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Optimal Overcurrent Relays Coordination in Microgrid Using Cuckoo algorithm

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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Modelling of gas to hydrate conversion for promoting CO₂ capture processes in the oil and gas industry

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PETROLEUM SCIENCE AND TECHNOLOGY 2016年第7期34卷 642-651页

作者： Baghban, Alireza Bahadori, Mohammad Kashiwao, Tomoaki Bahadori, Alireza Petr Univ Technol Ahwaz Fac Petr Engn Dept Gas Engn Ahvaz Iran Griffith Univ Griffith Sch Environm Nathan Campus Nathan Qld 4111 Australia Natl Inst Technol Dept Elect & Control Engn Niihama Japan So Cross Univ Sch Environm Sci & Engn Lismore NSW 2480 Australia

In this contribution, equilibrium conditions of clathrate hydrates containing mixtures of carbon dioxide and tetra-n butylammonium bromide and tetra-n butylammonium chloride are modeled by a combination of particle swarm optimization algorithm with least square support vector machine intelligent approach. The evaluation of aforementioned model has been conducted by statistical analyses between experimental and predicted values. The obtained results show excellent performance of proposed model.

关键词： Semiclathrate hydrate CO2 TBAC TBAB promoter particle swarm optimization algorithm least square support vector machine

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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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Optimized sensor and sensorless control of PMSM modeled in discrete mode A comparison of KH and PSO algorithms

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2016年第3期35卷 1293-1320页

作者： Younesi, Aria Kazemi, Yousef Moradpour, Arman Tohidi, Sajjad Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Purpose - The purpose of this paper is to improve dynamic performance of the permanent magnet synchronous motor (PMSM) field-oriented control by means of optimizing its PI controller's coefficients. Design/methodology/approach - In this paper, Krill Herd (KH) optimization algorithm is proposed to optimize PI controller coefficients. Moreover, drive control system and steady state estimator are implemented in discrete mode. In order to evaluate the performance of the proposed algorithm, a comparison is carried out between KH and particle swarm optimization (PSO) algorithms. It is noteworthy that PSO algorithm has presented the best dynamic response among the studied approaches in literature. Findings - By using the proposed optimization algorithm, the system tracks the reference speed with a very low error. In addition, the discrete mode simulations do not require initiating integrators. Moreover, it significantly increases the processing speed. The proposed approach is carried out in both sensor and sensorless drive systems. The simulation results indicate lower torque ripple, shorter settling time, lower overshoot, faster dynamic response and higher robustness against speed, torque and machine parameter changes. Practical implications - The PI controller is extensively used in practical drive systems, due to its simple structure and implementation as well as its acceptable performance. On the other hand, PMSM benefits from high-power density, high efficiency, low torque ripple, low inertia, and high performance in a wide speed range. This paper presents an efficient approach to optimize PI coefficients of the PMSM drive system. Originality/value - This paper proposes a new algorithm (KH algorithm) to determine the coefficients of PI controllers in order to improve dynamic performance of the PMSM drive system.

关键词： Permanent magnet synchronous motor Field-oriented control Krill Herd optimization algorithm particle swarm optimization algorithm PI controller

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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

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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Balancing and positioning for a gun control system based on fuzzy fractional order proportional-integral-derivative strategy

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ADVANCES IN MECHANICAL ENGINEERING 2016年第3期8卷 1-9页

作者： Gao, Qiang Li, Kang Hou, Yuanlong Hou, Runmin Wang, Chao Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Jiangsu Peoples R China

To achieve perfect behavior of the unbalanced barrel of a gun control system, a novel control strategy for simultaneous balancing and positioning of the system is proposed, physically being on the basis of a novel hydraulic cylinder with three cavities. The fuzzy fractional order proportional-integral-derivative controller is developed, and the particle swarm optimization algorithm is adopted for optimal selection of the control parameters for the gun control system. The results demonstrate that the fuzzy fractional order proportional-integral-derivative control strategy can finely improve dynamic performance of the control system, and the nonlinear characteristics of system can be effectively suppressed.

关键词： Gun control system balancing and positioning fuzzy fractional order proportional-integral-derivative particle swarm optimization algorithm

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Applying particle swarm optimization algorithm to roundness measurement

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第2期36卷 3428-3438页

作者： Sun, Te-Hsiu Chaoyang Univ Technol Dept Ind Engn & Management Wufeng 413 Taichung County Taiwan

Measuring the roundness of a circular workpiece is a common problem of quality control and inspection. In this area, maximum inscribed circle (MIC) and maximum circumscribing circle (MCC), minimum zone circle (MZC) and least square circle (LSC) are four commonly used methods. In particular, MIC, MCC, and MZC, which are nonlinear constrained optimization problems, have not been thoroughly discussed lately. This study proposes a machine vision-based roundness measuring method that applies the particle swarm optimization algorithm (PSO) to compute MIC, MCC and MZC. To facilitate the PSO process, five different PSO's were encoded using a radius (R) and circle center (x,y) and extensively evaluated using an experimental design, in which the impact of inertia weight, maximum velocity and the number of particles on the performance of the particle swarm optimizer was analyzed. The proposed method was verified with a set of testing images and benchmarked with the GA-based (genetic algorithm) method [Chen, M. C. (2000). Roundness inspection strategies for machine visions using non-linear programs and genetic algorithms. International Journal of Production Research, 38, 2967-2988]. The experimental results reveal that the PSO-based method effectively solved the MIC, MCC, and MZC problems and outperforms GA-based method in both accuracy and the efficiency. As a finals, several industrial applications are presented to explore the effectiveness and efficiency of the proposed method. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Roundness measurement Machine vision particle swarm optimization algorithm

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Using Hybrid particle swarm optimization Based on the Generating Capacity Adjustment Mechanism to Solve Economic Dispatch Problems of Cogeneration Systems

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2015年第4期24卷

作者： Chang, Hong-Chan Lin, Shang-Chih Kuo, Cheng-Chien Chiang, Chao-Lung Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10607 Taiwan St Johns Univ Dept Elect Engn Tamsui 25135 Taiwan Nan Kai Univ Technol Dept Elect Engn Nantou 54243 Taiwan

This paper presents a computer technology called hybrid particle swarm optimization (HPSO) algorithm, to solve the economic dispatch (ED) problem of cogeneration systems. First outlined the status of the power system development and technical discussion, followed by test cases from the literature are used to evaluate the accuracy and validity of the algorithm. The HPSO algorithm combines genetic algorithm-specific mutation factor (MF) characteristics and the disturbance mechanism (DM), then used the exponential function to control the MF. A generating capacity adjustment mechanism (GCAM) is designed help the algorithm effectively identify the global optimum, effectively enhance the performance of the algorithm, the advantages of reducing computation time and the search range is reached.

关键词： particle swarm optimization algorithm economic dispatch cogeneration system mutation factor disturbance mechanism generating capacity adjustment mechanism

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