252 research outputs found

    Modeling and simulation of magnetic components in electric circuits

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    This thesis demonstrates how by using a variety of model constructions and parameter extraction techniques, a range of magnetic component models can be developed for a wide range of application areas, with different levels of accuracy appropriate for the simulation required. Novel parameter extraction and model optimization methods are developed, including the innovative use of Genetic Algorithms and Metrics, to ensure the accuracy of the material models used. Multiple domain modeling, including the magnetic, thermal and magnetic aspects are applied in integrated simulations to ensure correct and complete dynamic behaviour under a range of environmental conditions. Improvements to the original Jiles-Atherton theory to more accurately model loop closure and dynamic thermal behaviour are proposed, developed and tested against measured results. Magnetic Component modeling techniques are reviewed and applied in practical examples to evaluate the effectiveness of lumped models, 1D and 2D Finite Element Analysis models and coupling Finite Element Analysis with Circuit Simulation. An original approach, linking SPICE with a Finite Element Analysis solver is presented and evaluated. Practical test cases illustrate the effectiveness of the models used in a variety of contexts. A Passive Fault Current Limiter (FCL) was investigated using a saturable inductor with a magnet offset, and the comparison between measured and simulated results allows accurate prediction of the behaviour of the device. A series of broadband hybrid transformers for ADSL were built, tested, modeled and simulated. Results show clearly how the Total Harmonic Distortion (THD), Inter Modulation Distortion (IMD) and Insertion Loss (IL) can be accurately predicted using simulation.A new implementation of ADSL transformers using a planar magnetic structure is presented, with results presented that compare favourably with current wire wound techniques. The inclusion of transformer models in complete ADSL hybrid simulations demonstrate the effectiveness of the models in the context of a complete electrical system in predicting the overall circuit performance

    Thermal modeling and evaluation of harmonic effects on a dry-type air-core reactor

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    For the design engineers of reactors as well as for the most economical use of that equipment, the temperatures of different parts of reactors should be precisely known so that the thermal losses can be optimized and minimized. Through conventional surface testing methods the exact locations of hot spots inside reactor coils can only be estimated by means of empirical mathematical calculations. Therefore, the ability to directly measure the temperatures inside the coils between the conductors would lead to better design of the reactors, and at the same time would show the exact locations of hottest-spot areas and temperatures. In the IEC and IEEE standards, the test methods for determining temperatures and hot spots are mainly described as surface-temperature measuring methods, since modern dry-type air-core reactors usually employ fully encapsulated windings. Therefore, direct access to the winding is not possible for the measurement of hot spot temperatures during a heat-run test. However, it is possible to measure winding surface temperatures with some degree of accuracy. Such winding surface temperature measurements are essentially a measurement of winding hot spot due to the fact that the winding encapsulation medium is thin compared to the winding conductor cross section. Since energy costs are on the increase, losses are becoming a more significant component of the total operating cost. Further, the correct current distribution between the coils causes even temperatures in each coil and helps to optimize the manufacturing and losses of the whole reactor. For this reason, the research work behind this thesis was started and a test reactor manufactured. During the manufacturing process, several fiber optic wires (instead of fiber optic probes, as mentioned in the IEEE standards) were installed in the middle and at the surface of several cylinder windings, for temperature monitoring purposes. Through these optic wires, it became possible to measure the dynamic temperature changes in several cylinders of the reactor, because the temperatures depend on the location and time. The dynamic temperature behavior could be determined in the middle of the windings for the whole length, from bottom to top. At the same time, with other optic wires, the surface temperatures could also be measured from bottom to top. For comparison, some surface temperatures as well as cooling air temperatures in the air ducts were also measured by means of thermocouples and infrared cameras. In this dissertation, the modeling methods for calculating the temperature distribution and hot-spot temperatures in large multi cylinder air-core reactors are studied and a new method is proposed for thermal loss optimization

    Development of magnetic induction machines for micro turbo machinery

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references.This thesis presents the nonlinear analysis, design, fabrication, and testing of an axial-gap magnetic induction micro machine, which is a two-phase planar motor in which the rotor is suspended above the stator via mechanical springs, or tethers. The micro motor is fabricated from thick layers of electroplated NiFe and copper, by our collaborators at Georgia Institute of Technology. The rotor and the stator cores are 4 mm in diameter each, and the entire motor is about 2 mm thick. During fabrication, SU-8 epoxy is used as a structural mold material for the electroplated cores. The tethers are designed to be compliant in the azimuthal direction, while preventing axial deflections and maintaining a constant air gap. This enables accurate measurements of deflections within the rotor plane via a computer microvision system. The small scale of the magnetic induction micro machine, in conjunction with the good thermal contact between its electroplated stator layers, ensures an isothermal device which can be cooled very effectively. Current densities over 109 A/m2 simultaneously through each phase is repeatedly achieved during experiments; this density is over two orders of magnitude larger than what can be achieved in conventional macro-scale machines. More than 5 Nm of torque is obtained for an air gap of about 5 zm, making this micro motor the highest torque density micro-scale magnetic machine to date. About 0.3 buNm for the large air gap of 70 m is also achieved in systematic tests that reveal the influence of strong eddy-currents and associated nonlinear saturation within the micro motor.(cont.) Eddy-current effects are modeled using a finite-difference vector potential formulation. Its results demonstrate the presence of flux crowding on the stator surface, which leads to heavy saturation. To capture saturation effects, a fully nonlinear finite-difference time-domain simulation is developed to solve Maxwell's Equations within the computational space of the micro machine. To mitigate the inherent stiffness in the partial differential equations, the speed of light is artificially reduced by five orders of magnitude, taking special care that assumptions of magnetoquasistatic behavior are still met. The results from this model are in very good agreement with experimental data from the tethered magnetic induction micro motor.by Hür KöÅer.Ph.D

    Reduction of conductivity uncertainty propagations in the inverse problem of EEG source analysis

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    In computer simulations, the response of a system under study depends on the input parameters. Each of these parameters can be assigned a fixed value or a range of values within the input parameter space for system performance evaluations. Starting from values of the input parameters and a certain given model, the so-called forward problem can be solved that needs to approximate the output of the system. Starting from measurements related to the output of the system model it is possible to determine the state of the system by solving the so-called inverse problem. In the case of a non-linear inverse problem, non-linear minimization techniques need to be used where the forward model is iteratively evaluated for different input parameters. The accuracy of the solution in the inverse problem is however decreased due to the noise available in the measurements and due to uncertainties in the system model. Uncertainties are parameters for which their values are not exactly known and/or that can vary in time and/or depend on the environment. These uncertainties have, for given input parameter values, an influence on the forward problem solution. This forward uncertainty propagation leads then to errors in the inverse solutions because the forward model is iteratively evaluated for recovering the inverse solutions. Until now, it was assumed that the recovery errors could not be reduced. The only option was to either quantify the uncertain parameter values as accurate as possible or to reflect the uncertainty in the inverse solutions, i.e. determination of the region in parameter space wherein the inverse solution is likely to be situated. The overall aim of this thesis was to develop reduction techniques of inverse reconstruction errors so that the inverse problem is solved in a more robust and thus accurate way. Methodologies were specifically developed for electroencephalography (EEG) source analysis. EEG is a non-invasive technique that measures on the scalp of the head, the electric potentials induced by the neuronal activity. EEG has several applications in biomedical engineering and is an important diagnostic tool in clinical neurophysiology. In epilepsy, EEG is used to map brain areas and to receive source localization information that can be used prior to surgical operation. Starting from Maxwell’s equations in their quasi-static formulation and from a physical model of the head, the forward problem predicts the measurements that would be obtained for a given configuration of current sources. The used headmodels in this thesis are multi-layered spherical head models. The neural sources are parameterized by the location and orientation of electrical dipoles. In this thesis, a set of limited number of dipole sources is used as source model leading to a well posed inverse problem. The inverse problem starts from measured EEG data and recovers the locations and orientations of the electrical dipole sources. A loss in accuracy of the recovered neural sources occurs because of noise in the EEG measurements and uncertainties in the forward model. Especially the conductivity values of scalp, skull and brain are not well known since these values are difficult to measure. Moreover, these uncertainties can vary from person to person, in time, etc. In this thesis, novel numerical methods are developed so to provide new approaches in the improvement of spatial accuracy in EEG source analysis, taking into account model uncertainties. Nowadays, the localization of the electrical activity in the brain is still a current and challenging research topic due to the many difficulties arising e.g. in the process of modeling the head and dealing with the not well known conductivity values of its different tissues. Due to uncertainty in the conductivity value of the head tissues, high values of errors are introduced when solving the EEG inverse problem. In order to improve the accuracy of the solution of the inverse problem taking into account the uncertainty of the conductivity values, a new mathematical approach in the definition of the cost function is introduced and new techniques in the iterative scheme of the inverse reconstruction are proposed. The work in this thesis concerns three important phases. In a first stage, we developed a robust methodology for the reduction of errors when reconstructing a single electrical dipole in the case of a single uncertainty. This uncertainty concerns the skull to soft tissue conductivity ratio which is an important parameter in the forward model. This conductivity ratio is difficult to quantify and depends from person to person. The forward model that we employed is a three shell spherical head model where the forward potentials depend on the conductivity ratio. We reformulated the solution of the forward problem by using a Taylor expansion around an actual value of the conductivity ratio which led to a linear model of the solution for the simulated potentials. The introduction of this expanded forward model, led to a sensitivity analysis which provided relevant information for the reconstruction of the sources in EEG source analysis. In order to develop a technique for reducing the errors in inverse solutions, some challenging mathematical questions and computational problems needed to be tackled. We proposed in this thesis the Reduced Conductivity Dependence (RCD) method where we reformulate the traditional cost function and where we incorporated some changes with respect to the iterative scheme. More specifically, in each iteration we include an internal fitting procedure and we propose selection of sensors. The fitting procedure makes it possible to have an as accurate as possible forward model while the selection procedure eliminates the sensors which have the highest sensitivity to the uncertain skull to brain conductivity ratio. Using numerical experiments we showed that errors in reconstructed electrical dipoles are reduced using the RCD methodology in the case of no noise in measurements and in the case of noise in measurements. Moreover, the procedure for the selection of electrodes was thoroughly investigated as well as the influence of the use of different EEG caps (with different number of electrodes). When using traditional reconstruction methods, the number of electrodes has not a high influence on the spatial accuracy of the reconstructed single electrical dipole. However, we showed that when using the RCD methodology the spatial accuracy can be even more increased. This because of the selection procedure that is included within the RCD methodology. In a second stage, we proposed a RCD method that can be applied for the reconstruction of a limited number of dipoles in the case of a single uncertainty. The same ideas were applied onto the Recursively Applied and Projected Multiple Signal Classification (RAP-MUSIC) algorithm. The three shell spherical head model was employed with the skull to brain conductivity ratio as single uncertainty. We showed using numerical experiments that the spatial accuracy of each reconstructed dipole is increased, i.e. reduction of the conductivity dependence of the inverse solutions. Moreover, we illustrated that the use of the RCD-based subspace correlation cost function leads to a high efficiency even for high noise levels. Finally, in a third stage, we developed a RCD methodology for the reduction of errors in the case of multiple uncertainties. We used a five shell spherical head model where conductivity ratios with respect to skull, cerebrospinal fluid, and white matter were uncertain. The cost function as well as the fitting and selection procedure of the RCD method were extended. The numerical experiments showed reductions in the reconstructed electrical dipoles in comparison with the traditional methodology and also compared to the RCD methodology developed for dealing with a single uncertainty

    Study of electromechanical effects in high field accelerator magnets

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    Future upgrades of machines like the LHC at CERN require pushing accelerator magnets beyond 10 T. Larger magnet sizes and more performing superconductors introduce additional challenges. This work improves existing analytical models of the magnetic field and stress of dipole and quadrupole sector windings, addressing how far the engineering of High Field Magnets can be pushed. Problems and limitations of Nb3Sn magnets are identified by correlating the field intensity and the loss of field quality to the magnetic and mechanical properties of the material.been reproduced by means of an elastoplastic finite element model

    Aspects of the structural Effects of Plasma Disruptions on Tokamaks

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    Measurements and finite element modelling of transformer flux with dc and power frequency current

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    Geomagnetically induced currents (GIC’s) caused by solar storms or other sources of dc excitation in the presence of ac energization can disturb the normal operation of power transformers. If large enough, they cause half-cycle saturation of a power transformer’s core which could lead to overheating due to excessive stray flux. Finite element matrix (FEM) modelling software is of considerable use in transformer engineering as it is able to solve electromagnetic fields in transformers. For many problems, typically involving only specific parts of a transformer, fairly accurate solutions can be reached quickly. Modelling the effects of GIC or leakage currents from dc systems, however, is more complex because dc components are superimposed on ac in transformers with nonlinear electrical core steel parameters. At the beginning of the investigation, FEM models of different bench-scale laboratory transformers and a 40 MVA three-phase three limb power transformer were investigated, but the results did not sufficiently represent the measurement data due to the application of widely used modelling assumptions regarding the transformer joints. Following the preliminary analyses, practical measurements and FEM simulations were carried out using three industrially made model single-phase four limb transformers (1p4L) without tanks. These test transformers resemble a real power transformer because they have high-quality grain oriented electrical core steel and parallel winding assemblies. Practical laboratory measurements recorded during ac testing were used to calibrate 2D FEM models by adding “equivalent air gaps” at the joints. The implementation of this joint detail helped to overcome the shortcomings of the preliminary FEM simulation. Analyses of the electrical and magnetic responses of the FEM models using simultaneous ac and dc then followed. A refined 3D FEM simulation with more detailed modelling of the core joints of 1p4L model transformers agreed more closely with the practical measurements of ac only no-load conditions. Further, the depiction of stray flux leaving the transformer’s saturated core under simultaneous ac and dc excitation showed an improvement in the approach as measured in the physical model. Saturation inductance (Lsat) is an important parameter for input into mid- to low-frequency lumped parameter transformer models that are used in electromagnetic transients software such as PSCAD/EMTDC, but it is not easily measured and is seldom provided by manufacturers. Some Lsat measurements on the 1p4L test transformers are presented in this thesis, along with some 3D FEM analyses. The measurements and FEM analyses investigated “air core inductance” which represents a transformer without a core, and “terminal saturation inductance” which represents deep saturation due to dc excitation. An important finding in this thesis is that “terminal saturation inductance” is the more useful of the two for topological transformer models investigating realistic GIC excitation. Further to this, a new composite depiction of half-cycle saturation with a multi-parametric relationships supported by measurement and simulation is presented. The main contribution of this thesis is that it gives more accurately the electrical response and distribution of the leakage flux under conditions such as those caused by GIC or other sources of leakage dc excitation, as well as including of joint details in the FEM models through calibration with physical models. This calibration can aid transformer modelling and design in industry for mitigation of the effects of GICs, contributing to improved transformer survival during significant geomagnetic disturbances
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