Distributed multilevel optimization for complex structures

Optimization problems concerning complex structures with many design variables may entail an unacceptable computational cost. This problem can be reduced considerably with a multilevel approach: A structure consisting of several components is optimized as a whole (global) as well as on the component level. In this paper, an optimization method is discussed with applications in the assessment of the impact of new design considerations in the development of a structure. A strategy based on fully stressed design is applied for optimization problems in linear statics. A global model is used to calculate the interactions (e.g., loads) for each of the components. These components are then optimized using the prescribed interactions, followed by a new global calculation to update the interactions. Mixed discrete and continuous design variables as well as different design configurations are possible. An application of this strategy is presented in the form of the full optimization of a vertical tail plane center box of a generic large passenger aircraft. In linear dynamics, the parametrization of the component interactions is problematic due to the frequency dependence. Hence, a modified method is presented in which the speed of component mode synthesis is used to avoid this parametrization. This method is applied to a simple test case that originates from noise control. \u

Precise modelling of switching and conduction losses in cascaded h-bridge multilevel inverters

Nowadays, voltage source multilevel inverters are being used extensively in industry due to its many advantages, compared to conventional two level inverters, such as higher output voltage at low switching frequency, low voltage stress(dv/dt), lower total harmonic distortion (THD), less electro-magnetic interference (EMI), smaller output filter and higher fundamental output. However, the evaluation of multilevel inverter losses is much more complicated compared to two level inverters. This paper proposes an on-line model for precise calculation of conduction and switching losses for cascaded h-bridge multilevel inverter. The model is simple and efficient and gives clear process of loss calculation. A singlephase 7-level cascaded h-bridge with IGBT's as switching devices has been used as a case study of the proposed model. The inverter has been controlled using selective harmonic elimination in which the switching angles were determined using the Genetic Algorithm (GA). MATLAB-SIMULINK is used for the modelling and simulation

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Comparison between unipolar and bipolar single phase grid-connected inverters for PV applications

An inverter is essential for the interfacing of photovoltaic panels with the AC network. There are many possible inverter topologies and inverter switching schemes and each one will have its own relative advantages and disadvantages. Efficiency and output current distortion are two important factors governing the choice of inverter system. In this paper, it is argued that current controlled inverters offer significant advantages from the point of view of minimisation of current distortion. Two inverter switching strategies are explored in detail. These are the unipolar current controlled inverter and the bipolar current controlled inverter. With respect to low frequency distortion, previously published works provide theoretical arguments in favour of bipolar switching. On the other hand it has also been argued that the unipolar switched inverter offers reduced switching losses and generates less EMI. On efficiency grounds, it appears that the unipolar switched inverter has an advantage. However, experimental results presented in this paper show that the level of low frequency current distortion in the unipolar switched inverter is such that it can only comply with Australian Standard 4777.2 above a minimum output current. On the other hand it is shown that at the same current levels bipolar switching results in reduced low frequency harmonics

Comparison between unipolar and bipolar single phase grid-connected inverters for PV applications

An inverter is essential for the interfacing of photovoltaic panels with the AC network. There are many possible inverter topologies and inverter switching schemes and each one will have its own relative advantages and disadvantages. Efficiency and output current distortion are two important factors governing the choice of inverter system. In this paper, it is argued that current controlled inverters offer significant advantages from the point of view of minimisation of current distortion. Two inverter switching strategies are explored in detail. These are the unipolar current controlled inverter and the bipolar current controlled inverter. With respect to low frequency distortion, previously published works provide theoretical arguments in favour of bipolar switching. On the other hand it has also been argued that the unipolar switched inverter offers reduced switching losses and generates less EMI. On efficiency grounds, it appears that the unipolar switched inverter has an advantage. However, experimental results presented in this paper show that the level of low frequency current distortion in the unipolar switched inverter is such that it can only comply with Australian Standard 4777.2 above a minimum output current. On the other hand it is shown that at the same current levels bipolar switching results in reduced low frequency harmonics

Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design

An optimal experimental set-up maximizes the value of data for statistical inferences and predictions. The efficiency of strategies for finding optimal experimental set-ups is particularly important for experiments that are time-consuming or expensive to perform. For instance, in the situation when the experiments are modeled by Partial Differential Equations (PDEs), multilevel methods have been proven to dramatically reduce the computational complexity of their single-level counterparts when estimating expected values. For a setting where PDEs can model experiments, we propose two multilevel methods for estimating a popular design criterion known as the expected information gain in simulation-based Bayesian optimal experimental design. The expected information gain criterion is of a nested expectation form, and only a handful of multilevel methods have been proposed for problems of such form. We propose a Multilevel Double Loop Monte Carlo (MLDLMC), which is a multilevel strategy with Double Loop Monte Carlo (DLMC), and a Multilevel Double Loop Stochastic Collocation (MLDLSC), which performs a high-dimensional integration by deterministic quadrature on sparse grids. For both methods, the Laplace approximation is used for importance sampling that significantly reduces the computational work of estimating inner expectations. The optimal values of the method parameters are determined by minimizing the average computational work, subject to satisfying the desired error tolerance. The computational efficiencies of the methods are demonstrated by estimating the expected information gain for Bayesian inference of the fiber orientation in composite laminate materials from an electrical impedance tomography experiment. MLDLSC performs better than MLDLMC when the regularity of the quantity of interest, with respect to the additive noise and the unknown parameters, can be exploited

Parameterization adaption for 3D shape optimization in aerodynamics

When solving a PDE problem numerically, a certain mesh-refinement process is always implicit, and very classically, mesh adaptivity is a very effective means to accelerate grid convergence. Similarly, when optimizing a shape by means of an explicit geometrical representation, it is natural to seek for an analogous concept of parameterization adaptivity. We propose here an adaptive parameterization for three-dimensional optimum design in aerodynamics by using the so-called "Free-Form Deformation" approach based on 3D tensorial B\'ezier parameterization. The proposed procedure leads to efficient numerical simulations with highly reduced computational costs

Power Quality Enhancement in Electricity Grids with Wind Energy Using Multicell Converters and Energy Storage

In recent years, the wind power industry is experiencing a rapid growth and more wind farms with larger size wind turbines are being connected to the power system. While this contributes to the overall security of electricity supply, large-scale deployment of wind energy into the grid also presents many technical challenges. Most of these challenges are one way or another, related to the variability and intermittent nature of wind and affect the power quality of the distribution grid. Power quality relates to factors that cause variations in the voltage level and frequency as well as distortion in the voltage and current waveforms due to wind variability which produces both harmonics and inter-harmonics. The main motivation behind work is to propose a new topology of the static AC/DC/AC multicell converter to improve the power quality in grid-connected wind energy conversion systems. Serial switching cells have the ability to achieve a high power with lower-size components and improve the voltage waveforms at the input and output of the converter by increasing the number of cells. Furthermore, a battery energy storage system is included and a power management strategy is designed to ensure the continuity of power supply and consequently the autonomy of the proposed system. The simulation results are presented for a 149.2 kW wind turbine induction generator system and the results obtained demonstrate the reduced harmonics, improved transient response, and reference tracking of the voltage output of the wind energy conversion system.Peer reviewedFinal Accepted Versio