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Det er gjennomført en interessentanalyse med fokus på utbygginger av storskala havvindparker i Norge. Analysen hadde som mål å kartlegge relevante interessenter, fastsette deres mål og viktigste samarbeidspartnere og undersøke deres oppfatning av de viktigste driverne og barrierene for storskala havvindparkutbygginger i norske farvann. Interessentene ble delt inn i kategorier, og data ble samlet inn ved hjelp av strukturerte personlige en-til-en-intervjuer med hver identifiserte interessent. Resultatene viser at interessentlandskapet er stort og inkluderer mange flere aktører enn bare vindparkutviklerne. Dette inkluderer grupper med tidligere interesse for områdene som påvirkes av utviklingen, aktører i leverandørindustrien og brede samfunnsgrupper som strømforbrukere, kommuner og miljøorganisasjoner. Hoveddriverne for utbyggingen er identifisert som verdiskaping både for vindparkutviklere, leverandørindustri og samfunnet ellers, samt å bringe mer fornybar energi inn i energisystemet for å nå klimamålene. Hovedbarrierer er identifisert som potensielle områdekonflikter, langvarige og komplekse konsesjonsprosesser, integrasjon av uregulerbar kraftproduksjon og tilgjengelighet av utstyr, råvarer og teknologi. Flere aktører har meldt om behov for større åpenhet om konsesjonsprosessen, miljøhensyn og markedsutformingen av utbyggingene. Den mest sentrale begrunnelsen for behovet for slik åpenhet er behovet for offentlig støtte til de politiske prosessene som trengs for en så storstilt utvikling. Uten politisk vilje vil det ikke bli realisert utbygginger av havvindparker i stor skala. A stakeholder analysis focused on offshore wind farm developments in Norway has been conducted. The analysis aimed at mapping the relevant stakeholders, determining their objectives and main collaborating partners and investigating their perception of the main drivers and barriers for large scale offshore wind farm developments in Norwegian waters. The stakeholders were divided into categories, and data was collected by means of structured in-person one-on-one interviews with each identified stakeholder. The results show that the stakeholder landscape is large and includes many more actors than just the wind farm developers. This includes groups with prior interest in the areas affected by the developments, actors in the supply industry and wide groups of society such as electricity consumers, municipalities and environmental organisations. Main drivers for the development have been identified as value creation both for wind farm developers, supply industry and society as a whole as well as bringing more renewable energy into the energy system in order to meet the climate targets. Main barriers have been identified as potential area conflicts, lengthy and complex licensing processes, integration of intermittent power production and availability of equipment, raw materials and technology. Several stakeholders have reported the need for greater transparency in the licensing process, the environmental considerations done and the market design of the developments. The stated central rationale for the need for such transparency is the need for public support to the political processes needed for such large-scale developments. Without long term political willingness large scale offshore wind farm developments is not likely to be realised.

In this report the results of the emittance measurements made at CERN's linac 3 ECR (Electron Cyclotron Resonance) heavy ions source are given. A large number of the source parameters were varied and the effect on the emittance measured. The report also includes a short introduction to the emittance concept in particle beam physics and the working principles of the ECR source as well as the measuring system and instrumentation.

The demand for energy is steadily increasing. The global community is working towards a society supplied by clean energy from renewable sources. This leads to new requirements for the electrical power system. To solve the transition, digital technologies need to be implemented in the system through smart grid solutions. This master’s thesis explored one central aspect of smart grid; prediction of fault occurrences in the power grid. These questions were attempted to be answered: Is it possible to predict the faults before they happen? How long in advance can they be predicted, if the first question turns out positive? What kind of physical parameters are most suitable to use as features for prediction? What kind of method should be used to predict the faults? Will it be possible to implement this in a real-time monitoring system? Knowledge from the electrical power system domain and the data science domain were combined to obtain a qualitative evaluation of the tests conducted. Measurement data of the fault types interruption, voltage dip, and earth fault, as well as data gathered from nominal operated power grid were be used in the predictions. The samples were collected from Norwegian power grids operated at voltage levels ranging from 15 kV to 300 kV. The assessment was be divided into multiple tests. The objective in focus was to build and compare results from three different recurrent neural network (RNN) architectures trained on time-series data acquired from power quality (PQ) measurements. A sequence-to-sequence Autoencoder was proposed for use in signal feature extraction. Various tests were conducted from investigating the raw data, to analysing the output of the model. Results have shown a prediction horizon up to seven minutes is possible. It was proposed that even longer horizons may be plausible. Further investigation into the harmonic components was proposed, related to signal analysis and statistics, for better feature extraction. The most promising features were the harmonic components of voltage and current. The fault types may have various composition of harmonic components giving the different fault types an unique signature. Improvements to the model have been proposed, focusing on anomaly detection. In combination with other monitoring equipment, a fault event prediction system can be used as a tool in decision making. As part of a competence building research program this thesis contributes to the foundation of further research on the area outlined. Placing the research in a broader view, the results may lead to increased security of power supply, reduced operation and maintenance (O & M) costs, and indirectly reducing the impact on the environment by enabling a safer integration of sustainable energy sources. Etterspørselen etter energi er i stadig økning. Verdenssamfunnet samarbeider om en fremtid forsynt av ren energi fra fornybare energikilder. Dette fører til nye krav for det elektriske kraftsystemet. For å løse overgangen må digitale teknologier implementeres i systemet gjennom smartnettløsninger. Denne masteroppgaven utforsket et sentralt punkt ved smartenett; nemlig predikering av feilhendelser i strømnettet. Disse spørsmålene ble forsøkt besvart: Er det mulig å predikere feilene før de oppstår? Hvor langt inni fremtiden kan feilene bli predikert? Hvilke fysiske parametre er mest egnet til bruk som features ved predikering? Hvilen metode burde brukes til å predikere feil? Vil det være mulig å implementere dette i et sanntidsmonitoreringssystem? Etterspørselen etter energi er i stadig økning. Verdenssamfunnet samarbeider om en fremtid forsynt av ren energi fra fornybare energikilder. Dette fører til nye krav for det elektriske kraftsystemet. For å løse overgangen må digitale teknologier implementeres i systemet gjennom smartnettløsninger. Denne masteroppgaven utforsket et sentralt punkt ved smartenett; nemlig predikering av feilhendelser i strømnettet. Disse spørsmålene ble forsøkt besvart: Er det mulig å predikere feilene før de oppstår? Hvor langt inni fremtiden kan feilene bli predikert? Hvilke fysiske parametre er mest egnet til bruk som features ved predikering? Hvilen metode burde brukes til å predikere feil? Vil det være mulig å implementere dette i et sanntidsmonitoreringssystem? M-MF