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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Tutkun, N. Gegin, K. Sarma, N. Salam, Z. Duzce Univ Fac Engn Dept Elect & Elect Engn Duzce Turkey Univ Teknol Malaysia Fac Elect Engn Ctr Elect Energy Syst Skudai Malaysia

ISBN: (纸本)9781538656860

In PV systems, it is usually expected that power generation under varying conditions at any time should be maximum as much as possible. Therefore, it is essential to build a circuit model based on a widely used single or double diode model under instantly varying conditions with minimum estimation error through the measured values in the manufacturers' data sheet. It is fact that these approximate circuits models ease the performance analysis of PV systems if the unknown parameters are accurately estimated at various temperatures and unchanged irradiance levels or vice versa. This paper comparatively studies the performances of the two approximate circuit models for the four types of PV modules using the real-coded genetic algorithms. The results indicated that I-V characteristics obtained from the identified parameters exhibit almost similar trend with those given in manufacturers' data sheets under the same conditions. Besides, the estimated parameters obtained from using both models slightly show difference from each other and it can be said that the single diode model is solely satisfactory to model a typical PV model as long as they are accurately estimated.

关键词： double diode circuit model genetic algorithm optimization PV-Battery system single diode circuit model

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Design of a PV Powered Charging Station for PHEVs 6

Design of a PV Powered Charging Station for PHEVs

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6th International Renewable and Sustainable Energy Conference (IRSEC)

作者： Tutkun, N. Afandi, A. N. Istanbul Aydin Univ Fac Engn Dept Elect & Elect Engn Istanbul Turkey Univ Negeri Malang Elect Engn Dept Malang Indonesia

ISBN: (纸本)9781728111827

Recently the plug-in hybrid electric vehicles (PHEVs) have increasingly been used for transportation due to less petrol consumption and low carbon emission. The wellknown disadvantages of this vehicle are charging time and mile range compared to gas-powered vehicles. Moreover, charging a vast number of vehicles directly from the grid may cause damage to the local grid due to due to high current draws. Thus off-grid sources may be employed to share amount of power for charging PHEVs. This paper focuses on the design of a grid connected 30 kW photovoltaic powered PHEV charging station with optional battery storage units it is indispensable to design such system with low cost operation with respect to grid utility price. This is achieved by optimizing charging time of PHEVs through demand side management strategies such as load shifting, valley filling etc. using the real-coded genetic algorithms.

关键词： plug-in hybrid electric vehicles genetic algorithm optimization PV charging station renewable sources

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Wind Tower Vibration Controlled by a Pendulum TMD using genetic optimization: Beam Modelling 10

Wind Tower Vibration Controlled by a Pendulum TMD using Gene...

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10th International Conference on Structural Dynamics (EURODYN)

作者： Colherinhas, Gino B. Shzu, Maura A. M. Avila, Suzana M. de Morais, Marcus V. G. Univ Brasilia Campus Univ Darcy Ribeiro Brasilia DF Brazil Univ Brasilia Campus UnB Gama Gama DF Brazil

This paper models a wind turbine tower and passive pendulum TMD with finite element using the resources and capabilities of commercial software ANSYS. Although structural control of high slender towers using pendulum TMDs is widely studied in literature, it was not found yet studies modelling the pendulum TMD with ANSYS. Using a genetic algorithms (GA), this work optimize the parameters of pendulum TMD (stiffness, damping, length and mass ratio) installed on the top of a wind turbine tower. The aim of this paper is to evaluate the differences between the responses obtained through finite element and analytical 2 degree of freedom (2DOF) models. Also it is investigated the influence of the model adopted on TMD parameters optimization results. It was verified a little difference between the frequency response functions obtained through beam and analytical 2DOF models, using the same TMD parameters in both cases. (c) 2017 The Authors. Published by Elsevier Ltd.

关键词： Wind Turbine Tuned Mass Damper genetic algorithm optimization Structural Control

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DESIGN OF A MODULAR, AXIAL-FLUX DIRECT DRIVE PERMANENT MAGNET GENERATOR FOR WIND TURBINES

DESIGN OF A MODULAR, AXIAL-FLUX DIRECT DRIVE PERMANENT MAGNE...

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作者： AYDIN BASKAYA MIDDLE EAST TECHNICAL UNIVERSITY

学位级别：硕士

Wind energy technology is becoming more important issue in terms of renewable energy applications. Theoretical maximum of wind energy utilization is known (which is predetermined by Betz as 59%) and generally imperfections in blade manufacture reduce the actual energy yield of the turbine less than the useable energy. Therefore, maximum energy yield from generator topology is desired. According to fault statistics of wind turbines, most of the cases are related to gear- box failures. Mechanical losses and heat losses are again result from these gear-box drive train systems. In this thesis, axial flux permanent magnet direct drive (AFPM DD) topology is investigated because of its high energy yield, lower maintenance periods due to its modular direct-drive concept and axial length advantages. Proposed generator has an electrical output power of 5 MW at 12 rpm. Outer stator axial flux concept will be used in the air-cored generator. genetic algorithm (GA) optimization is used in determining analytical parameters of the design. In this thesis, cost based optimization procedure is carried under 9 different wind speed conditions in order to get a more realistic design. Wind speed data are taken from real field based measurement values of a sample wind power plant (WPP) located in Canakkale, Turkey. The algorithm calculated the design parameters of the proposed AFPM generator based on a power generation reference of a commercial 5 MW Permanent Magnet Synchronous Generator (PMSG) wind turbine under aforementioned wind conditions. In addition, the algorithm also considers the wind speed time probabilistic and related generation incomes. In order to verify the validity of analytical design method, first a sample 50 kW AFPM design is simulated by using FEA (Finite Element Analysis) and then critical parameters such as air gap flux density, and induced emf values are compared with the analytical results. Comparison of the results of the designed generator supported the proposed

关键词： Wind energy conversion axial flux permanent magnet generator direct drive modular generator genetic algorithm optimization finite element analysis (FEA)

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Study on Power Loss Minimization for Distribution Network Reconfiguration Using genetic algorithm Case Study: Addis North 132/15 KV Substation

Study on Power Loss Minimization for Distribution Network Re...

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作者： Habtamu, Eshete Addis Ababa University

学位级别：硕士

Distribution system is a largest portion of network of electrical power system. Different actions have been taken to improve the efficiency of the distribution network. One of the most basic and the commonest way to improve the performance of the distribution network is the network reconfiguration. Electric distribution systems reconfiguration comprises tie and sectionalizing switches. Tie switches are normally open, and sectionalizing switches are normally closed. By opening and closing these switches, the distribution network can be reconfigured. This reconfiguration can be done for the objective of loss minimization. In order to get feasible results (loss minimization), the reconfiguration must meet some constraints, like Kirchhoff‟s voltage and current laws, other equality and inequality constraints. Distribution network reconfiguration is an optimization problem and needs a suitable algorithm (method).The method used for this optimization problem is a genetic algorithm optimization method. The genetic algorithm has been described in detail and then applied specifically to the network reconfiguration problem. In the optimization process, load flow of the distribution system was computed. Then, computer simulation was performed by using DIgSILENT PowerFactory software for analyzing the distribution network reconfiguration. In addition, optimized genetic algorithm was used as a tool for network reconfiguration. Addis North 132/15 kV substation feeders are used as test system for this particular study. Before the reconfiguration, the power loss is 3.983783 MW, after reconfiguration the power loss has decreased from 3.983783 MW to 1.594640 MW, which is 2.389143 MW (59.91716%) reduction. Besides, the maximum voltage drop before reconfiguration is 0.352639, and the reconfiguration increased it to 5.050102. The minimum voltage before reconfiguration is 0.992618 p.u., and the minimum voltage is found to be 0.947288 p.u. after reconfiguration. In addition, the maximum vo

关键词： Network Reconfiguration genetic algorithm optimization Power Loss Minimization

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Optimal control of high harmonics for the generation of double attosecond pulses by using a chirped laser pulse

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JOURNAL OF MODERN OPTICS 2017年第20期64卷 2117-2122页

作者： Navid, H. A. Keshavarzi, M. Univ Bonab Dept Laser & Opt Engn Bonab Iran

An efficient scheme for the optimization of ultrashort femtosecond pulse shapes interacting with an atom to control high harmonics spectrum and double attosecond pulse generation is presented. The time-dependent Schrodinger equation of one-dimensional hydrogen atom is solved numerically to obtain electric field emission. The genetic algorithm optimization method is used to control the phase and amplitude of ultrashort excitation laser pulses to generate the desired attosecond-shaped pulses. An appropriate cost function is introduced for genetic algorithm optimization of double attosecond pulse generation. It is shown that the relative intensity of two generated pulses, their delay time and duration can be controlled in this approach. Finally, the parameters of the optimized emitted attosecond pulse are compared with those of desired pulses, and the underlying physical mechanisms are discussed in detail.

关键词： Chirped pulse double attosecond pulses high harmonic generation optimal control theory genetic algorithm optimization

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A new approach of GA-based type reduction of interval type-2 fuzzy model for nonlinear MIMO system: Application in methane oxidation process

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2017年 167卷 152-160页

作者： Tayyebi, Shokoufe Soltanali, Saeed Res Inst Petr Ind POB 14665-137 Tehran Iran

The aim of this paper is the application of a type-2 fuzzy model to a nonlinear MIMO system. In the suggested approach, genetic algorithm is employed to find the optimal type reduction of the interval type-2 fuzzy model for model prediction. To verify its performance, the proposed method is used in prediction of the methane conversion and the formic acid selectivity of the methane oxidation process with H2O2. In this process, the input variables are temperature, pressure, time on stream, catalyst content, H2O2 concentration and stirring speed. Interval type-2 fuzzy logic model has designed with reduced rules number compared with the type-1 fuzzy model. The results confirm that the proposed method based on type reduction interval type-2 fuzzy model outperforms the other strategies with an R-2 value of greater than 0.988.

关键词： Interval type-2 fuzzy logic genetic algorithm optimization Type reduction Modeling Methane oxidation

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Predictive modelling and optimization of carbon fiber mechanical properties through high temperature furnace

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APPLIED THERMAL ENGINEERING 2017年 125卷 1539-1554页

作者： Khayyam, Hamid Fakhrhoseini, Seyed Mousa Church, Jeffrey S. Milani, Abbas S. Bab-Hadiashar, Alireza Jazar, Reza N. Naebe, Minoo RMIT Univ Sch Engn Melbourne Vic 3000 Australia Deakin Univ Inst Frontier Mat Carbon Nexus Geelong Vic 3216 Australia Commonwealth Sci & Ind Res Org Mfg Waurn Ponds Vic 3216 Australia Univ British Columbia Sch Engn Composites Res Network Vancouver BC V1V 1V7 Canada

Developments in carbon fiber mechanical properties are directly dependent on its manufacturing process, especially the thermal process of carbonization through a High Temperature (HT) furnace. These mechanical properties cannot be straightforwardly predicted experimentally since the thermal process involves complex parameters such as: (i) zones temperature, (ii) applied fiber tension, (iii) the N2 gas flow rate which is influenced by uncertain disturbances. The aim of this paper is to optimize the carbon fiber modulus based on the given range of tensile strength using a predictive modelling technique. The data collection of the carbonization thermal process is significantly costly and time consuming, hence we used a novel DOE method to minimize the number of required experiments. The carbon fiber with the highest and lowest tensile and modulus properties were initially analyzed by X-ray diffraction and Raman Spectroscopy, but no significant differences were observed that could account for the differences in measured mechanical and physical properties, thus a surrogate modelling approach was adopted. Four different mathematical methods were employed for modelling and compared with one another. These are (i) non-linear multivariable regression, (ii) Levenberg-Marquardt neural network algorithm, (iii) thin plate splines, and (iv) a convex hull technique. The results clearly showed that the convex hull technique has less error compared to the other tested methods. This method was selected to optimize the carbon fiber modulus based on the given range of tensile strength. Using the convex hull technique along with an evolutionary search (genetic algorithm) method, it is shown that the modulus of carbon fiber can be optimized for any given range of tensile strength, providing an optimum set of control parameters are chosen during the process. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： HT furnace Thermal process genetic algorithm optimization Convex hulls

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Wind Tower Vibration Controlled by a Pendulum TMD using genetic optimization: Beam Modelling

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Procedia Engineering 2017年 199卷 1623-1628页

作者： Gino B. Colherinhas Maura A.M. Shzu Suzana M. Avila Marcus. V.G. de Morais Universidade de Brasília Campus Universitário Darcy Ribeiro Brasília Distrito Federal Brazil Universidade de Brasília Campus UnB Gama Gama Distrito Federal Brazil

关键词： Wind Turbine Tuned Mass Damper genetic algorithm optimization Structural Control

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Characteristics optimization of the Maglev Train Hybrid Suspension System Using genetic algorithm

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2015年第3期30卷 1163-1170页

作者： Safaei, Farhad Suratgar, Amir Abolfazl Afshar, Ahmad Mirsalim, Mojtaba Amirkabir Univ Technol Dept Elect Engn Tehran 15914 Iran St Marys Univ Dept Engn San Antonio TX 78228 USA

This paper focuses on the optimal structural design of a hybrid permanent-magnet-electro-magnetic suspension system (PEMS) for a magnetic levitation (Maglev) transportation system in order to decrease the suspension power loss. First, the nonlinear magnetic force expression of a PEMS system is obtained by developing the magnetic equivalent circuit of the hybrid structure. The proposed analytical framework accounts for leakage fluxes and material properties such as iron reluctances. A number of design considerations are also presented to attain more practical results. genetic algorithm is then employed to optimize the lifting force while reducing the system power loss. Moreover, 3-D finite element method (FEM) is utilized in the analyses and it is shown that the results calculated from the proposed model match well with those obtained from FEM. In addition, superiorities of the implemented model over the existing approaches are demonstrated. The outcomes show that the proposed method has increased the magnetic force, while significantly reducing the suspension power loss compared with those in the conventional pure electromagnet structure and in a previously proposed hybrid structure.

关键词： Finite-element-method genetic algorithm optimization hybrid magnetic levitation Maglev train modeling permanent-magnet (PM) permanent-magnet-electro-magnetic

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