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Corrective action planning considering FACTS allocation and optimal load shedding using bacterial foraging oriented by particle swarm optimization algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2010年第4期18卷 597-612页

作者： Hooshmand, Rahmat Allah Pour, Mostafa Ezatabadi Univ Isfahan Dept Elec Eng Esfahan *** Iran

Reactive power planning (RPP) involves optimal allocation and determination of the type and size of new reactive power (VAR) supplies to satisfy voltage constraints during normal and contingency states. The RPP issue is in fact an optimization of large scale mixed integer nonlinear programming problem, so it is proper to use an evolutionary algorithm to solve the problem. In this paper, in order to solve the RPP problem for corrective action of power systems, the bacterial foraging (BF) oriented by particle swarm optimization (pso) algorithm (BF-pso) is proposed. In the algorithm, the VAR control has been carried out by using flexible AC transmission systems (FACTS) devices, in order to minimize the installation costs of these devices. In order to determine the saving rate in the costs, corrective control is also performed by the utilization of load shedding algorithm. The IEEE 57-Bus system, is used to test the proposed method. The simulation results of the proposed algorithm are compared with pso and genetic algorithms (GA) to show the efficiency of this method in the RPP problem.

关键词： FACTS Devices Reactive Power Planning Load Shedding BF algorithm pso algorithm

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Broadband MVDR Beamformer Applying pso 1

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1st International Conference on Swarm Intelligence

作者： Wang, Liang Song, Zhijie Ocean Univ China Dept Ocean Technol Qingdao 266100 Peoples R China

ISBN: (数字)9783642134951

ISBN: (纸本)9783642134944

In this paper, a broadband MVDR(minimum variance distortionless response) beamforming method based on time-domain (TMVDR) is presented. Using TMVDR beamformer, stable sample matrix estimation could be obtained in short time period. To obtain the stable optimum solution of TMVDR, a numerical searching method optimized by pso algorithm with constrain condition is introduced. Out-sea experiment show the performance of TMVDR beamformer applying pso algorithm proposed in this paper.

关键词： DOA estimation adaptive beamtorming pso algorithm

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Optimal Location of FACTS Devices Using Adaptive Particle Swarm Optimization Hybrid with Simulated Annealing

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2010年第2期5卷 179-190页

作者： Ajami, Ali Aghajani, Gh Pourmahmood, M. Azarbaijan Univ Tarbiat Moallem Dept Elect Engn Tehran Iran Tabriz Univ Fac Elect Comp Engn Tabriz Iran

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm. optimization (pso) called adaptive particle swarm optimization (Apso), mixed with simulated annealing (SA), is introduced and referred to as Apso-SA. This algorithm uses a novel pso algorithm (Apso) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The Apso-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of Apso-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The Apso-SA is used to solve the above non linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional pso. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional pso algorithm. The optimization results are compared with the standard pso method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the Apso-SA is fast and has a much lower computational cost.

关键词： FACTS devices optimal location pso algorithm SA algorithm Apso-SA algorithm

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Swarm Tetris: Applying Particle Swarm Optimization to Tetris

Swarm Tetris: Applying Particle Swarm Optimization to Tetris

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2010 IEEE World Congress on Computational Intelligence

作者： Langenhoven, Leo van Heerden, Willem S. Engelbrecht, Andries P. Univ Pretoria Computat Intelligence Res Grp Dept Comp Sci ZA-0002 Pretoria South Africa

ISBN: (纸本)9781424481262

This paper investigates the applicability of swarm-based algorithms to the game of Tetris. This work proposes an approach to the problem in which neural network weight values are optimized using a particle swarm optimization (pso) algorithm. Such an approach has not previously been demonstrated as feasible for Tetris. The reported experimental results show the learning progress of the algorithm, as well as a comparison against a hand-optimized Tetris playing algorithm. The results indicate that the Tetris agents show a continuous improvement over the course of training. Since the experimental focus was on the feasibility of the approach rather than optimizing performance, optimized pso-based agents were found to be outperformed by the hand-optimized algorithm. However, the playing strategies of the two agents were compared and shown to be similar. The results indicate that a swarm-based approach is feasible, and warrants further investigation.

关键词： pso algorithm pso-based agents computer games hand-optimized algorithm hand-optimized tetris playing algorithm learning (artificial intelligence) learning progress neural nets neural network weight values particle swarm optimisation particle swarm optimization playing strategy swarm tetris swarm-based algorithms tetris agents

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Improvement of Grayscale Image 2D Maximum Entropy Threshold Segmentation Method

Improvement of Grayscale Image 2D Maximum Entropy Threshold ...

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International Conference on Logistics Systems and Intelligent Management

作者： Zheng, Liping Li, Guangyao Bao, Yun Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Tongji Univ CAD Res Ctr Shanghai 201804 Peoples R China

ISBN: (纸本)9781424473281

To increase the segmentation speed and efficiency, traditional 2D maximum entropy threshold segmentation method is improved. The improved segmentation method is called pso-SDAIVE algorithm. In this new algorithm, the 2D gray histogram is changed and forms the 2D D-value attribute gray histogram. When computing image entropy, the spatial gray information of pixels is included in computation. The improved entropy is called spatial different attribute information value entropy (SDAIVE). Otherwise, Particle Swarm Optimization (pso) algorithm is used to solve maximum of SDAIVE. The corresponding solution of SDAIVE maximum is taken as optimal image segmentation threshold. In experiment, segment different grayscale image and testify the algorithm performance. Experimental results show that pso-SDAIVE algorithm can quickly and accurately obtain segmentation threshold. Compare with other segmentation method, the cost time of solving optimal threshold is short. Otherwise, this algorithm can better segment noise image.

关键词： Grayscale Image Segmentation 2D Histogram Information Entropy Gray Probability pso algorithm

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Parameter Estimation of an Induction Machine using a Dynamic Particle Swarm Optimization algorithm

Parameter Estimation of an Induction Machine using a Dynamic...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Huynh, Duy C. Dunnigan, Matthew W. Heriot Watt Univ Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9781424463916

This paper proposes a new application of a dynamic particle swarm optimization (pso) algorithm for parameter estimation of an induction machine. The dynamic pso is one of the pso variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the pso as linear time-varying parameters. The acceleration coefficients are varied during the evolution process of the pso to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The algorithm uses the measurements of the three-phase stator currents, voltages, and the speed of the induction machine as the inputs to the parameter estimator. The experimental results obtained compare the estimated parameters with the induction machine parameters achieved using traditional tests such as the dc, no-load, and locked-rotor tests. There is also a comparison of the solution quality between a genetic algorithm (GA), standard pso, and dynamic pso. The results show that the dynamic pso is better than the standard pso and GA for parameter estimation of the induction machine.

关键词： pso algorithm asynchronous machines dynamic pso dynamic particle swarm optimization algorithm genetic algorithm genetic algorithms induction machine linear time-varying parameters optimization process parameter estimation parameter estimation particle swarm optimisation standard pso stators three-phase stator currents

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Generate and Optimize Fuzzy Rules Using the Particle Swarm algorithm

Generate and Optimize Fuzzy Rules Using the Particle Swarm A...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Esmin, A. A. A. Lambert-Torres, G. Univ Fed Lavras DCC UFLA Dept Comp Sci BR-37200000 Lavras MG Brazil

ISBN: (纸本)9781424465880

The success of fuzzy application to solve the control problems depends on a number of parameters, such as fuzzy rules and membership functions. The problem of generating desirable fuzzy rules is very important in the development of fuzzy systems, which are usually decided upon subjectively. This paper describes a very simple and straightforward fuzzy rule generation and optimization technique by using the Particle Swarm Optimization algorithm (pso). The proposed algorithm can obtain a set of fuzzy rules which cover the examples set in iterative process. The proposed method is tested with promising results.

关键词： pso algorithm control problems fuzzy control fuzzy rules optimization fuzzy systems membership function particle swarm optimisation particle swarm optimization algorithm

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Parameters Optimization in SVM Based-on Ant Colony Optimization algorithm

Parameters Optimization in SVM Based-on Ant Colony Optimizat...

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International Conference on Advances in Computer Science and Engineering

作者： Liu, Xiangying Jiang, Huiyan Tang, Fengzhen Northeastern Univ Software Coll Shenyang 110819 Peoples R China Northeastern Univ Sino Dutch Biomed & Informat Engn Coll Shenyang 110819 Peoples R China Northeastern Univ Minist Educ Key Lab Med Image Comp Shenyang 110819 Liaoning Provin Peoples R China

ISBN: (纸本)9780878492510

In this paper ACO (Ant Colony Optimization) algorithm, which is a well-known intelligent optimization method, is applied to selecting parameters for *** has the characteristics of positive feedback, parallel mechanism and distributed computation. This paper gives comparison of ACO-SVM, pso-SVM whose parameters are determined by particle swarm optimization algorithm, and traditional SVM whose parameters are decided through trial and error. The experimental results on real-world datasets show that this proposed method avoids randomness and subjectivity in the traditional SVM. Additionally it is able to gain better parameters which could dedicate to a higher classification accuracy than the pso-SVM. Results confirm that proposed optimization method is better than the two others.

关键词： SVM ACO algorithm pso algorithm

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Two-Layer Control of Multi-Chiller Systems

Two-Layer Control of Multi-Chiller Systems

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IEEE International Conference on Control Applications Part of 2010 IEEE Multi-Conference on Systems and Control

作者： Beghi, Alessandro Cecchinato, Luca Cosi, Giovanni Rampazzo, Mirco Univ Padua Dipartimento Ingn Informaz Via Gradenigo 6-B I-35131 Padua Italy Univ Padua Dipartimento Fis Tecn I-35131 Padua Italy

ISBN: (纸本)9781424453634

In HVAC plants of medium-high cooling capacity, multiple chiller systems are often employed. The performance of the system is evaluated in terms of user comfort, energy use, and financial costs. In this paper a two-layer control structure is proposed for control and optimization of a multiple chiller system. Set points and operating modes for cooling plant equipment can be set by the supervisor to maximize overall operating efficiency. At any given time, cooling needs can be met with various combinations of modes of operation and set points. In the proposed control architecture, a cooling load estimation algorithm is employed, and the selection of the optimal set of operation modes and set point values is performed by means of a pso algorithm. The performance of the two-layer control is evaluated by resorting to a dynamic simulation environment developed in Matlab/Simulink (TM), where the plant dynamics are accurately described. It is shown that the proposed technique gives superior performance with respect to standard approaches.

关键词： HVAC HVAC plants Matlab/Simulink pso algorithm control architecture cooling cooling load estimation cooling plant equipment dynamic simulation environment energy use financial costs medium-high cooling capacity multichiller systems multiple chiller systems particle swarm optimisation plant dynamics two-layer control structure user comfort

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Optimal Power Sharing and Design Optimization for Fuel Cell/Battery Hybrid Electric Vehicles Based on Swarm Intelligence

Optimal Power Sharing and Design Optimization for Fuel Cell/...

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25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition Conference

作者： Hegazy, Omar Van Mierlo, Joeri Verbrugge, Bavo Ellabban, Omar Vrije Univ Brussel Pl Laan 2 B-1050 Brussles Belgium Erasmus Univ Coll Brussels B-1070 Anderlecht Belgium

ISBN: (纸本)9789889968496

The powertrain control strategy and the components sizing significantly affect the vehicle performance, cost and fuel economy. This paper presents a developed control strategy for optimizing the power sharing between sources and components sizing by using particle swarm optimization (pso) algorithm. pso method is a member of the wide category of Swarm Intelligence methods (SI). This control strategy implemented on Fuel cell/Battery hybrid electric vehicle with the aim to achieve the best performance with minimum fuel consumption and minimum powertrain components sizing for a given driving cycle. The powertrain and the proposed control strategy have been simulated by MATLAB/SIIVIULlNK. The simulation results have demonstrated that the optimal sizing of the powertrain of Fuel cell/battery components and the minimum fuel consumption have been improved by applying the pso control strategy (EVS25 Copyright).

关键词： Fuel cell Hybrid Electric Vehicle (FCHEV) Powertrain Modeling Fuel cell Model Battery Model pso algorithm Control Strategies

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