Fault Diagnosis of a Variable-Speed Wind Turbine via Support Vector Machines

In recent years, wind energy is considered as the most practical substitute energy to replace the fossil fuels. Wind turbines are massive and installed in locations, where a non-planned maintenance is very costly. Therefore, a fault-tolerant control system that is able to maintain the wind turbine connected after the occurrence of certain faults can avoid major economic losses. To keep the wind turbine operational or at least safe, in severe cases, a reliable fault diagnosis methodology has to be exploited. It must detect, in the required time, any deviation of the system behaviour from its ordinary case, identify the location and type of the fault and reconfigure the control system to accommodate the so-called discrepancy. To achieve the above goals, a vast number of methods have been suggested by many researchers all around the world. In this thesis, the promising classification framework of the Support Vector Machines is applied to fault detection for variable speed turbines, highlighting its features. In this regard, different fault scenarios are imposed on a benchmark model of a horizontal-axis wind turbine to check the functionality of the mentioned fault detector and the control reconfiguration module

New Fault Tolerant Multicast Routing Techniques to Enhance Distributed-Memory Systems Performance

Distributed-memory systems are a key to achieve high performance computing and the most favorable architectures used in advanced research problems. Mesh connected multicomputer are one of the most popular architectures that have been implemented in many distributed-memory systems. These systems must support communication operations efficiently to achieve good performance. The wormhole switching technique has been widely used in design of distributed-memory systems in which the packet is divided into small flits. Also, the multicast communication has been widely used in distributed-memory systems which is one source node sends the same message to several destination nodes. Fault tolerance refers to the ability of the system to operate correctly in the presence of faults. Development of fault tolerant multicast routing algorithms in 2D mesh networks is an important issue. This dissertation presents, new fault tolerant multicast routing algorithms for distributed-memory systems performance using wormhole routed 2D mesh. These algorithms are described for fault tolerant routing in 2D mesh networks, but it can also be extended to other topologies. These algorithms are a combination of a unicast-based multicast algorithm and tree-based multicast algorithms. These algorithms works effectively for the most commonly encountered faults in mesh networks, f-rings, f-chains and concave fault regions. It is shown that the proposed routing algorithms are effective even in the presence of a large number of fault regions and large size of fault region. These algorithms are proved to be deadlock-free. Also, the problem of fault regions overlap is solved. Four essential performance metrics in mesh networks will be considered and calculated; also these algorithms are a limited-global-information-based multicasting which is a compromise of local-information-based approach and global-information-based approach. Data mining is used to validate the results and to enlarge the sample. The proposed new multicast routing techniques are used to enhance the performance of distributed-memory systems. Simulation results are presented to demonstrate the efficiency of the proposed algorithms

Reservoir Characterization and Flow Simulation for CO 2-EOR in the Tensleep Formation Using Discrete Fracture Networks, Teapot Dome, Wyoming

The Tensleep oil reservoir at Teapot Dome, Wyoming, USA, is a naturally fractured tight sandstone reservoir that has been considered for carbon-dioxide enhanced oil recovery (CO2-EOR) and sequestration. CO2-EOR analysis requires a thorough understanding of the Tensleep fracture network. Wireline image logs from the field suggest that the reservoir fracture network is dominated by early formed structural hinge oblique fractures with interconnectivity enhanced by hinge parallel and hinge perpendicular fracture sets. Available post stack 3D seismic data are used to generate a seismic fracture intensity attribute for the reservoir fracture network. The resulting seismic fracture intensity is qualitatively correlated to the field production history. Wells located on hinge-oblique discontinuities are more productive than other wells in the field. We use Oda\u27s method to upscale the fracture permeabilities in the discrete fracture network for use in a dual porosity fluid flow simulator. We analytically show that Oda\u27s method is sensitive to the grid orientation relative to fracture set strike. Results show that the calculated permeability tensors have maximum geometric mean for the non-zero permeability components (kxx,kyy,kzz,kxy) when the dominant fracture set cuts diagonally through the grid cell at 45° relative to the grid cell principal directions (i,j). The geometric mean of the permeability tensor components falls to a minimum when the dominant fracture set is parallel to either grid wall (i or j principal directions). The latter case has off-diagonal permeability terms close to zero. We oriented the Tensleep reservoir grid to N72°W to minimize the off-diagonal permeability terms. The seismic fracture intensity attribute is then used to generate a realization of the reservoir fracture network. Subsequently, fracture properties are upscaled to the reservoir grid scale for a fully compositional flow simulation. We implemented a PVT analysis using CO2 swelling test results to build an 8 component equation of the state. A fully compositional flow simulation is conducted to acquire a history match between model production and production history. The history matching process reveals that high fracture permeabilities enhance water conning around the producers and decreases the oil production. Moreover, increasing apertures in the model DFN will result in higher oil production from the field. Thus, aperture and vertical permeabilities are adjusted for the model DFN to approximate the production history. We analyzed two CO2-EOR cases with different injection patterns. One has the injectors parallel to the main fracture set and the second one has injectors perpendicular to the main fracture set. Results show that the former model has higher oil recovery with later CO2 breakthrough than the second model. The dominant fracture set (N76°W) affects the CO2-EOR sweep efficiency in the Tensleep reservoir. We show that CO2 breakthrough is inevitable in both cases. The fault transmissibility multipliers are also assumed; they are uncertain parameters that could influence CO2-EOR. The model with completely impermeable faults yields a lower CO2-EOR sweep efficiency compared to the case for which all faults are fully permeable

Fault-tolerant interconnection networks for multiprocessor systems

Interconnection networks represent the backbone of multiprocessor systems. A failure in the network, therefore, could seriously degrade the system performance. For this reason, fault tolerance has been regarded as a major consideration in interconnection network design. This thesis presents two novel techniques to provide fault tolerance capabilities to three major networks: the Baseline network, the Benes network and the Clos network. First, the Simple Fault Tolerance Technique (SFT) is presented. The SFT technique is in fact the result of merging two widely known interconnection mechanisms: a normal interconnection network and a shared bus. This technique is most suitable for networks with small switches, such as the Baseline network and the Benes network. For the Clos network, whose switches may be large for the SFT, another technique is developed to produce the Fault-Tolerant Clos (FTC) network. In the FTC, one switch is added to each stage. The two techniques are described and thoroughly analyzed

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Relative Geologic Time By Dynamic Time Warping

This thesis considers an approach to tackle a core problem within seismic interpretation, which is bringing an autonomously generated interpretation of the seismic data, which is now known as a Relative Geologic Time. The proposed method readily utilizes the method of Dynamic Time Warping, which is an established method within signal processing. Using Dynamic Time Warping is thought to replicate similar interpretations an interpreter would conduct when fulfilling an interpretation of the subsurface. Utilizing Dynamic Time Warping to seismic data results in a fully autonomous interpretation of the subsurface, conducted in minutes and seconds. The method is simple and extendable, which can easily be further expanded. The workflow established during the thesis work results in a method that successfully produces an RGT volume. However, problems related to the method must be improved to enhance the outcome further and diminish errors present in the result. Furthermore, even with problems associated with the method, potential solutions are described in detail in the discussion and appendix. Discussion affiliated with previous attempts in solving Relative Geologic Time volumes is emphasized. The research conducted in Dynamic Time Warping is promising and emits potential for further research. LaTeX setup by Gunn and Patel (2017)