The purpose with this project is to develop a model of the whole power system of Gotland in the power system simulation software PSS/E. A model of the whole power system of Gotland has earlier been used in the power system simulation software Simpow but now there is a need to develop a model in PSS/E. In the power system of Gotland there are several components that need to be modelled such as lines, loads, transformers, shunt impedances, synchronous machines, asynchronous machines, an HVDC Classic link and an HVDC Light link. These components are modelled in the Simpow model and needs to be converted to the PSS/E model. The aim is to develop a model in PSS/E that is as equal as possible to the model in Simpow. Especially the HVDC Light link at Gotland has been investigated in the project. A problem with converting data from Simpow to PSS/E is that the models of several components differ in Simpow and PSS/E. Lines and shunt impedances can be modelled in the same way but the models for loads, transformers, synchronous machines, asynchronous machines, the HVDC Classic link, and the HVDC Light link differ in Simpow and PSS/E. The models in Simpow are converted to the models in PSS/E in an as equal way as possible. The results in PSS/E are analyzed and compared with the Simpow model. In the project we have also made a test of fault simulations in time-domain simulations in PSS/E. The aim with this test is to verify the PSS/E calculations when a three-phase or a single-phase fault is applied. The reason for that is that PSS/E only calculates using positivesequence components and therefore only is able to calculate exact during circumstances of symmetrical loads and faults. The result shows that the calculations for both symmetrical and unsymmetrical faults in PSS/E are correct concerning the positive-sequence components. A drawback in PSS/E is, however, that we do not have any information concerning the negativeand zero-sequence  components, which results in that we cannot calculate the three phasequantities.

I detta projekt har målet varit att utveckla en elektronikenhet som möjliggör mätning av ström och spänning i tre faser. En viktig motivation för detta arbete äratt hantera utmaningarna med varierande nätkvalitet vid export av trefasmaskinerglobalt. Genom att övervaka ström och spänning kan användare få information omeventuella förändringar för att skydda maskinerna. Resultaten av detta arbete innefattar en jämförelse av olika sensorer, vilket ledde tillvalet av EVAL_ADE9430ARSDZ-kortet för att säkerställa att alla specifikationeruppfylldes. Efter att kortet hade programmerats genomgick det olika tester för attsäkerställa att alla specificerade kriterier uppfylldes. Resultaten visade en avvikelsemed endast cirka 0,163% för spänningsmätningar och 5,11% för strömmätningar. Sammanfattningsvis har detta arbete möjliggjort utvecklingen av en enhet som kanvara värdefull för tillverkare av mobil trefasansluten utrustning och kan bidra till attsäkerställa maskiners prestanda under varierande nätvillkor. Det finns dock utrymme för vidare utveckling och förbättringar av mätdatainsamlingen för att genereramer omfattande och detaljerade resultat. / In this project, the goal has been to develop an electronic unit enabling the measurement of current and voltage in three phases. An essential motivation for thiswork is to address the challenges posed by varying power quality when exportingthree-phase machinery globally. Users can be informed of any changes to protect themachinery by monitoring current and voltage. The results of this work include a comparison of different development sensors,leading to the selection of the EVAL_ADE9430ARSDZ board to ensure that allspecifications were met. After programming, the board underwent various tests toverify compliance with all specified criteria. The results indicated a deviation ofonly approximately 0.163% for voltage measurements and 5.11% for current measurements. In summary, this work has facilitated the development of a device that can be valuable for manufacturers of mobile three-phase equipment, contributing to ensuringmachine performance under varying power conditions. However, there is room forfurther development and improvements in data collection to generate more comprehensive and detailed results.

Detta examensarbete beskriver en nätanalys av delar av Ale Els nät för att underlätta vid framtida reinvesteringar i nätet. Examensarbetet syftar till att göra en sammanställning av reinvesteringsbehovet för två 10 kV linjers tillhörande lågspänningsnät och deras transformatorstationer. En nätanalys kan utföras på olika sätt och innehålla flera olika delar. Denna nätanalys omfattar en beskrivning av det aktuella området och nätberäkningar som utförts i dpPower för att undersöka vilka delar av nätet som inte är optimalt utformade. Detta har tillsammans med fältbesök och undersökning av störningsstatistik mynnat ut i olika åtgärdsförslag. Nätstationerna av typen "Combi Lomma" har rangordnats efter deras behov att ersättas. Därefter har åtgärder vid 9 olika nätstationer föreslagits med målet att minska spännings-fall, bryttider, belastningsgrader och öka driftsäkerheten. Åtgärdsförslagen innefattar som ett exempel en kund med 17 % spänningsfall, 8,9 s bryttid där delar av ledningen har en belastningsgrad på 130 %. Efter att luftledningen har ersatts med nedgrävd kabel och ett kabelskåp installerats har det beräknade spänningsfallet minskats till 3,9 %, bryttiden till 0,047 s och belastningsgraden till 49 %. / This thesis describes a grid analysis of parts of Ale El’s grid to facilitate future re-investments in the grid. The thesis aims to make a compilation of reinvestment requirements for two 10 kV line associated low voltage grid and their substations. A grid analysis can be performed in various ways and include several different parts. The grid analysis includes a description of the area and grid calculations performed in dpPower to investigate which parts of the grid that are not optimally designed. This, together with field visits and study of power outage statistics resulted in various proposals for action. Substations of type "Combi Lomma" have been ranked according to their need to be re-placed. Thereafter measures have been proposed in 9 different substations with the aim to reduce voltage drop, break-times, overloads and increase reliability. As an example of these measures is improving the quality of electricity for a customer with 17% voltage drop, 8.9 s break-time and parts of the line supplying the costumer has a load rate of 130 %. The overhead line that is supplying the costumer at present could be replaced with cables in the ground and a distribution board could be installed. This would result in a calculated voltage drop reduced to 3.9%, break-time to 0,047s and the load rate to 49%.

No description available.

This thesis treats the design and implementation of a power distribution network for a controller PCB for controlling charging of electric vehicles. The controller PCB is powered by mains power, and thus needs both AC to DC conversion and DC to DC conversion in order to operate. The thesis focuses on the design of an isolated flyback topology AC to DC converter, while also describing the design and implementation of the DC to DC converters needed for the controller PCB to operate. The work started with some theoretical study, and then progressed into designing the converters. The AC to DC and the DC to DC converters where designed in parallel. After the design phase was complete the converters where implemented on PCBs for evaluation. The evaluation of the AC to DC converter involved evaluation of several different transformers from different suppliers, as well as evaluation of the circuit design itself. All converters designed proved functional after evaluation.

Improving the reliability and efficiency of rotating machinery are central problems in many application domains, such as energy production and transportation. This requires efficient condition monitoring methods, including analytics needed to predict and detect faults and manage the high volume and velocity of data. Rolling element bearings are essential components of rotating machines, which are particularly important to monitor due to the high requirements on the operational conditions. Bearings are also located near the rotating parts of the machines and thereby the signal sources that characterize faults and abnormal operational conditions. Thus, bearings with embedded sensing, analysis and communication capabilities are developed.   However, the analysis of signals from bearings and the surrounding components is a challenging problem due to the high variability and complexity of the systems. For example, machines evolve over time due to wear and maintenance, and the operational conditions typically also vary over time. Furthermore, the variety of fault signatures and failure mechanisms makes it difficult to derive generally useful and accurate models, which enable early detection of faults at reasonable cost. Therefore, investigations of machine learning methods that avoid some of these difficulties by automated on-line adaptation of the signal model are motivated. In particular, can unsupervised feature learning methods be used to automatically derive useful information about the state and operational conditions of a rotating machine? What additional methods are needed to recognize normal operational conditions and detect abnormal conditions, for example in terms of learned features or changes of model parameters?   Condition monitoring systems are typically based on condition indicators that are pre-defined by experts, such as the amplitudes in certain frequency bands of a vibration signal, or the temperature of a bearing. Condition indicators are used to define alarms in terms of thresholds; when the indicator is above (or below) the threshold, an alarm indicating a fault condition is generated, without further information about the root cause of the fault. Similarly, machine learning methods and labeled datasets are used to train classifiers that can be used for the detection of faults. The accuracy and reliability of such condition monitoring methods depends on the type of condition indicators used and the data considered when determining the model parameters. Hence, this approach can be challenging to apply in the field where machines and sensor systems are different and change over time, and parameters have different meaning depending on the conditions. Adaptation of the model parameters to each condition monitoring application and operational condition is also difficult due to the need for labeled training data representing all relevant conditions, and the high cost of manual configuration. Therefore, neither of these solutions is viable in general.   In this thesis I investigate unsupervised methods for feature learning and anomaly detection, which can operate online without pre-training with labeled datasets. Concepts and methods for validation of normal operational conditions and detection of abnormal operational conditions based on automatically learned features are proposed and studied. In particular, dictionary learning is applied to vibration and acoustic emission signals obtained from laboratory experiments and condition monitoring systems. The methodology is based on the assumption that signals can be described as a linear superposition of noise and learned atomic waveforms of arbitrary shape, amplitude and position. Greedy sparse coding algorithms and probabilistic gradient methods are used to learn dictionaries of atomic waveforms enabling sparse representation of the vibration and acoustic emission signals. As a result, the model can adapt automatically to different machine configurations, and environmental and operational conditions with a minimum of initial configuration. In addition, sparse coding results in reduced data rates that can simplify the processing and communication of information in resource-constrained systems.   Measures that can be used to detect anomalies in a rotating machine are introduced and studied, like the dictionary distance between an online propagated dictionary and a set of dictionaries learned when the machine is known to operate in healthy conditions. In addition, the possibility to generalize a dictionary learned from the vibration signal in one machine to another similar machine is studied in the case of wind turbines.   The main contributions of this thesis are the extension of unsupervised dictionary learning to condition monitoring for anomaly detection purposes, and the related case studies demonstrating that the learned features can be used to obtain information about the condition. The cases studies include vibration signals from controlled ball bearing experiments and wind turbines; and acoustic emission signals from controlled tensile strength tests and bearing contamination experiments. It is found that the dictionary distance between an online propagated dictionary and a baseline dictionary trained in healthy conditions can increase up to three times when a fault appears, without reference to kinematic information like defect frequencies. Furthermore, it is found that in the presence of a bearing defect, impulse-like waveforms with center frequencies that are about two times higher than in the healthy condition are learned. In the case of acoustic emission analysis, it is shown that the representations of signals of different strain stages of stainless steel appear as distinct clusters. Furthermore, the repetition rates of learned acoustic emission waveforms are found to be markedly different for a bearing with and without particles in the lubricant, especially at high rotational speed above 1000 rpm, where particle contaminants are difficult to detect using conventional methods. Different hyperparameters are investigated and it is found that the model is useful for anomaly detection with as little as 2.5 % preserved coefficients.

Compact, electric drives for automotive traction applications represent animportant enabler towards realizing tomorrow’s fossil free transport solutions.One attractive solution is to integrate the power electronic converter withits associated electric machinery into a single unit. This thesis, along withits appended papers, considers design and analysis of electric machinery forintegrated electric drives intended for automotive applications. Particular focusis put on permanent-magnet synchronous machines (PMSMs) with interiormountedpermanent magnets combined with modular converter topologies.In the first part of the thesis, different converter concepts and windingarrangements suitable for an integrated drive are reviewed. Compared to theconventional solution utilizing a three-phase two-level converter, a compactintegration can be implemented by physically splitting the converter and itsassociated dc-link capacitor into a number of converter submodules. Moreover,a modular concept also enables a certain level of fault tolerance.In the second part of the thesis, fractional-slot concentrated windings(FSCWs) are analyzed. First, a review for how to determine suitable slot, pole,and phase combinations is identified considering mainly the winding factor forthe main harmonic and the associated rotor losses. Then, integrated modularconverter concepts and associated winding configurations are considered andslot, pole and phase combinations that also comply with the consideredmodular converters are proposed. Further, two possible winding arrangementssuitable for the stacked polyphase bridges (SPB) and the parallel polyphasebridges (PPB) type converter are compared with respect to torque duringpost-fault operation in the event of failure of a single converter submodule.In the third part, an iterative process adopting both finite element analysisand analytical techniques is proposed for the design of PMSMs with interiormountedpermanent magnets and FSCWs. The resulting machine designsillustrate tradeoffs in terms of fault tolerance, power factor, torque density,and potential for field-weakening operation. From a given set of specifications,an experimental prototype is also designed and built.Finally, since a FSCW generally results in a large harmonic content ofthe resulting flux-density waveform, models for predicting eddy-current lossesin the permanent magnets are analyzed and compared. Particularly, modelsadopting different formulations to the Helmholtz equation to solve for the eddycurrents are compared to a simpler model relying on an assumed eddy-currentdistribution. Boundaries in terms of magnet dimensions and angular frequencyare also identified in order to aid the machine designer whether the mostsimple loss model is applicable or not. With a prediction of the eddy-currentlosses in the permanent magnets together with a corresponding thermal model,predicted volumetric loss densities exemplified for combinations of slot andpole numbers common in automotive applications are presented along withthe associated thermal impact. / <p>QC 20170530</p>

This thesis presents a novel system-level synthesis framework called System-Level Architectural Synthesis Framework (SYLVA), which synthesizes DigitalSignal Processing (DSP) sub-systems modeled by synchronous data ?ow intohardware implementations in Application-Specific Integrated Circuit (ASIC),Field-Programmable Gate Array (FPGA) or Coarse-Grained ReconfigurableArchitecture (CGRA) style. SYLVA synthesizes in terms of pre-characterizedFunction Implementations (FIMPs). It explores the design space in threedimensions, number of FIMPs, type of FIMPs, and pipeline parallelism be-tween the producing and consuming FIMPs. SYLVA also introduces timingand interface model of FIMPs to enable reuse and automatic generation ofGlobal Interconnect and Control (GLIC) to glue the FIMPs together into aworking system. SYLVA has been evaluated by applying it to several realand synthetic DSP applications and the experimental results are analyzedfor the design space exploration, the GLIC synthesis, the code generation,and the CGRA floorplanning features. The conclusion from the experimentalresults is that by exploring the multi-dimensional design space in terms ofpre-characterized FIMPs, SYLVA explores a richer design space and does itmore effectively compared to the existing High-Level Synthesis (HLS) toolsto improve both engineering and computational efficiency. / <p>QC 20160125</p>

Several factors affect the perceived quality of a video streamed over a network. Technical aspects related to the codec and the video play an important role besides the delivery of the video content in a timely and error-free manner. If the streamed video frames do not arrive in time, temporal artifacts may become observable during the video playback. This has potentially an adverse effect on the user experience. The study of temporal artifacts during the video playback is the area of concern for Quality of Delivery. The licentiate work investigates the Quality of Delivery for Variable Bit Rate video sent over wireless technologies. The ability of the network to deliver data to the video’s jitter buffer before its playout deadline is studied. Jitter buffer exhaustions are of particular interest as they tell something about the Quality of Delivery. A number of experiments are conducted where a set of videos with different bit-rates are streamed to a mobile device over a wireless LAN and a W-CDMA network. The video streams are recorded and analyzed based on specific Quality of Service parameters and are related to the states of the jitter buffer. The statistical tools Support Vector Machines and the Mahalanobis distance are applied to the parameters to obtain a model that can classify the jitter buffer states. The performance evaluation indicates that the Mahalanobis distance can classify the jitter buffer state marginally better than the Support Vector Machines. However, the Support Vector Machines can produce more reliable predictions compared to the Mahalanobis distance. It is also observed that the perceived Quality of Delivery is not only affected by the behavior of the wireless networks but also by the behavior of the streaming server. Finally, the ability of the Quality of Service parameters to describe the Quality of Delivery is studied. The results indicate that metrics based on the packet arrival-rate and packet inter-arrival time are most suitable in this particular case.

This project deals with E-health web application framework, cloud platform and responsive web design which aim to adjust the presentation on mobile devices. This work presents the whole development process of the self-care management web-app framework which provides instructive supports for future other E-health field application. The report consists of the following main parts: analysis, design and implementation, and evaluation. Literature review and internet search are main methods for making an investigation on existing systems and related works. A prototype is developed by using .Net, CSS3, Java script and HTML5 technologies. The system test and evaluation is made to show the system’s usability.