy performance index modal analysis particle swarm optimization algorithm STATCOM voltage stability

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Short-Term Traffic Flow Prediction Method Based on Grey Adaptive particle swarm Support Vector Machine 2

Short-Term Traffic Flow Prediction Method Based on Grey Adap...

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2nd IEEE International Conference on Computer and Communications (ICCC)

作者： Duo, Mei Xu, E. Qi, Yan Bohai Univ Coll Informat Sci & Technol Jinzhou Peoples R China

ISBN: (纸本)9781467390262

In order to further improve the accuracy of the short-term traffic flow prediction, a combination of short-term traffic flow prediction model had been proposed by analyzing the characteristics of grey model, adaptive particle swarm optimization (PSO) algorithm and support vector machine (SVM) model. First, use the grey model to accumulate the original traffic flow data, weaken the randomness of the traffic flow data sequence, then optimize the support vector machine model based on adaptive particle swarm optimization algorithm and realize short-term traffic flow prediction, finally, get the final predicted value table by grey mode. The model was verified based on the traffic flow data of the major road in Changchun and the experimental result showed the proposed model was effective and feasible.

关键词： traffic flow prediction grey model particle swarm optimization algorithm support vector machine

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A Design Method of Zero Ripple Input Current DC-DC Converter Assisted by Constrained GA&PSO algorithms to Minimize Power Losses 19

A Design Method of Zero Ripple Input Current DC-DC Converter...

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19th International Conference on Electrical Machines and Systems (ICEMS)

作者： Marsala, Giuseppe Ragusa, Antonella CNR ISSIA UOS Palermo Italy

ISBN: (纸本)9784886860989

The paper presents a constrained optimization procedure to design a DC-DC converter with coupled inductors for minimizing the power losses. Two algorithms have been used, Genetic and particle swarm optimization algorithm, and the results have been compared. In particular, with the proposed technique, the electrical, magnetic and geometrical characteristics of the coupled inductors have been obtained and the values of duty cycle and frequency have been defined in order to obtain the maximum efficiency.

关键词： DC-DC converter Genetic algorithm particle swarm optimization algorithm power losses

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Solving a multi-stage multi-product solid supply chain network design problem by meta-heuristics

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SCIENTIA IRANICA 2016年第3期23卷 1429-1440页

作者： Mahmoodirad, A. Sanei, M. Islamic Azad Univ Cent Tehran Branch Dept Math Tehran Iran

This paper presents an effective optimization method based on meta-heuristics algorithms for the design of a multi-stage, multi-product solid supply chain network design problem. First, a mixed integer linear programming model is proposed. Second, because the problem is an NP-hard, three meta-heuristics algorithms, namely Differential Evolution (DE), particle swarm optimization (PSO), and Gravitational Search algorithm (GSA), are developed for the first time for this kind of problem. To the best of our knowledge, neither DE, nor PSO, nor GSA have been considered for the multi-stage solid supply chain network design problems. Furthermore, the Taguchi experimental design method is used to adjust the parameters and operators of the proposed algorithms. Finally, to evaluate the impact of increasing the problem size on the performance of our proposed algorithms, different problem sizes are applied and the associated results are compared with each other. (C) 2016 Sharif University of Technology. All rights reserved.

关键词： Supply chain network design Differential evolution particle swarm optimization algorithm Gravitational search algorithm Taguchi experimental Design

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Optimizing Thermal-Elastic Properties of C/C-SiC Composites Using a Hybrid Approach and PSO algorithm

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MATERIALS 2016年第4期9卷 222-222页

作者： Xu, Yingjie Gao, Tian Northwestern Polytech Univ ESAC Xian 710072 Peoples R China

Carbon fiber-reinforced multi-layered pyrocarbon-silicon carbide matrix (C/C-SiC) composites are widely used in aerospace structures. The complicated spatial architecture and material heterogeneity of C/C-SiC composites constitute the challenge for tailoring their properties. Thus, discovering the intrinsic relations between the properties and the microstructures and sequentially optimizing the microstructures to obtain composites with the best performances becomes the key for practical applications. The objective of this work is to optimize the thermal-elastic properties of unidirectional C/C-SiC composites by controlling the multi-layered matrix thicknesses. A hybrid approach based on micromechanical modeling and back propagation (BP) neural network is proposed to predict the thermal-elastic properties of composites. Then, a particle swarm optimization (PSO) algorithm is interfaced with this hybrid model to achieve the optimal design for minimizing the coefficient of thermal expansion (CTE) of composites with the constraint of elastic modulus. Numerical examples demonstrate the effectiveness of the proposed hybrid model and optimization method.

关键词： C/C-SiC composites micromechanical modeling BP neural network particle swarm optimization algorithm thermal-elastic properties

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Optimal Overcurrent Relays Coordination in Microgrid Using Cuckoo algorithm

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Energy Procedia 2016年 100卷 280-286页

作者： Amir Ahmarinejad Seyed Mohsen Hasanpour Mojtaba Babaei Mohammad Tabrizian Department of Electrical Engineering Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch Islamic Azad University Tehran Iran

Microgrid(MG) is a controlled branch of Distributed Generations(DGs) and loads in distribution network and offers more potential in DGs operation and control. MG can operate with and without utility grid (Island mode). The operation of this system in islanded mode requires sophisticated control and protection schemes. MG protection should be capable of fault detection with appropriate sensitivity and selectivity in both rigid-connected and islanded modes. This paper presents solving the issue of short circuit level difference in both modes using fault current limiter (FCL). Overcurrent(O/C) relays coordination and fault current limiter impedance are selected in such a way that protection system has a suitable operation for both modes. For this purpose, artificial intelligence methods of particle swarm optimization (PSO) algorithm and Cuckoo algorithm have been used to achieve optimal relays coordination and fault current limiter impedance. The under study grid is a small MG related to Tehran Oil Refinery and the simulations have been running using MATLAB. The results indicate that fault current limiter can be used to solve the problem of short current level difference in both modes and Cuckoo algorithm is a more effective method for optimization applications.

关键词： Microgrid Overcurrent Relays Fault Current Limiters Cuckoo algorithm particle swarm optimization algorithm

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An Integrated Hybrid algorithm Based on Nature Inspired Evolutionary for Radial Basis Function Neural Network Learning

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2016年第2期25卷 1650004-1650004页

作者： Chen, Zhen-Yao Kuo, R. J. Hu, Tung-Lai DE LIN Inst Technol Dept Business Adm 1Ln 380Qingyun Rd New Taipei 23654 Taiwan Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Keelung Rd Taipei 10607 Taiwan Natl Taipei Univ Technol Dept Business Management 1Sec 3Zhongxiao E Rd Taipei 10608 Taiwan

This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time.

关键词： Function approximation radial basis function neural network particle swarm optimization algorithm genetic algorithm sales forecasting

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A Novel Approach to Task Scheduling using The PSO algorithm based Probability Model in Cloud Computing

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第11期9卷 309-321页

作者： Li Ruizhi Gao Jue Gao Honghao Bian Minjie Xu Huahu Shanghai Univ Sch Engn & Comp Sci Shanghai 200444 Peoples R China Shanghai Shang Da Hai Run Informat Syst Co Ltd Shanghai Peoples R China Shanghai Univ Ctr Comp Shanghai 200444 Peoples R China

With the development of cloud computing technology, people not only want to pursue the shortest time to complete the tasks by using cloud computing, but also hope to take into the running costs of machines. Existing task scheduling algorithm in the cloud computing environment has been unable to meet people's needs. As an extension and generalization of the model checking theory, probability model checking is also used in many fields, such as random distributed algorithm and other areas. The task scheduling algorithm based on the particle swarm optimization algorithm combined with probability model is proposed in this paper. The algorithm defines the fitness functions of the time cost and the running cost. The fitness functions can improve the efficiency of the cloud computing platform. At the same time, the probability model can be used to analyze the running states of machines and the computing capability of the nodes in the cloud cluster. The probability, which is calculated by the probability model, provides the basis for changing particle swarm algorithm's the inertia factor and the learning factor, so as to solve the drawback that the inertia factor and the learning factor solely depend on the fixed value.

关键词： particle swarm optimization algorithm Probability Model Inertia Factor Learning Factor Auto-correcting

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