Discharge characteristics and discharge simulation model of long air gaps containing a floating conductor

Long air gaps containing a floating conductor are common insulation types in power grids. During the transmission line live-line work, the process of lineman entering the transmission line air gap constitutes a live-line work combined air gap, which is a typical long air gap containing a floating conductor. This thesis investigates the discharge characteristics, the discharge mechanism and a discharge simulation model of long air gaps containing a floating conductor in order to address the engineering issues in live-line work. The innovative achievements of the thesis are as follows: (1) The effect of the gap distance, the floating electrode structure, the switching impulse wavefront time, the altitude, and the deviation of the floating conductor from the axis on the breakdown voltage was determined. (2) The physical process of the discharges in long air gaps containing a floating conductor was determined. The reason why the discharge characteristics of long air gaps containing a floating electrode with complex geometrics and sharp protrusions and long air gaps with a rod-shaped floating electrode are similar has been studied. The formation mechanism of the lowest breakdown voltage area of a long air gap containing a floating conductor is explained. (3) A simulation discharge model of long air gaps containing a floating conductor was established, which can describe the physical process and predict the breakdown voltage. The model can realize the accurate prediction of the breakdown voltage of typical long air gaps containing a floating conductor and live-line work combined air gaps in transmission lines. The findings of the study can provide theoretical reference and technical support for improving the safety of live-line work.

Models for reliability estimation of HVDC cable systems

The work carried out in this thesis aims at: - studying – in both simulative and experimental methods – the effect of electrical transients (i.e., Voltage Polarity Reversals VPRs, Temporary OverVoltages TOVs, and Superimposed Switching Impulses SSIs) on the aging phenomena in HVDC extruded cable insulations. Dielectric spectroscopy, conductivity measurements, Fourier Transform Infra-Red FTIR spectroscopy, and space charge measurements show variation in the insulating properties of the aged Cross-Linked Polyethylene XLPE specimens compared to non-aged ones. Scission in XLPE bonds and formation of aging chemical bonds is also noticed in aged insulations due to possible oxidation reactions. The aged materials show more ability to accumulate space charges compared to non-aged ones. An increase in both DC electrical conductivity and imaginary permittivity has been also noticed. - The development of life-based geometric design of HVDC cables in a detailed parametric analysis of all parameters that affect the design. Furthermore, the effect of both electrical and thermal transients on the design is also investigated. - The intrinsic thermal instability in HVDC cables and the effect of insulation characteristics on the thermal stability using a temperature and field iterative loop (using numerical methods – Finite Difference Method FDM). The dielectric loss coefficient is also calculated for DC cables and found to be less than that in AC cables. This emphasizes that the intrinsic thermal instability is critical in HVDC cables. - Fitting electrical conductivity models to the experimental measurements using both models found in the literature and modified models to find the best fit by considering the synergistic effect between field and temperature coefficients of electrical conductivity.

Multi-organ support device for combined extracorporeal carbon dioxide removal and hemofiltration treatments with pump-free solution for ultrafiltration

The use of extracorporeal organ support (ECOS) devices is increasingly widespread, to temporarily sustain or replace the functions of impaired organs in critically ill patients. Among ECOS, respiratory functions are supplied by extracorporeal life support (ECLS) therapies like extracorporeal membrane oxygenation (ECMO) and extracorporeal carbon dioxide removal (ECCO2R), and renal replacement therapies (RRT) are used to support kidney functions. However, the leading cause of mortality in critically ill patients is multi-organ dysfunction syndrome (MODS), which requires a complex therapeutic strategy where extracorporeal treatments are often integrated to pharmacological approach. Recently, the concept of multi-organ support therapy (MOST) has been introduced, and several forms of isolated ECOS devices are sequentially connected to provide simultaneous support to different organ systems. The future of critical illness goes towards the development of extracorporeal devices offering multiple organ support therapies on demand by a single hardware platform, where treatment lines can be used alternately or in conjunction. The aim of this industrial PhD project is to design and validate a device for multi-organ support, developing an auxiliary line for renal replacement therapy (hemofiltration) to be integrated on a platform for ECCO2R. The intended purpose of the ancillary line, which can be connected on demand, is to remove excess fluids by ultrafiltration and achieve volume control by the infusion of a replacement solution, as patients undergoing respiratory support are particularly prone to develop fluid overload. Furthermore, an ultrafiltration regulation system shall be developed using a powered and software-modulated pinch-valve on the effluent line of the hemofilter, proposed as an alternative to the state-of-the-art solution with peristaltic pump.

Adjustable robust optimization with nonlinear recourses

Over the last century, mathematical optimization has become a prominent tool for decision making. Its systematic application in practical fields such as economics, logistics or defense led to the development of algorithmic methods with ever increasing efficiency. Indeed, for a variety of real-world problems, finding an optimal decision among a set of (implicitly or explicitly) predefined alternatives has become conceivable in reasonable time. In the last decades, however, the research community raised more and more attention to the role of uncertainty in the optimization process. In particular, one may question the notion of optimality, and even feasibility, when studying decision problems with unknown or imprecise input parameters. This concern is even more critical in a world becoming more and more complex —by which we intend, interconnected —where each individual variation inside a system inevitably causes other variations in the system itself. In this dissertation, we study a class of optimization problems which suffer from imprecise input data and feature a two-stage decision process, i.e., where decisions are made in a sequential order —called stages —and where unknown parameters are revealed throughout the stages. The applications of such problems are plethora in practical fields such as, e.g., facility location problems with uncertain demands, transportation problems with uncertain costs or scheduling under uncertain processing times. The uncertainty is dealt with a robust optimization (RO) viewpoint (also known as "worst-case perspective") and we present original contributions to the RO literature on both the theoretical and practical side.

Analysis and improvements of four-wire power converters for electric vehicle chargers

A robust and well-distributed backbone charging network is the priority to ensure widespread electrification of road transport, providing a driving experience similar to that of internal combustion engine vehicles. International standards set multiple technical targets for on-board and off-board electric vehicle chargers; output voltage levels, harmonic emissions, and isolation requirements strongly influence the design of power converters. Additionally, smart-grid services such as vehicle-to-grid and vehicle-to-vehicle require the implementation of bi-directional stages that inevitably increase system complexity and component count. To face these design challenges, the present thesis provides a rigorous analysis of four-leg and split-capacitor three-phase four-wire active front-end topologies focusing on the harmonic description under different modulation techniques and conditions. The resulting analytical formulation paves the way for converter performance improvements while maintaining regulatory constraints and technical requirements under control. Specifically, split-capacitor inverter current ripple was characterized as providing closed-form formulations valid for every sub-case ranging from synchronous to interleaved PWM. Outcomes are the base for a novel variable switching PWM technique capable of mediating harmonic content limitation and switching loss reduction. A similar analysis is proposed for four-leg inverters with a broad range of continuous and discontinuous PWM modulations. The general superiority of discontinuous PWM modulation in reducing switching losses and limiting harmonic emission was demonstrated. Developments are realized through a parametric description of the neutral wire inductor. Finally, a novel class of integrated isolated converter topologies is proposed aiming at the neutral wire delivery without employing extra switching components rather than the one already available in typical three-phase inverter and dual-active-bridge back-to-back configurations. The fourth leg was integrated inside the dual-active-bridge input bridge providing relevant component count savings. A novel modified single-phase-shift modulation technique was developed to ensure a seamless transition between working conditions like voltage level and power factor. Several simulations and experiments validate the outcomes.

Analytics and optimization for emergency healthcare processes

This thesis deals with the analysis and management of emergency healthcare processes through the use of advanced analytics and optimization approaches. Emergency processes are among the most complex within healthcare. This is due to their non-elective nature and their high variability. This thesis is divided into two topics. The first one concerns the core of emergency healthcare processes, the emergency department (ED). In the second chapter, we describe the ED that is the case study. This is a real case study with data derived from a large ED located in northern Italy. In the next two chapters, we introduce two tools for supporting ED activities. The first one is a new type of analytics model. Its aim is to overcome the traditional methods of analyzing the activities provided in the ED by means of an algorithm that analyses the ED pathway (organized as event log) as a whole. The second tool is a decision-support system, which integrates a deep neural network for the prediction of patient pathways, and an online simulator to evaluate the evolution of the ED over time. Its purpose is to provide a set of solutions to prevent and solve the problem of the ED overcrowding. The second part of the thesis focuses on the COVID-19 pandemic emergency. In the fifth chapter, we describe a tool that was used by the Bologna local health authority in the first part of the pandemic. Its purpose is to analyze the clinical pathway of a patient and from this automatically assign them a state. Physicians used the state for routing the patients to the correct clinical pathways. The last chapter is dedicated to the description of a MIP model, which was used for the organization of the COVID-19 vaccination campaign in the city of Bologna, Italy.

A robotic platform for precision agriculture and applications

Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.

Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena.

Enhancing the reliability of electric transportation motors through insulation coordination and improved qualification

The ambitious goals of increasing the efficiency, performance and power densities of transportation drives cannot be met with compromises in the motor reliability. For the insulation specialists the challenge will be critical as the use of wide-bandgap converters (WBG, based on SiC and GaN switches) and the higher operating voltages expected for the next generation drives will enhance the electrical stresses to unprecedented levels. It is expected for the DC bus in aircrafts to reach 800 V (split +/-400 V) and beyond, driven by the urban air mobility sector and the need for electrification of electro-mechanical/electro-hydraulic actuators (an essential part of the "More Electric Aircraft" concept). Simultaneously the DC bus in electric vehicles (EV) traction motors is anticipated to increase up to 1200 V very soon. The electrical insulation system is one of the most delicate part of the machine in terms of failure probability. In particular, the appearance of partial discharges (PD) is disruptive on the reliability of the drive, especially under fast repetitive transients. Extensive experimental activity has been performed to extend the body of knowledge on PD inception, endurance under PD activity, and explore and identify new phenomena undermining the reliability. The focus has been concentrated on the impact of the WGB-converter produced waveforms and the environmental conditions typical of the aeronautical sector on insulation models. Particular effort was put in the analysis at the reduced pressures typical of aircraft cruise altitude operation. The results obtained, after a critical discussion, have been used to suggest a coordination between the insulation PD inception voltage with the converter stresses and to propose an improved qualification procedure based on the existing IEC 60034-18-41 standard.

On the motion planning & control of nonlinear robotic systems

In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance.

Computational modeling of human atrial cardiomyocytes: integration of electro-mechanical & mechano-electric feedback pathways

The cardiomyocytes are very complex consisting of many interlinked non-linear regulatory mechanisms between electrical excitation and mechanical contraction. Thus given a integrated electromechanically coupled system it becomes hard to understand the individual contributor of cardiac electrics and mechanics under both physiological and pathological conditions. Hence, to identify the causal relationship or to predict the responses in a integrated system the use of computational modeling can be beneficial. Computational modeling is a powerful tool that provides complete control of parameters along with the visibility of all the individual components of the integrated system. The advancement of computational power has made it possible to simulate the models in a short timeframe, providing the possibility of increased predictive power of the integrated system. My doctoral thesis is focused on the development of electromechanically integrated human atrial cardiomyocyte model with proper consideration of feedforward and feedback pathways.

Models and algorithms for real-world optimization problems

This thesis deals with efficient solution of optimization problems of practical interest. The first part of the thesis deals with bin packing problems. The bin packing problem (BPP) is one of the oldest and most fundamental combinatorial optimiza- tion problems. The bin packing problem and its generalizations arise often in real-world ap- plications, from manufacturing industry, logistics and transportation of goods, and scheduling. After an introductory chapter, I will present two applications of two of the most natural extensions of the bin packing: Chapter 2 will be dedicated to an application of bin packing in two dimension to a problem of scheduling a set of computational tasks on a computer cluster, while Chapter 3 deals with the generalization of BPP in three dimensions that arise frequently in logistic and transportation, often com- plemented with additional constraints on the placement of items and characteristics of the solution, like, for example, guarantees on the stability of the items, to avoid potential damage to the transported goods, on the distribution of the total weight of the bins, and on compatibility with loading and unloading operations. The second part of the thesis, and in particular Chapter 4 considers the Trans- mission Expansion Problem (TEP), where an electrical transmission grid must be expanded so as to satisfy future energy demand at the minimum cost, while main- taining some guarantees of robustness to potential line failures. These problems are gaining importance in a world where a shift towards renewable energy can impose a significant geographical reallocation of generation capacities, resulting in the ne- cessity of expanding current power transmission grids.

Analysis and design considerations of resonator arrays for inductive power transfer systems

In the frame of inductive power transfer (IPT) systems, arrays of magnetically coupled resonators have received increasing attention as they are cheap and versatile due to their simple structure. They consist of magnetically coupled coils, which resonate with their self-capacitance or lumped capacitive networks. Of great industrial interest are planar resonator arrays used to power a receiver that can be placed at any position above the array. A thorough circuit analysis has been carried out, first starting from traditional two-coil IPT devices. Then, resonator arrays have been introduced, with particular attention to the case of arrays with a receiver. To evaluate the system performance, a circuit model based on original analytical formulas has been developed and experimentally validated. The results of the analysis also led to the definition of a new doubly-fed array configuration with a receiver that can be placed above it at any position. A suitable control strategy aimed at maximising the transmitted power and the efficiency has been also proposed. The study of the array currents has been carried out resorting to the theory of magneto-inductive waves, allowing useful insight to be highlighted. The analysis has been completed with a numerical and experimental study on the magnetic field distribution originating from the array. Furthermore, an application of the resonator array as a position sensor has been investigated. The position of the receiver is estimated through the measurement of the array input impedance, for which an original analytical expression has been also obtained. The application of this sensing technique in an automotive dynamic IPT system has been discussed. The thesis concludes with an evaluation of the possible applications of two-dimensional resonator arrays in IPT systems. These devices can be used to improve system efficiency and transmitted power, as well as for magnetic field shielding.

Exact and heuristic algorithms for the integrated planning of multi-energy systems

The aim of this thesis is to present exact and heuristic algorithms for the integrated planning of multi-energy systems. The idea is to disaggregate the energy system, starting first with its core the Central Energy System, and then to proceed towards the Decentral part. Therefore, a mathematical model for the generation expansion operations to optimize the performance of a Central Energy System system is first proposed. To ensure that the proposed generation operations are compatible with the network, some extensions of the existing network are considered as well. All these decisions are evaluated both from an economic viewpoint and from an environmental perspective, as specific constraints related to greenhouse gases emissions are imposed in the formulation. Then, the thesis presents an optimization model for solar organic Rankine cycle in the context of transactive energy trading. In this study, the impact that this technology can have on the peer-to-peer trading application in renewable based community microgrids is inspected. Here the consumer becomes a prosumer and engages actively in virtual trading with other prosumers at the distribution system level. Moreover, there is an investigation of how different technological parameters of the solar Organic Rankine Cycle may affect the final solution. Finally, the thesis introduces a tactical optimization model for the maintenance operations’ scheduling phase of a Combined Heat and Power plant. Specifically, two types of cleaning operations are considered, i.e., online cleaning and offline cleaning. Furthermore, a piecewise linear representation of the electric efficiency variation curve is included. Given the challenge of solving the tactical management model, a heuristic algorithm is proposed. The heuristic works by solving the daily operational production scheduling problem, based on the final consumer’s demand and on the electricity prices. The aggregate information from the operational problem is used to derive maintenance decisions at a tactical level.

Distributed and synchronized measurements for the wide-area observation of electric power systems in distorted and low-inertia conditions

With the aim of heading towards a more sustainable future, there has been a noticeable increase in the installation of Renewable Energy Sources (RES) in power systems in the latest years. Besides the evident environmental benefits, RES pose several technological challenges in terms of scheduling, operation, and control of transmission and distribution power networks. Therefore, it raised the necessity of developing smart grids, relying on suitable distributed measurement infrastructure, for instance, based on Phasor Measurement Units (PMUs). Not only are such devices able to estimate a phasor, but they can also provide time information which is essential for real-time monitoring. This Thesis falls within this context by analyzing the uncertainty requirements of PMUs in distribution and transmission applications. Concerning the latter, the reliability of PMU measurements during severe power system events is examined, whereas for the first, typical configurations of distribution networks are studied for the development of target uncertainties. The second part of the Thesis, instead, is dedicated to the application of PMUs in low-inertia power grids. The replacement of traditional synchronous machines with inertia-less RES is progressively reducing the overall system inertia, resulting in faster and more severe events. In this scenario, PMUs may play a vital role in spite of the fact that no standard requirements nor target uncertainties are yet available. This Thesis deeply investigates PMU-based applications, by proposing a new inertia index relying only on local measurements and evaluating their reliability in low-inertia scenarios. It also develops possible uncertainty intervals based on the electrical instrumentation currently used in power systems and assesses the interoperability with other devices before and after contingency events.