Konstanz, Univ., Diplomarb., 2001.

Konstanz, Univ., Bachelorarb., 2002.

Zugl.: Darmstadt, Techn. Univ., Diss., 2009

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.

In the field of cell biology, there is an increasing use of time-lapse data to understand cellular function. Using automated microscopes, large numbers of images can be acquired, delivering videos of cell samples over time. Analysing the images manually is extremely time consuming as there are typically thousands of individual images in any given sequence. Additionally, decisions made by those analysing the images, e.g. labelling a mitotic phase (one of a set of distinct sequential stages of cell division) can be subjective, especially around transition boundaries between phases, leading to inconsistencies in the annotation. There is therefore a need for tools which facilitate automated high-throughput analysis. In this thesis we develop systems to automatically detect, track and analyse sub-cellular structures in image sequences to address biological research needs in three areas: (i) Mitotic phase labelling, (ii) Mitotic defect detection, and (iii) Cell volume estimation. We begin by presenting a system for automated segmentation and mitotic phase labelling using temporal models. This work takes the novel approach of using temporal features evaluated over the whole of the mitotic phases rather than over single frames, thereby capturing the distinctive behaviour over the phases. We compare and contrast three different temporal models: Dynamic Time Warping, Hidden Markov Models, and Semi Markov Models. A new loss function is proposed for the Semi Markov model to make it more robust to inconsistencies in data annotation near transition boundaries. We then present an approach for detecting subtle chromosome segregation errors in mitosis in embryonic stem cells, targeting two cases: misaligned chromosomes in a metaphase cell, and lagging chromosomes between anaphase cells. We additionally explore an unsupervised approach to detect unusual mitotic occurrences and test its applicability to detecting misaligned metaphase chromosomes. Finally, we describe a fully automated method, suited to high-throughput analysis, for estimating the volume of spherical mitotic cells based on a learned membrane classifier and a circular Hough transform. We also describe how it is being used further in biological research.

This thesis is concerned with the problem of place recognition for mobile robots. How can a robot determine its location from an image or sequence of images, without any prior knowledge of its position, even in a world where many places look identical? We outline a new probabilistic approach to the problem, which we call Fast Appearance Based Mapping or FAB-MAP. Our map of the environment consists of a set of discrete locations, each with an associated appearance model. For every observation collected by the robot, we compute a probability distribution over the map, and either create a new location or update our belief about the appearance of an existing location. The technique can be seen as a new type of SLAM algorithm, where the appearance of locations (rather than their position) is subject to estimation. Unlike existing SLAM systems, our appearance based technique does not rely on keeping track of the robot in any metric coordinate system. Thus it is applicable even when informative observations are available only intermittently. Solutions to the loop closure detection problem, the kidnapped robot problem and the multi-session mapping problem arise as special cases of our general approach. Abstract Our probabilistic model introduces several technical advances. The model incorporates correlations between visual features in a novel way, which is shown to improve system performance. Additionally, we explicitly compute an approximation to the partition function in our Bayesian formulation, which provides a natural probabilistic measure of when a new observation should be assigned to a location not already present in the map. The technique is applicable even in visually repetitive environments where many places look the same. Abstract Finally, we define two distinct approximate inference procedures for the model. The first of these is based on concentration inequalities and has general applicability beyond the problem considered in this thesis. The second approach, built on inverted index techniques, is tailored to our specific problem of place recognition, but achieves extreme efficiency, allowing us to apply FAB-MAP to navigation problems on the largest scale. The thesis concludes with a visual SLAM experiment on a trajectory 1,000 km long. The system successfully detects loop closures with close to 100% precision and requires average inference time of only 25 ms by the end of the trajectory.

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.