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ASME Turbo Expo: Turbine Technical Conference and Exposition

作者： Yang, B. Xu, Q. He, L. Zhao, L. H. Gu, Ch. G. Ren, P. Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200030 Peoples R China Shanghai Elect Power Generat R&D Ctr Shanghai Peoples R China

ISBN: (纸本)9780791845578

In this paper, a novel global optimization algorithm has been developed, which is named as Particle Swarm optimization combined with Particle Generator (PSO-PG). In PSO-PG, a particle generator was introduced to iteratively generate the initial particles for PSO. Based on a series of comparable numerical experiments, it was convinced that the calculation accuracy of the new algorithm as well as its optimization efficiency was greatly improved in comparison with those of the standard PSO. It was also observed that the optimization results obtained from PSO-PG were almost independent of some critical coefficients employed in the algorithm. Additionally, the novel optimization algorithm was adopted in the airfoil optimization. A special fitness function was designed and its elements were carefully selected for the low-velocity airfoil. To testify the accuracy of the optimization method, the comparative experiments were also carried out to illustrate the difference of the aerodynamic performance between the optimized and its initial airfoil.

关键词： optimization optimization algorithms Airfoils

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Real-World Dynamic optimization Using An Adaptive-mutation Compact Genetic Algorithm

Real-World Dynamic Optimization Using An Adaptive-mutation C...

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IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

作者： Uzor, Chigozirim J. Gongora, Mario Coupland, Simon Passow, Benjamin N. De Montfort Univ Ctr Computat Intelligence Leicester Leics England De Montfort Univ DIGITS Leicester Leics England

ISBN: (纸本)9781479945153

While the interest in nature inspired optimization in dynamic environments has been increasing constantly over the past years, evaluations of some of these optimization algorithms are based on artificial benchmark problems. Little has been done to carry-out these evaluation using a real-world dynamic optimization problems. This paper presents a compact optimization algorithm for controllers in dynamic environments. The algorithm is evaluated using a real world dynamic optimization problem instead of an artificial benchmark problem, thus avoiding the reality gap. The experimental result shows that the algorithm has an impact on the performance of a controller in a dynamic environment. Furthermore, results suggest that evaluating the algorithm's candidate solution using an actual real-world problem increases the controller's robustness.

关键词： control system synthesis dynamic programming genetic algorithms performance index stability vibration control PID controller design adaptive-mutation compact genetic algorithm compact optimization algorithm controller performance controller robustness dynamic environment nature inspired optimization real-world dynamic optimization problems torsional mass-spring damper system Microcontrollers Microcontrollers control system synthesis Genetic Algorithm (GA) vibration control performance indicators dynamic programming optimization algorithms Controllers Benchmark testing controller design

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An Accelerated Dual Gradient-Projection Algorithm for Embedded Linear Model Predictive Control

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2014年第1期59卷 18-33页

作者： Patrinos, Panagiotis Bemporad, Alberto IMT Inst Adv Studies Lucca I-55100 Lucca Italy

This paper proposes a dual fast gradient-projection method for solving quadratic programming problems that arise in model predictive control of linear systems subject to general polyhedral constraints on inputs and states. The proposed algorithm is well suited for embedded control applications in that: 1) it is extremely simple and easy to code;2) the number of iterations to reach a given accuracy in terms of optimality and feasibility of the primal solution can be tightly estimated;and 3) the computational cost per iteration increases only linearly with the prediction horizon.

关键词： Computational methods optimization algorithms predictive control for linear systems

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Multiobjective consequent management of a contaminated network under pressure-deficient conditions

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JOURNAL AMERICAN WATER WORKS ASSOCIATION 2014年第10期106卷 E470-E480页

作者： Afshar, Amin Marino, Miguel A. Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA

This article presents an integrated simulation optimization scheme for operational response management. It integrates a pressure-driven network solver (PDNS) with the multiobjective honey bee mating optimization (HBMO) algorithm. Solutions to the proposed multiobjective optimization problem generate a set of nondominated optimal operational strategies that minimizes consequences of intentional physical attacks on water infrastructure systems. Each trial solution developed by the optimizer defines a new network topology resulting from modified operation modes of nominated valves and hydrants. The PDNS finalizes the nodal pressure and modifies nodal withdrawal for the identified trial solution. Performance of the approach is illustrated using previously tested examples. Sensitivity of the optimal strategies to mathematical definition of the objective function is discussed.

关键词： optimization algorithms Simulation Topology Water supply

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Reliability-based maintenance scheduling of generating units in restructured power systems

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2014年第5期22卷 1147-1158页

作者： Fotuhi-Firuzabad, Mahmud Aminifar, Farrokh Shahzadeh, Abbas Sharif Univ Technol Dept Elect Engn Ctr Excellence Power Syst Management & Control Tehran Iran Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran Aryan Sanat Co Tehran Iran

Maintenance scheduling of generating units in restructured power systems is a collaborative and interactive process between independent system operators (ISOs) and generating companies (GENCOs). The ISO should comply with GENCO maintenance preferences subject to targeted system reliability levels. This process might be multistage since the admission of all initial unit outage requests may threaten system reliability. Hence, the ISO accepts some proposals and determines alternatives for the remaining units. The GENCOs are then allowed to confirm the alternatives or revise them. In this paper the ISO problem of generating unit maintenance scheduling is tackled. The objective function is to minimize the deviation of awarded schedule from the requested schedule, as measured in MW weeks. A novel and effective optimization technique is developed to solve the problem at hand. In the proposed methodology, risk leveling and dynamic programming optimization algorithms are concurrently utilized to lessen the computation burden and enhance the solution process. The effectiveness of the proposed method and its applicability to real-life systems are verified by examining the generation section of the IEEE reliability test system (IEEE-RTS). The performance of the new method is also compared with that of the individual risk leveling approach.

关键词： Dynamic programming maintenance scheduling optimization algorithms reliability assessment risk leveling

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Transferability of SWAT Models between SWAT2009 and SWAT2012

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JOURNAL OF ENVIRONMENTAL QUALITY 2014年第3期43卷 869-880页

作者： Seo, Mijin Yen, Haw Kim, Min-Kyeong Jeong, Jaehak Texas A&M Univ College Stn TX 77843 USA Texas A&M AgriLife Res Blackland Res Ctr Temple TX 76502 USA Rural Dev Adm Natl Acad Agr Sci Suwon 441707 Kyeonggi South Korea

In recent years, the Soil and Water Assessment Tool (SWAT) has experienced upgrades with enhanced functionalities and modeling capacities as it gets to the current version, SWAT2012. Changes in the SWAT code on a specific process may result in propagating influences in the output of other related processes. In this study, the characteristic significance of the enhancements in SWAT code was investigated using the two recent versions, SWAT2009 and SWAT2012. Using a global optimization technique, each model was calibrated for flow, sediment, and nutrient and then tested for transferability of parameters between the models. Results indicate that flow and water quality output were well calibrated with both models. However, the calibrated parameters determined by SWAT2009 and SWAT2012 were noticeably different, due mostly to the enhancements made in SWAT2012. Our results indicate that only the stream flow result was reliable when the models were upgraded or downgraded between the two versions after calibration. Sediment prediction was marginally reliable. SWAT parameters were nontransferrable if nutrient was the main output. The differences are due to various reasons, such as disparities in algorithms at the process level and propagation of the resulting uncertainty into higherorder processes.

关键词： Studies Watersheds Models Groundwater optimization algorithms Soil erosion Water quality

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A novel self-adaptive learning charged system search algorithm for unit commitment problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第1期26卷 439-449页

作者： Niknam, Taher Bavafa, Farhad Jabbari, Masoud Shiraz Univ Technol SUTech Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Marvdasht Branch Dept Elect Engn Marvdasht Iran

Charged System Search (CSS) is a new evolutionary algorithm inspired by the interaction between charged particles. This paper presents a modified CSS (SACSS) algorithm, which highly improves the performance of CSS and applies it to solve the unit commitment (UC) problem. In order to achieve better performance and higher speed in solving the UC problem, a self-adaptive reformation technique with tree updated schemes has been implemented. The proposed algorithm has been tested on 10, 20, 40 and 100 unit systems for one-day and scheduling horizon. The results are compared with the solutions obtained from other methods such as Lagrangian Relaxation (LR), Genetic Algorithm (GA), Particle Swarm optimization (PSO), Bacterial Foraging (BF) and Shuffled Frog Leaping Algorithm (SFLA). The results show the high performance and convergence speed of SACSS.

关键词： Charged system search economic dispatch generation scheduling optimization algorithms unit commitment

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Wideband digital integrators and differentiators designed using particle swarm optimisation

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IET SIGNAL PROCESSING 2014年第6期8卷 668-679页

作者： Gupta, Maneesha Relan, Bhavesh Yadav, Richa Aggarwal, Varun Netaji Subhas Inst Technol Dept Elect & Commun Engn Adv Elect Lab New Delhi 110075 India Netaji Subhas Inst Technol Dept Comp Engn New Delhi 110075 India AspringMinds Gurgaon Haryana India

Digital integrators (DIs) and digital differentiators (DDs) of second, third and fourth-order based on particle swarm optimisation (PSO) algorithm are presented. A modified particle swarm optimisation (MPSO) algorithm with reducing maximum velocity has been used to optimise the mean square error of the digital operators. Statistical and simulation results have been presented for comparing quality of optimal operators obtained by MPSO, genetic algorithm (GA), two variants of PSO and PSO-GA hybrid techniques. The results obtained for best solutions by the proposed algorithm are either superior or at par with the basic PSO variants and hybrid techniques. The proposed digital operators have also been simulated using MATLAB, and the results have been compared with that of existing DIs and DDs derived by different optimisation algorithms, to demonstrate the effectiveness of the use of proposed MPSO. The relative magnitude errors (dB) obtained for digital integrators and differentiators are as low as -40 and -35 dB, respectively, which are valid for almost the full band of normalised frequency.

关键词： MATLAB normalized frequency Particle swarm optimization maximum speed Methylprednisolone Suleptanate Mean square error optimization algorithms DIFFERENTIATORS parametric subharmonic oscillator

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Automated Design of Optimal Finite Thrust Orbit Insertion with Ballistic Flyby Constraints

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JOURNAL OF SPACECRAFT AND ROCKETS 2014年第6期51卷 1872-1884页

作者： Jesick, Mark Ocampo, Cesar Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA Univ Texas Austin Dept Aerosp Engn & Engn Mech WR Woolrich Labs Austin TX 78712 USA

An automated method is developed to generate a minimum-fuel, finite thrust orbit insertion sequence while simultaneously generating a flyby trajectory of the same body that satisfies pre- and postflyby conditions in a general force field with ephemeris-level dynamics. The initial estimate requires no user input, and an impulsive solution is automatically converted to an optimal finite thrust transfer. As part of the initial estimate, a new method of generating ephemeris-level single flyby free returns is presented. A hybrid method is implemented for the optimal control solution whereby the costates are added to the vector of free parameters and the cost function is minimized directly. Analytic gradients are derived to decrease computation time and aid convergence of the optimization algorithm. The method is successfully applied in the Earth-moon system with application to human spaceflight and in the Saturn-Titan system with application to robotic spaceflight.

关键词： Optimal control Computer-Aided Design MANNED SPACE FLIGHT Cost functions computational time Earth-Moon couple optimization algorithms Optimal Automated methods

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Application of random search method for maximum power point tracking in partially shaded photovoltaic systems

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IET RENEWABLE POWER GENERATION 2014年第6期8卷 670-678页

作者： Sundareswaran, Kinattingal Peddapati, Sankar Palani, S. Natl Inst Technol EEE Dept Tiruchirappalli 620015 Tamil Nadu India Sudharsan Engn Coll Pudukkottai 620015 Tamil Nadu India

The power-voltage (P-V) curve of a photovoltaic (PV) power generation system under partially shaded conditions (PSCs) is largely non-linear and multimodal, and hence, global optimisation techniques are required for maximum power point tracking. A traditional optimisation algorithm is proposed here, namely random search method (RSM) for tracking the global maximum power point in a solar power system under PSC. The RSM is based on the use of random numbers in finding the global optima and is a gradient independent method. The major advantage of RSM is its very simple computational steps, which requires very less memory. The performance of RSM in tracking the peak power is studied for a variety of shading patterns and the tracking performance is compared with two-stage perturb and observe (P&O) and population-based particle swarm optimisation (PSO) methods. The simulation results strongly suggest that the RSM is far superior to two-stage P& O method and better than PSO method. Experimental results obtained from a 120-watt prototype PV system validate the effectiveness of the proposed scheme.

关键词： ELECTRIC POWER GENERATION SYSTEMS Thermoelectric system maximum loads random numbers visual tracking Solar collectors Search methods Photovoltaic systems maximum power point trackers Random search method Power Points optimization algorithms solar energy system PO

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