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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Klimova, T. G. Nikolaeva, O. O. Temkina, R., V Natl Res Univ Moscow Power Engn Inst Moscow Russia

ISBN: (纸本)9781509056484

The subject of the article is the technique for determination of the automatic excitation controller (AEC) parameters based on measurements from phasor measurement units (PMU). This paper shows the possibility of using PMU measurements for determining the synchronous generator AEC parameters. The article considers the type of the AEC which is ARV-MT of the Russian production. By using the simulation in the hardware and software system RTDS (Real-Time Digital Simulator), PMU measurements have been obtained for different modes of the AEC operation. Processing of the results has been carried out in MATLAB. The developed technique is based on the processing of obtained measurements by optimization algorithm. As a result of the comparative analysis, the most suitable type of optimization has been chosen. The possibility of PMU measurements use for determining the AEC parameters in different modes of its operation is shown. Also, the results of application of the technique for cases of a different power level noise in the circuit is investigated. The developed technique determines the AEC parameters with a sufficient accuracy by using PMU measurements. Thus, the technique allows one to extend the field of PMU application, and, being further developed, will allow one to monitor the AEC and calculate its parameters in real time.

关键词： automatic excitation controller phasor measurement units optimization algorithms

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Optimal sub-band adaptive thresholding based edge preserved satellite image denoising using adaptive differential evolution algorithm

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NEUROCOMPUTING 2016年第PartB期174卷 698-721页

作者： Bhandari, A. K. Kumar, D. Kumar, A. Singh, G. K. PDPM Indian Inst Informat Technol Design & Mfg Jabalpur 482005 Madhya Pradesh India Natl Inst Technol Dept Elect & Commun Engn Patna 800005 Bihar India Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 Uttarakhand India

An image is often corrupted by different kinds of noise during its acquisition and transmission. Conventional denoising methods can suppress the Gaussian noise effectively, but fail to maintain the quality of denoised images and may blur edges in an image. To address these short comings, this paper aims to develop an optimized adaptive thresholding function based framework for edge preserved satellite image denoising using different nature inspired algorithms which is capable of effectively removing the Gaussian noise from images without over smoothing edge details. Image denoising using adaptive thresholding functions selects the suitable threshold values to separate noise from the actual image without affecting the actual features of the image. In this approach, most widely used nature inspired optimization algorithms are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the proposed adaptive differential evolution algorithm (JADE) algorithm based denoising approach has superior features and give better performance in terms of PSNR, MSE, SSIM and FSIM as compared to other methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Image denoising DWT optimization algorithms Adaptive thresholding function Adaptive differential evolution algorithm

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Multicore-Aware Virtual Machine Placement in Cloud Data Centers

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IEEE TRANSACTIONS ON COMPUTERS 2016年第11期65卷 3357-3369页

作者： Mann, Zoltan Adam Budapest Univ Technol & Econ Dept Comp Sci & Informat Theory Budapest Hungary

Finding the best way to map virtual machines (VMs) to physical machines (PMs) in a cloud data center is an important optimization problem, with significant impact on costs, performance, and energy consumption. In most situations, the computational capacity of PMs and the computational load of VMs are a vital aspect to consider in the VM-to-PM mapping. Previous work modeled computational capacity and load as one-dimensional quantities. However, today's PMs have multiple processor cores, all of which can be shared by cores of multiple multicore VMs, leading to complex scheduling issues within a single PM, which the one-dimensional problem formulation cannot capture. In this paper, we argue that at least a simplified model of these scheduling issues should be taken into account during VM placement. We show how constraint programming techniques can be used to solve this problem, leading to significant improvement over non-multicore-aware VM placement. Several ways are presented to hybridize an exact constraint solver with common packing heuristics to derive an effective and scalable algorithm.

关键词： VM placement VM consolidation cloud computing data center optimization algorithms constraint programming

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Optimal Wideband LPDA Design for Efficient Multimedia Content Delivery Over Emerging Mobile Computing Systems

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IEEE SYSTEMS JOURNAL 2016年第2期10卷 831-838页

作者： Zaharis, Zaharias D. Skeberis, Christos Lazaridis, Pavlos I. Xenos, Thomas D. Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece Univ Huddersfield Dept Engn & Technol Huddersfield HD1 3DH W Yorkshire England

An optimal synthesis of a wideband log-periodic dipole array (LPDA) is introduced in this paper. The LPDA optimization is performed under several requirements concerning the standing wave ratio, the forward gain, the gain flatness, the front-to-back ratio, and the sidelobe level, over a wide frequency range. The LPDA geometry that complies with the aforementioned requirements is suitable for efficient multimedia content delivery. The optimization process is accomplished by applying a recently introduced method called invasive weed optimization (IWO). The method has already been compared to other evolutionary methods and has shown superiority in solving complex nonlinear problems in telecommunications and electromagnetics. In this paper, the IWO method has been chosen to optimize an LPDA for operation in the frequency range of 800-3300 MHz. Due to its excellent performance, the LPDA can effectively be used for multimedia content reception over future mobile computing systems.

关键词： Invasive weed optimization (IWO) log-periodic dipole array (LPDA) design optimization algorithms

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A prediction model for the natural gas hydrate formation pressure into transmission line

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PETROLEUM SCIENCE AND TECHNOLOGY 2016年第9期34卷 824-831页

作者： Abbasi, Aijaz Hashim, Fakhruldin Mohd Univ Teknol PETRONAS Dept Mech Engn Seri Iskandar Malaysia

Gas hydrate is a major challenge in deepwater hydrocarbon transmission lines. It can lead to safety hazards in production and flow assurance in the transportation system of hydrocarbons. The authors propose a mathematical prediction model for hydrate formation pressure conditions based on exponential function and intelligent optimization. The intelligent optimization approach namely genetic algorithm (GA), particle swarm optimization (PSO) and grey wolf optimizer (GWO) were used to search the best value of coefficients that give a minimum error in prediction of pressure conditions during hydrate formation in the deepwater pipeline. The proposed approach was tested on the four experimental data of with and without inhibitor and nitrogen in the mixture of gases. The proposed approach of hydrate formation pressure conditions prediction model will be helpful in finding hydrate formation pressure in deepwater methane gas pipeline.

关键词： GA GWO hydrate formation pressure optimization algorithms prediction model for hydrate formation pressure PSO

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PCA-ICA Integrated with Bayesian Method for Non-Gaussian Fault Diagnosis

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2016年第17期55卷 4979-4986页

作者： Jiang, Qingchao Yan, Xuefeng Li, Juan E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Qingdao Agr Univ Coll Mech & Elect Engn Qingdao 266109 Peoples R China

Recent work has demonstrated the effectiveness of the principal component analysis (PCA)-independent component analysis (ICA) method for non-Gaussian process monitoring;however, the focus is on fault detection and isolation. The fault diagnosis issue has not been sufficiently investigated. This paper aims to introduce a PCA-ICA integrated with a Bayesian fault diagnosis method for non-Gaussian processes. First, PCA is employed to project the source signals into the dominant subspace. Second, ICA is employed to extract the independent components from the PCA dominant subspace. Then fault signature evidence is generated, and a Bayesian fault diagnosis system is established to identify the process status. Considering the significant amount of calculation in Bayesian diagnosis, a subset of optimal evidence sources are selected via a stochastic optimization algorithm. The efficiency and feasibility of the proposed method are exemplified by a numerical example and, the Tennessee Eastman benchmark process.

关键词： optimization algorithms Defect detection Subspace Gaussian processes component analysis Bayesian inference Fault diagnosis Bayes methods independent components Non-Gaussian process state numerical example

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New Real-Time Heuristics for Electrical Load Rebalancing in Aircraft

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2016年第3期52卷 1120-1131页

作者： Teroerde, Michael Schulz, Detlef Helmut Schmidt Univ Elect Power Syst Holstenhofweg 85 D-22043 Hamburg Germany

The concept of more electric aircraft leads to increases in the amount of electrical loads, as well as the power consumed in the aircraft of the future. To utilize the power feeders, more symmetric, load balancing methods can be applied to swap loads between different phases of an alternating current (AC) feeder, or even between different power feeders. If the load balancing system reacts to measurement data during the flight in real time, the cable power losses and return network power losses, too, are reduced. In addition, the rate of power management interventions decreases. The load balancing problem in three-phase systems is a mixed-integer nonlinear nondifferentiable optimization problem, which is typically solved by elaborate and time-consuming nonreal-time optimization algorithms. The AC loads in aircraft have different power factors, which result in currents described by complex numbers. To determine a load swapping scheme in real time starting from a given load allocation with sparse swapping, new heuristics are presented. One heuristic is specially designed to solve the phase swapping problem by shifting single-phase loads between phases of a feeder. Another heuristic, based on the first one, is enhanced to more than one three-phase feeder and considers the swapping of single-phase and three-phase loads. The heuristics are tested by simulations of a comprehensive case study based on real measurement data from a modern passenger aircraft. To prove the efficiency of the new concepts, a test bench has been built, and several experiments successfully conducted.

关键词： Swap Passenger aircraft LOAD optimization algorithms Heuristics Electric loads Reactive power power loss Aircraft measurement data load allocation Balance impairment Feeders ALTERNATING CURRENT three-phase system

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Optimal building envelope design based on simulated performance: History, current status and new potentials

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ENERGY AND BUILDINGS 2016年 117卷 387-398页

作者： Huang, Yu Niu, Jian-lei Hong Kong Polytech Univ Fac Construct & Environm Dept Bldg Serv Engn Kowloon Hong Kong Peoples R China

Green building design is presently among the hottest research topics in the world. Maintaining a comfortable indoor environment with minimum energy consumption is a challenging task that attracts the attention of experts around the world. With the recent advances in building performance simulation tools, it is now possible to predict and assess building performance at the design stage. Simulation-based optimization of building design is a potential application that connects building performance simulation with optimization algorithms. In this paper, numerous studies on the optimization of building envelope design were assembled and reviewed. Popular optimization algorithms were compared and discussed. Targeted objectives were collected and summarized. Based on the statistical results, the limitations in this research area were identified, and some potential breakthroughs were suggested. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Design optimization Building performance simulation Building envelope optimization algorithms optimization objectives

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Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method

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NEUROCOMPUTING 2016年第PartB期174卷 1087-1100页

作者： Chen, Jiaoliao Yang, Jiangxin Zhao, Jiangwu Xu, Fan Shen, Zheng Zhang, Libin Zhejiang Univ State Key Lab Fluid Power Transmiss & Control Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Technol Minist Educ Key Lab E&M Hangzhou 310014 Zhejiang Peoples R China Tongji Univ Inst Modern Agr Sci & Engn Shanghai 200092 Peoples R China

Energy demand forecasting is able to improve the energy efficiency and energy savings of the agricultural greenhouses. A model optimized prediction (MOP) methodology is proposed to predict the energy demand of greenhouses with a better performance of accuracy and cost time. The physical model of greenhouses energy demand is built up based on the energy and mass balance. According to the sensitivity analysis of the Sobol' method, the uncertain parameters of greenhouse energy model are sort by the first-order and total order indices. The uncertain parameters greatly affecting the model prediction can be collected from indistinct internal parameters for calibration to save computation time. Adaptive particle swarm optimization and genetic algorithms (APSO-GA) is utilized to calibrate the uncertain parameters of energy model by using the measured data in an experimental greenhouse with surface water source heat pumps system. To speed up the convergence, adaptive operator adjusts the proportion of particles for PSO and GA and changes the weight of the adjust factor during the optimization process. Compared with GA, PSO and conventional PSO-GA, APSO-GA can improve the optimization performance with more accurate of 3.2% and save the optimization time of more than 15.4%. Predicted energy demand by the optimized model is in agreement with measured energy demand with a better accuracy of a 95.6% significant level, which proves that the MOP methodology is reliable to predict energy demand and peak load of greenhouses. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Energy demand Greenhouses Sensitivity analysis Model optimized prediction optimization algorithms

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A Homogeneous and Self-Dual Interior-Point Linear Programming Algorithm for Economic Model Predictive Control

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2016年第8期61卷 2226-2231页

作者： Sokoler, Leo Emil Frison, Gianluca Skajaa, Anders Halvgaard, Rasmus Jorgensen, John Bagterp DTU Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark DONG Energy DK-2830 Virum Denmark

We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear system of equations solved in homogeneous and self-dual IPMs. Fast convergence is further achieved using a warm-start strategy. We implement the algorithm in MATLAB and C. Its performance is tested using a conceptual power management case study. Closed loop simulations show that: 1) the proposed algorithm is significantly faster than several state-of-the-art IPMs based on sparse linear algebra and 2) warm-start reduces the average number of iterations by 35%-40%.

关键词： Energy systems Linear programming algorithms optimization algorithms predictive control for linear systems Riccati iterations

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