Design methodology for multi-pumped discrete Raman amplifiers: case-study employing photonic crystal fibers

This paper proposes a new design methodology for discrete multi-pumped Raman amplifier. In a multi-objective optimization scenario, in a first step the whole solution-space is inspected by a CW analytical formulation. Then, the most promising solutions are fully investigated by a rigorous numerical treatment and the Raman amplification performance is thus determined by the combination of analytical and numerical approaches. As an application of our methodology we designed an photonic crystal fiber Raman amplifier configuration which provides low ripple, high gain, clear eye opening and a low power penalty. The amplifier configuration also enables to fully compensate the dispersion introduced by a 70-km singlemode fiber in a 10 Gbit/s system. We have successfully obtained a configuration with 8.5 dB average gain over the C-band and 0.71 dB ripple with almost zero eye-penalty using only two pump lasers with relatively low pump power. (C) 2009 Optical Society of America

A hybrid probabilistic criterion for market-based transmission expansion planning

Market-based transmission expansion planning gives information to investors on where is the most cost efficient place to invest and brings benefits to those who invest in this grid. However, both market issue and power system adequacy problems are system planers’ concern. In this paper, a hybrid probabilistic criterion of Expected Economical Loss (EEL) is proposed as an index to evaluate the systems’ overall expected economical losses during system operation in a competitive market. It stands on both investors’ and planner’s point of view and will further improves the traditional reliability cost. By applying EEL, it is possible for system planners to obtain a clear idea regarding the transmission network’s bottleneck and the amount of losses arises from this weak point. Sequentially, it enables planners to assess the worth of providing reliable services. Also, the EEL will contain valuable information for moneymen to undertake their investment. This index could truly reflect the random behaviors of power systems and uncertainties from electricity market. The performance of the EEL index is enhanced by applying Normalized Coefficient of Probability (NCP), so it can be utilized in large real power systems. A numerical example is carried out on IEEE Reliability Test System (RTS), which will show how the EEL can predict the current system bottleneck under future operational conditions and how to use EEL as one of planning objectives to determine future optimal plans. A well-known simulation method, Monte Carlo simulation, is employed to achieve the probabilistic characteristic of electricity market and Genetic Algorithms (GAs) is used as a multi-objective optimization tool.

A hybrid planning method for transmission networks in a deregulated environment

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented.

Unit Commitment in a Competitive and Emission Constrained Environment

This paper is on the unit commitment problem, considering not only the economic perspective, but also the environmental perspective. We propose a bi-objective approach to handle the problem with conflicting profit and emission objectives. Numerical results based on the standard IEEE 30-bus test system illustrate the proficiency of the proposed approach.

Optimal design for active damping in sandwich structures using the Direct MultiSearch method

This paper addresses the problem of optimal positioning of surface bonded piezoelectric patches in sandwich plates with viscoelastic core and laminated face layers. The objective is to maximize a set of modal loss factors for a given frequency range using multiobjective topology optimization. Active damping is introduced through co-located negative velocity feedback control. The multiobjective topology optimization problem is solved using the Direct MultiSearch Method. An application to a simply supported sandwich plate is presented with results for the maximization of the first six modal loss factors. The influence of the finite element mesh is analyzed and the results are, to some extent, compared with those obtained using alternative single objective optimization. (C) 2013 Elsevier Ltd. All rights reserved.

Optimal design for active damping in sandwich structures using the Direct MultiSearch method

This paper addresses the problem of optimal positioning of surface bonded piezoelectric patches in sandwich plates with viscoelastic core and laminated face layers. The objective is to maximize a set of modal loss factors for a given frequency range using multiobjective topology optimization. Active damping is introduced through co-located negative velocity feedback control. The multiobjective topology optimization problem is solved using the Direct MultiSearch Method. An application to a simply supported sandwich plate is presented with results for the maximization of the first six modal loss factors. The influence of the finite element mesh is analyzed and the results are, to some extent, compared with those obtained using alternative single objective optimization.

Manipulator trajectory planning using a MOEA

Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.

Evaluation of the Electric Vehicle Impact in the Power Demand Curve in a Smart Grid Environment

Smart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.

Toroid inductor development for a SIC DC-DC converter up to 150kW, based on finite element method

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Use of field measurements in fatigue analysis of forestry cranes

Tässä työssä on esitetty väsyttävän kuormituksen mittaamiseen ja mittausdatan jälkikäsittelyyn sekä väsymismitoitukseen liittyviä menetelmiä. Menetelmien sovelluskohteena oli metsäkoneen kuormain, joka on väsyttävästi kuormitettu hitsattu rakenne. Teoriaosassa on kuvattu väsymisilmiötä ja väsymismitoitusmenetelmiä sekä kuormitusten tunnistamiseen ja mittausten jälkikäsittelyyn liittyviä menetelmiä. Yleisimmin käytettyjen väsymismitoitusmenetelmien rinnalle on esitetty luotettavuuteen perustuvaa väsymismitoitusmenetelmää. Kuormainten suunnittelussa on keveys- j a kestoikävaatimusten takia erityisen suuri merkitys väsymisen huomioimisella. Rakenteille on ominaista tietyt toiminnan kannalta välttämättömät hitsatut yksityiskohdat, jotka usein määräävät koko rakenteen kestoiän. Koska nämä ongelmakohdat pystytään useimmiten tunnistamaan jo suunnitteluvaiheessa, voidaan yksityiskohtien muotoilulla usein parantaa huomattavasti koko rakenteen kestoikää. Näiden yksityiskohtien optimointi on osittain mahdollista toteuttaa ilman kuormituskertymätietoa, mutta useimmiten kuormitusten tunnistaminen on edellytys parhaan ratkaisun löytymiselle. Tällöin toistaiseksi paras keino todellisen väsyttävän kuormituksen tunnistamiseksi on pitkäaikaiset kenttämittaukset. Kenttämittauksilla selvitetään rakenteeseen kohdistuvat kuormitukset venymäliuskojen avulla. Kuormitusten tunnistamisella on erityisen suuri merkitys kun halutaan määrittää rakenteen kestoikä. Väsyminen ja väsyttävä kuormitus ovat kuitenkin tilastollisia muuttujia j a yksittäiselle rakenteelle ei ole mahdollista määrittää tarkkaa k estoikää. Tilastollisia menetelmiä käyttäen on kuitenkin mahdollista määrittää rakenteen vaurioitumisriski. Laskettaessa vaurioitumisriskiä suurelle määrälle yksittäisiä rakenteita voidaan muodostaa tarkkojakin ennusteita mahdollisten vaurioiden lukumäärästä. Tällöin kuormituskertymätiedosta voi olla tavanomaisen suunnittelun lisäksi laajempaa hyötyä esimerkiksi takuukäsittelyssä. Tässä työssä on sovellettu esitettyjä teorioita käytännössä metsäkoneen harvesterin puomiston väsymistarkasteluun. Kyseisen rakenteen kuormituksia mitattiin kahden viikon aikana yhteensä 35 tuntia, jonka perusteella laskettiin väsyttävän kuormituksen tilastollinen jakauma esimerkkitapaukselle. Mittauksen perusteella ei voitu tehdä kuitenkaan johtopäätöksiä tuotteen koko elinkaaren kuormituksista eikä muiden samanlaisten tuotteiden kuormituksista, koska mitattu otos oli suhteellisen lyhyt ja rajoittui vain yhteen käyttäjään ja muutamaan käyttökohteeseen. Menetelmien testaamiseksi kyseinen otos oli kuitenkin riittävä. Kuormituskertymätietoa käytettiin hyväksi myös laatumääritysten muodostamisessaesimerkkitapaukselle. Murtumismekaniikkaan perustuvalla menetelmällä arvioitiinharvesteripilarin valun mahdollisten valuvirheiden suurin sallittu koko. Luotettavuuteen pohjautuvan mitoitusmenettelyn tarve näyttää olevanlisääntymässä, joten pitkäaikaisten kenttämittausten tehokas hyödyntäminen tulee olemaan keskeinen osa väsymismitoitusta lähitulevaisuudessa. Menetelmiä olisi mahdollista tehostaa yhdistämällä kuormituskertymään erilaisia kuormitusten suhteen riippuvia tunnettuja suureita kuten käsiteltävän puun halkaisija. Todellisettuotekohtaiset tilastolliset jakaumat kuormituksista voitaisiin muodostaa mahdollisesti tehokkaammin, jos esimerkiksi kuormitusten riippuvuus metsätyypistä pystyttäisiin ensin määrittämään.

Tietoverkon varmistusten simulointi ja optimointi

Tässä diplomityössä määritellään varmistusjärjestelmän simulointimalli eli varmistusmalli. Varmistusjärjestelmän toiminta optimoidaan kyseisen varmistusmallin avulla. Optimoinnin tavoitteena on parantaa varmistusjärjestelmän tehokkuutta. Parannusta etsitään olemassa olevien varmistusjärjestelmän resurssien maksimaalisella hyödyntämisellä. Varmistusmalli optimoidaan evoluutioalgoritmin avulla. Optimoinnissa on useita tavoitteita, jotka ovat ristiriidassa keskenään. Monitavoiteoptimointiongelma muunnetaan yhden tavoitteen optimointiongelmaksi muodostamalla tavoitefunktio painotetun summan menetelmän avulla. Rinnakkain edellisen menetelmän kanssa käytetään myös Pareto-optimointia. Pareto-optimaalisen rintaman pisteiden etsintä ohjataan lähelle painotetun summan menetelmän optimipistettä. Evoluutioalgoritmin toteutuksessa käytetään hyväksi varmistusjärjestelmiin liittyvää ongelmakohtaista tietoa. Työn tuloksena saadaan varmistusjärjestelmän simulointi- sekä optimointityökalu. Simulointityökalua käytetään kartoittamaan nykyisen varmistusjärjestelmän toimivuutta. Optimoinnin avulla tehostetaan varmistusjärjestelmän toimintaa. Työkalua voidaan käyttää myös uusien varmistusjärjestelmien suunnittelussa sekä nykyisten varmistusjärjestelmien laajentamisessa.

Valuation models and Simon's bounded rationality

This paper aims at reconciling the evidence that sophisticated valuation models are increasingly used by companies in their investment appraisal with the literature of bounded rationality, according to which objective optimization is impracticable in the real world because it would demand an immense level of sophistication of the analytical and computational processes of human beings. We show how normative valuation models should rather be viewed as forms of reality representation, frameworks according to which the real world is perceived, fragmented for a better understanding, and recomposed, providing an orderly method for undertaking a task as complex as the investment decision.

Inverse Illumination Design with Genetic Programming

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

A hybridised evolutionary algorithm for multi-criterion minimum spanning tree problems

A hybridised and Knowledge-based Evolutionary Algorithm (KEA) is applied to the multi-criterion minimum spanning tree problems. Hybridisation is used across its three phases. In the first phase a deterministic single objective optimization algorithm finds the extreme points of the Pareto front. In the second phase a K-best approach finds the first neighbours of the extreme points, which serve as an elitist parent population to an evolutionary algorithm in the third phase. A knowledge-based mutation operator is applied in each generation to reproduce individuals that are at least as good as the unique parent. The advantages of KEA over previous algorithms include its speed (making it applicable to large real-world problems), its scalability to more than two criteria, and its ability to find both the supported and unsupported optimal solutions.

A robust controller design method for feedback substitution schemes using genetic algorithms

Controllers for feedback substitution schemes demonstrate a trade-off between noise power gain and normalized response time. Using as an example the design of a controller for a radiometric transduction process subjected to arbitrary noise power gain and robustness constraints, a Pareto-front of optimal controller solutions fulfilling a range of time-domain design objectives can be derived. In this work, we consider designs using a loop shaping design procedure (LSDP). The approach uses linear matrix inequalities to specify a range of objectives and a genetic algorithm (GA) to perform a multi-objective optimization for the controller weights (MOGA). A clonal selection algorithm is used to further provide a directed search of the GA towards the Pareto front. We demonstrate that with the proposed methodology, it is possible to design higher order controllers with superior performance in terms of response time, noise power gain and robustness.