Description:
Study of some typical cases of low voltage distribution systems; sizing and selection of protection and switching devices, according to different international standards; comparison of different solutions.

6 months internship with ABB in Bergamo. Location is easy to reach by public transportation.

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The design of the arc proof Medium Voltage switchgear must provide safety to operating personnel, and should be able to limit the consequence to the compartment. The purpose of the thesis is to define the main failure mechanisms of a medium voltage panel, on the basis of the mechanisms develop a predictive algorithm that measures the risk level relative to the internal arc failure.

6 months internship with ABB in Dalmine (BG). Location is NOT easily reachable by public transportation.

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Description:
Characterization of raw materials used in low voltage circuit breakers to correlate a chemical parameter and the arc quenching capacity.

Part of the activities will be carried out with an internship period at the ABB laboratories in Bergamo.

6 months internship with ABB in Bergamo. Location is easy to reach by public transportation.

Lo studio riguarda la caratterizzazione e la selezione di materiali compositi con proprietà chimiche e termiche adatte a facilitare l'estinzione dell'arco elettrico durante l'interruzione di correnti elevate in apparecchi per tensioni fino a 1000V.

Tirocinio di 6 mesi presso ABB a Bergamo. Luogo raggiungibile facilmente con i mezzi pubblici.

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A performance model describes operation and reliability of medium voltage circuit breakers and switchboards on a statistical basis. In such a model, predictive / prognostic algorithms are used to evaluate the health status of assets. Inputs are statistical data (monitoring, inspection, endurance tests, returns from field) and real time data from sensors (current, temperature, operation times). Algorithms use predictive models based on attention thresholds, and output reliability KPIs (Key performance Indicators), such as probability of failure and Residual Useful Life, and their confidence bounds.

Activities:

1. Participate in implementation/validation of innovative prognostic models, evaluating output accuracy

2. Participate in the development of a simulator of the complete model (inputs, prognostic algorithms, parameters, outputs).

6 months internship with ABB in Dalmine (BG). Location is NOT easily reachable by public transportation.

Costruzione di modelli di quadri e interruttori di media tensione per descrivere il funzionamento e l'affidabilità delle apparecchiature su base statistica. Applicando algoritmi di predizione/prognostica tali modelli permettono di valutare lo stato di salute dei suddetti asset. Gli input sono sia dati statistici (osservazione, ispezione, endurance test, ritorni dal campo) sia dati in tempo reale ottenibili tramite sensori (corrente, temperatura, tempi di manovra..). Gli algoritmi utilizzano modelli predittivi, basati su soglie di attenzione, per generare come output gli indici più importanti per l'affidabilità (probability of failure, Residual Useful Life) coi relativi confidence bounds.

Attività :

1. Contribuire all’implementazione / validazione di modelli innovativi di prognostica, valutando l’accuratezza degli output

2. Contribuire allo sviluppo di un simulatore del modello completo (inputs, algoritmi di prognostica, parametri, outputs).

Stage di 6 mesi presso ABB a Dalmine (BG). Luogo NON facilmente raggiungibile con i mezzi pubblici.

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Description:
The electrification of agricolture vehicles and implements requires the study of new architectures able to manage power on board respect to the need of the several devices mounted. Each of them is on the same DC bus, but with different needs in term of power supply and energy access. Aim of the thesis is to implement a simulation model suitable for the testing and validation of this architectures.

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Description:
Plastic transistor performances are growing year by year. The improvement of their frequency behavior is mandatory to successfully address IoT applications. The thesis, by means of an ad-hoc developed code for the simulation of plastic devices, aims at elucidating the interplay between transistor geometry, transistor transition frequency and semiconductor mobility.

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Distribution grids probabilistic analysis is an essential step in order to assess the daily network operability under uncertain and stress conditions. It is also functional to the development of new services that require load growth capacity or to the exploitation of new energy resources affected by uncertainty. Efficient numerical tools able to forecast the possible scenarios while accounting for loads and sources uncertainty are thus of paramount importance. The majority of available uncertainty-aware predictive tools are based on Monte Carlo analysis which allows probabilistic evaluations of the network state at the price of time consuming simulations. The aim of the thesis is to develop new algorithm for Probabilistic load Flow

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Description:
Machine learning/Artificial Intelligence has the potential to accelerate technological advances in leaps and bounds. The aim of this project is use machine learning to learn from Pareto-front results in the case of magnetic design, and generate component outputs without the need of going through the Pareto-front analysis, in order to reduce computation time, in order to generate information to aid power system design

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L’obiettivo della tesi è la modellazione del campo elettrico, della distribuzione di cariche e della temperatura all’interno dell’isolante di cavi HVDC in condizioni non stazionarie. La conoscenza delle grandezze fisiche succitate ricopre un’importanza rilevante per la progettazione dei cavi HVDC ma è assai difficile ottenerla tramite misure su cavi in opera. Lo sviluppo di un modello numerico è sfidante in quanto deve poter descrivere accuratamente 1) fenomeni con tempi caratteristici diversi 2) geometrie con fattori di forma estremi e 3) materiali con microstruttura complessa.

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Description:
Enactment of a business process heavily rely on data. These data can be the input for properly perform the activities, and they can be the output of these activities. Computing systems can automatically manage these data as well as human activities can do it manually. Goal of the thesis is to focus on when the business process cannot be executed due to problem in managing the required data. In order to improve the resilience of the process, a novel approach for exploiting smart objects living in the same context of the business process to grab the needed information that is currently not available according to the usual channels.

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In the last years the borders between edge computing and cloud computing became less and less defined. Nowadays, edge components, i.e., sensors, personal devices, etc., can produce data as well as process small portion of such data, opening to scenarios where the data managers can decide to process the data directly in the edge, near the data source, and later move the refined data in the cloud for further analyses. The decision of when and where to move data and computations can be done in an automated fashion, based on the requirements of the data managers and its data movement strategies. However, the current state of the art misses a method for the definition of such strategies and requirements. The goal of this thesis consists in defining a method for the definition of data manager’s requirements and data movement strategies and a web interface for the specification of such requirements and strategies. Environment based on Docker, Kubernetes, OpenFaaS will be investigated on this purpose

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Description:
Business process monitoring usually relies on the information stored into the event log, which contains what has been observed by a BPMS during the execution of a process (e.g., when each activity has started and/or completed its execution). Especially in case of human-centric business processes, it may happen that the process continues being executed even if, for a period of time, the BPMS is not informed about the on-going activities. In this case, monitoring the execution of the process at runtime may experience delays or inconsistencies, and it is possibly done only post-mortem. To solve this issue, artifact-driven monitoring is proposed as an approach to monitor the whole process by monitoring the artifacts manipulated during the process execution, starting from the assumption that if all the artifacts are properly managed then the process instance is conform to the process model. Goal of the thesis is to study and develop an IoT based solution that, starting from a process model is able to properly configure a set of smart devices able to monitor the associated artifact.

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Description:
Business Process Monitoring aims to check if the execution of the activities composing a business process are executed according to the expected plan. Especially in a distributed environment, where different parties are involved in the same process, each of them with monitoring capabilities limited to the activities belonging to their realms, a coherent monitoring of the entire process could be difficult to achieve. For this reason, current approaches usually relies on a central authority on which all the parties trust. If on the one side, this type of solution solves the main issue, the existence of a central node could affect the reliability and the performance of the whole system. As blockchain has emerged as a key technology for enabling the trust among distributed parties without requiring a central authorities, goal of this thesis is to study how blockchain can be involved in the area of business process monitoring to allow a distributed monitoring for multi-party business processes

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