Power Converters and Electric Drives for Smart Grid Applications

The present dissertation aims to explore, theoretically and experimentally, the problems and the potential advantages of different types of power converters for “Smart Grid” applications, with particular emphasis on multi-level architectures, which are attracting a rising interest even for industrial requests. The models of the main multilevel architectures (Diode-Clamped and Cascaded) are shown. The best suited modulation strategies to function as a network interface are identified. In particular, the close correlation between PWM (Pulse Width Modulation) approach and SVM (Space Vector Modulation) approach is highlighted. An innovative multilevel topology called MMC (Modular Multilevel Converter) is investigated, and the single-phase, three-phase and "back to back" configurations are analyzed. Specific control techniques that can manage, in an appropriate way, the charge level of the numerous capacitors and handle the power flow in a flexible way are defined and experimentally validated. Another converter that is attracting interest in “Power Conditioning Systems” field is the “Matrix Converter”. Even in this architecture, the output voltage is multilevel. It offers an high quality input current, a bidirectional power flow and has the possibility to control the input power factor (i.e. possibility to participate to active and reactive power regulations). The implemented control system, that allows fast data acquisition for diagnostic purposes, is described and experimentally verified.

Harnessing the power of Drosophila model to obtain new insights on MYC and ABCC1 cellular functions: from neuroblastoma to autism spectrum disorder

MYCN amplification is a genetic hallmark of the childhood tumour neuroblastoma. MYCN-MAX dimers activate the expression of genes promoting cell proliferation. Moreover, MYCN seems to transcriptionally repress cell differentiation even in absence of MAX. We adopted the Drosophila eye as model to investigate the effect of high MYC to MAX expression ratio on cells. We found that dMyc overexpression in eye cell precursors inhibits cell differentiation and induces the ectopic expression of Antennapedia (the wing Hox gene). The further increase of MYC/MAX ratio results in an eye-to-wing homeotic transformation. Notably, dMyc overexpression phenotype is suppressed by low levels of transcriptional co-repressors and MYCN associates to the promoter of Deformed (the eye Hox gene) in proximity to repressive sites. Hence, we envisage that, in presence of high MYC/MAX ratio, the “free MYC” might inhibit Deformed expression, leading in turn to the ectopic expression of Antennapedia. This suggests that MYCN might reinforce its oncogenic role by affecting the physiological homeotic program. Furthermore, poor neuroblastoma outcome associates with a high level of the MRP1 protein, encoded by the ABCC1 gene and known to promote drug efflux in cancer cells. Intriguingly, this correlation persists regardless of chemotherapy and ABCC1 overexpression enhances neuroblastoma cell motility. We found that Drosophila dMRP contributes to the adhesion between the dorsal and ventral epithelia of the wing by inhibiting the function of integrin receptors, well known regulators of cell adhesion and migration. Besides, integrins play a crucial role during synaptogenesis and ABCC1 locus is included in a copy number variable region of the human genome (16p13.11) involved in neuropsychiatric diseases. Interestingly, we found that the altered dMRP/MRP1 level affects nervous system development in Drosophila embryos. These preliminary findings point out novel ABCC1 functions possibly defining ABCC1 contribution to neuroblastoma and to the pathogenicity of 16p13.11 deletion/duplication

Frequency domain analysis of stationary time series

This thesis provides a necessary and sufficient condition for asymptotic efficiency of a nonparametric estimator of the generalised autocovariance function of a Gaussian stationary random process. The generalised autocovariance function is the inverse Fourier transform of a power transformation of the spectral density, and encompasses the traditional and inverse autocovariance functions. Its nonparametric estimator is based on the inverse discrete Fourier transform of the same power transformation of the pooled periodogram. The general result is then applied to the class of Gaussian stationary ARMA processes and its implications are discussed. We illustrate that for a class of contrast functionals and spectral densities, the minimum contrast estimator of the spectral density satisfies a Yule-Walker system of equations in the generalised autocovariance estimator. Selection of the pooling parameter, which characterizes the nonparametric estimator of the generalised autocovariance, controlling its resolution, is addressed by using a multiplicative periodogram bootstrap to estimate the finite-sample distribution of the estimator. A multivariate extension of recently introduced spectral models for univariate time series is considered, and an algorithm for the coefficients of a power transformation of matrix polynomials is derived, which allows to obtain the Wold coefficients from the matrix coefficients characterizing the generalised matrix cepstral models. This algorithm also allows the definition of the matrix variance profile, providing important quantities for vector time series analysis. A nonparametric estimator based on a transformation of the smoothed periodogram is proposed for estimation of the matrix variance profile.

Nanostructured piezoelectric materials for the design and development of self-sensing composite materials and energy harvesting devices

The work activities reported in this PhD thesis regard the functionalization of composite materials and the realization of energy harvesting devices by using nanostructured piezoelectric materials, which can be integrated in the composite without affecting its mechanical properties. The self-sensing composite materials were fabricated by interleaving between the plies of the laminate the piezoelectric elements. The problem of negatively impacting on the mechanical properties of the hosting structure was addressed by shaping the piezoelectric materials in appropriate ways. In the case of polymeric piezoelectric materials, the electrospinning technique allowed to produce highly-porous nanofibrous membranes which can be immerged in the hosting matrix without inducing delamination risk. The flexibility of the polymers was exploited also for the production of flexible tactile sensors. The sensing performances of the specimens were evaluated also in terms of lifetime with fatigue tests. In the case of ceramic piezo-materials, the production and the interleaving of nanometric piezoelectric powder limitedly affected the impact resistance of the laminate, which showed enhanced sensing properties. In addition to this, a model was proposed to predict the piezoelectric response of the self-sensing composite materials as function of the amount of the piezo-phase within the laminate and to adapt its sensing functionalities also for quasi-static loads. Indeed, one final application of the work was to integrate the piezoelectric nanofibers in the sole of a prosthetic foot in order to detect the walking cycle, which has a period in the order of 1 second. In the end, the energy harvesting capabilities of the piezoelectric materials were investigated, with the aim to design wearable devices able to collect energy from the environment and from the body movements. The research activities focused both on the power transfer capability to an external load and the charging of an energy storage unit, like, e.g., a supercapacitor.

A multidisciplinary approach to the study of protein dynamics and signal transmission

Allostery is a phenomenon of fundamental importance in biology, allowing regulation of function and dynamic adaptability of enzymes and proteins. Despite the allosteric effect was first observed more than a century ago allostery remains a biophysical enigma, defined as the “second secret of life”. The challenge is mainly associated to the rather complex nature of the allosteric mechanisms, which manifests itself as the alteration of the biological function of a protein/enzyme (e.g. ligand/substrate binding at the active site) by binding of “other object” (“allos stereos” in Greek) at a site distant (> 1 nanometer) from the active site, namely the effector site. Thus, at the heart of allostery there is signal propagation from the effector to the active site through a dense protein matrix, with a fundamental challenge being represented by the elucidation of the physico-chemical interactions between amino acid residues allowing communicatio n between the two binding sites, i.e. the “allosteric pathways”. Here, we propose a multidisciplinary approach based on a combination of computational chemistry, involving molecular dynamics simulations of protein motions, (bio)physical analysis of allosteric systems, including multiple sequence alignments of known allosteric systems, and mathematical tools based on graph theory and machine learning that can greatly help understanding the complexity of dynamical interactions involved in the different allosteric systems. The project aims at developing robust and fast tools to identify unknown allosteric pathways. The characterization and predictions of such allosteric spots could elucidate and fully exploit the power of allosteric modulation in enzymes and DNA-protein complexes, with great potential applications in enzyme engineering and drug discovery.