This thesis is focused on a design and an implementation of a power consumption computation for a passenger vessel using the industrial automation system Siemens Simatic S7-200 and the touch panel Weintek Easy View MT8150X. A part of the thesis represents a theoretical summary of features and parametres of the programmable logic controllers (PLC) and a way how they are programmed. The work includes a description of a computational algorithm, a block diagram of interconnections and a visualization software for testing. A greater part of this publication is devoted to a detailed decription of a program in the PLC and its visualization, especially the power consumption computation, a distance, an average speed and a wind speed and a direction. At the end, the thesis shows a procedure for an implementation and a measurement on the passenger ships at the Brno dam, including an evaluation of downloaded data.

This thesis deals with conception of proposal and implementation of a weather station. Weather station is composed of three main parts which are measuring station, showing station and USB wireless receiving module. Measuring station measures basic meteorologic variables and sends measured variables through wireless communication. Showing station is designed to present measured data with graphics user interface and generate data for a web server.

The recent worldwide economic crisis, climate change and global warming have emphasized that the need for low carbon emissions while also ensuring the economic feasibility. In this paper, wind power potential of ETD in KTH was investigated. The technical and economical feasibility of tower mounted small scale standalone wind turbine installation is conducted. The potential of wind power production was statistically analysed. The average wind speed data of four-season interval of one year period (2011) which its measurement was taken on the roof top of the ETDB, and this was adopted and analysed in order to find out the potential of wind power generation. The Rayleigh distribution probability was applied to calculate the wind speed distribution at KTH, by doing so the annual wind power potential at the area and annual energy production of the chosen wind turbine was estimated, after the selection of a proper wind turbine have been made upon the site conditions. Therefore, the study result shows that installation of the wind turbine at 24 meters hub height for this particular area will have a better performance of annual energy production, capacity factor, carbon savings and better economical value than the current turbine installed at 17 meters height at the ETD. The economic evaluation shows that the turbine can save an electricity bill of US$3661.05 per year and cover 1.84% of the electricity consumption of the ETD by reducing its respective CO2 emission from the electricity use at the department. Moreover, the payback period of the turbine installation with the inclusion of the green certificate is approximately 14 years which is more feasible if it is considered for small wind turbines too, which is already in practice for renewables including wind power in Sweden.

Although generally accurate, the quality of SeaWinds on QuikSCAT scatterometer ocean vector winds is compromised by certain natural phenomena and retrieval algorithm limitations. This dissertation addresses three main contributers to scatterometer estimate error: poor ambiguity selection, estimate uncertainty at low wind speeds, and rain corruption. A quality assurance (QA) analysis performed on SeaWinds data suggests that about 5% of SeaWinds data contain ambiguity selection errors and that scatterometer estimation error is correlated with low wind speeds and rain events. Ambiguity selection errors are partly due to the "nudging" step (initialization from outside data). A sophisticated new non-nudging ambiguity selection approach produces generally more consistent wind than the nudging method in moderate wind conditions. The non-nudging method selects 93% of the same ambiguities as the nudged data, validating both techniques, and indicating that ambiguity selection can be accomplished without nudging. Variability at low wind speeds is analyzed using tower-mounted scatterometer data. According to theory, below a threshold wind speed, the wind fails to generate the surface roughness necessary for wind measurement. A simple analysis suggests the existence of the threshold in much of the tower-mounted scatterometer data. However, the backscatter does not "go to zero" beneath the threshold in an uncontrolled environment as theory suggests, but rather has a mean drop and higher variability below the threshold. Rain is the largest weather-related contributer to scatterometer error, affecting approximately 4% to 10% of SeaWinds data. A simple model formed via comparison of co-located TRMM PR and SeaWinds measurements characterizes the average effect of rain on SeaWinds backscatter. The model is generally accurate to within 3 dB over the tropics. The rain/wind backscatter model is used to simultaneously retrieve wind and rain from SeaWinds measurements. The simultaneous wind/rain (SWR) estimation procedure can improve wind estimates during rain, while providing a scatterometer-based rain rate estimate. SWR also affords improved rain flagging for low to moderate rain rates. QuikSCAT-retrieved rain rates correlate well with TRMM PR instantaneous measurements and TMI monthly rain averages. SeaWinds rain measurements can be used to supplement data from other rain-measuring instruments, filling spatial and temporal gaps in coverage.

Optimizing the operation and maintenance of wind turbines is crucial as the wind energy sector continues to expand. Predicting the mass imbalance of wind turbines, which can seriously damage the rotor blades, gearbox, and other components, is one of the key issues in this field. In this work, we propose a machine learning-based method for predicting the mass imbalance of wind turbines utilizing information from multiple sensors and monitoring systems. We collected data and trained the model from Adwen AD8 wind turbine model and evaluated on the real wind turbine SCADA data which is located at Fraunhofer IWES, Bremerhaven. The data included various parameters such as wind speed, blade root bending moments and rotor speed. We used this data to train and test machine learning classification models based on different algorithms, including extra-tree classifiers, support vector machines, and random forest. Our results showed that the machine learning models were able to predict the mass imbalance percentage of wind turbines with high accuracy. Particularly, the extra tree classifiers with blade root bending moments outperformed other research for multiclassification problem with an F1 score of 0.91 and an accuracy of 90%. Additionally, we examined the significance of various features in predicting the mass imbalance and observed that the rotor speed and blade root bending moments were the most crucial variables. Our research has significant effects for the wind energy sector since it offers a reliable and efficient way for predicting wind turbine mass imbalance. Wind farm operators can save maintenance costs, minimize downtime of wind turbines, and increase the lifespan of turbine components by identifying and eliminating mass imbalances. Also, further investigation will allow us to apply our method to different kinds of wind turbines, and it is simple to incorporate into current monitoring systems as it supports prediction without installing additional sensors. In conclusion, our study demonstrates the potential of machine learning for predicting the percentage of mass imbalance of wind turbines. We believe that our approach can significantly benefit the wind energy industry and contribute to the development of sustainable energy sources.

Long-term omni-directional ambient noise was collected at several sites in the Gulf of Mexico during 2004 and 2005. The Naval Oceanographic Office deployed bottom moored Environmental Acoustic Recording System (EARS) buoys approximately 159 nautical miles south of Panama City, Florida, in water depths of 3200 meters. The hydrophone of each buoy was 265 meters above the bottom. The data duration ranged from 10-14 months. The buoys were located near a major shipping lane, with an estimated 1.5 to 4.5 ships per day passing nearby. The data were sampled at 2500 Hz and have a bandwidth of 10-1000 Hz. Data are processed in eight 1/3-octave frequency bands, centered from 25 to 950 Hz, and monthly values of the following statistical quantities are computed from the resulting eight time series of noise spectral level: mean, median, standard deviation, skewness, kurtosis and coherence time. Four hurricanes were recorded during the summer of 2004 and they have a major impact on all of the noise statistics. Noise levels at higher frequencies (400-950 Hz) peak during extremely windy months (summer hurricanes and winter storms). Standard deviation is least in the region 100-200 Hz but increases at higher frequencies, especially during periods of high wind variability (summer hurricanes). Skewness is positive from 25-400 Hz and negative from 630-950 Hz. Skewness and kurtosis are greatest near 100 Hz. Coherence time is low in shipping bands and high in weather bands, and it peaks during hurricanes. The noise coherence is also analyzed. The 14-month time series in each 1/3- octave band is highly correlated with other 1/3-octave band time series ranging from 2 octaves below to 2 octaves above the band's center frequency. Spatial coherence between hydrophones is also analyzed for hydrophone separations of 2.29, 2.56 and 4.84 km over a 10-month period. The noise field is highly coherent out to the maximum distance studied, 4.84 km. Additionally, fluctuations of each time series are analyzed to determine time scales of greatest variability. The 14-month data show clearly that variability occurs primarily over three time scales: 7-22 hours (shipping-related), 56-282 hours (2-12 days, weather-related) and over an 8-12 month period.

Impulsionado pela mecânica de leilões de energia, o aproveitamento energético de recursos eólicos no Brasil atravessa um momento de expansão em participação na matriz de energia elétrica nacional. Não obstante, o desempenho da geração dos parques eólicos que estão em operação foi monitorado e apresentou, em média, resultados aquém daquilo que fora confiado ao Sistema Interligado Nacional, revelando que as estimativas de geração projetadas e declaradas por alguns dos projetos vencedores dos processos licitatórios podem ter sido supervalorizadas. Tal cenário provocou a exigência de medidas mais conservadoras para participação nos leilões de energia, como a já vigente adoção do P90 no cálculo da Garantia Física e o aumento da duração da campanha de medição anemométrica, a entrar em rigor a partir de 2017. Sendo o vento uma variável estocástica, existem incertezas intrínsecas à Avaliação de Recursos Eólicos que influenciam no processo de estimação da geração por um parque eólico e que devem, desta forma, ser identificadas, quantificadas e reduzidas, na medida do possível. Nesse sentido, este trabalho estuda a influência da duração da campanha de medição anemométrica na Avaliação de Recursos Eólicos com base na aplicação do método MCP ferramenta imprescindível no processo de caracterização do regime eólico no longo prazo com vistas para aprimorar a exatidão das previsões de geração pela fonte eólica. Para tanto, foram utilizadas quatro bases de dados contendo séries temporais de velocidade e direção do vento referentes a uma região de interesse. Inicialmente, nove diferentes métodos MCP foram testados e comparados, sendo que o método Vertical Slice aplicado com auxílio do software Windographer destacou-se dos demais e mostrou-se mais aderente aos dados utilizados conforme as métricas de Erro Absoluto Médio e Raiz Quadrada do Erro Quadrático Médio. Posteriormente, as bases de dados foram configuradas para simular campanhas de medição anemométricas com durações que variavam de 2 a 6 anos, de modo a avaliar o comportamento da incerteza relativa à caracterização histórica de recursos eólicos e analisar em que medida esta incerteza impacta no cálculo da estimativa de geração de eletricidade por um conjunto de aerogeradores hipoteticamente dispostos naquele local de interesse. Foi possível verificar que, para os dados e casos analisados, à medida que se aumentou a duração da campanha de medição anemométrica, a incerteza da caracterização histórica de recursos eólicos sofreu queda significativa; determinando, por conseguinte, redução da incerteza total que permeia a geração eólica. Ademais, a quantidade de energia estimada para o parque eólico hipotético exemplificado também decresceu, permitindo melhora na acurácia da previsão de geração e beneficiando a confiabilidade da fonte eólica no sistema elétrico brasileiro. / Driven by the energy auctions system, the energetic harnessing of wind resource in Brazil is now going through a phase of expansion in participation in the national electric energy mix. Nevertheless, the performance of power generation of in-operation wind farms was monitored and the results proved to be, on average, below what was initially entrusted to the National Grid System, indicating that the energy production estimations projected by some energy auctions winners could have been overestimated. This scenario has caused the requirements for participating in the energy auctions to be more conservative, with measures such as the adoption of the P90 on the calculation of the physical guarantee and the increase of the wind measurement campaigns time span the latter to be enforced as of 2017. The wind is a stochastic resource, hence there are uncertainties intrinsic to the Wind Resource Assessment that influence a wind farms power generation estimation and that need to be properly identified, quantified and reduced, as far as possible. In this respect, the influence of a wind measurement campaigns time span on the Wind Resource Assessment based on MCP methods an important tool in the process of characterizing the long-term wind regime was studied in order to detect the potential of enhancing the accuracy of wind power generation forecasts. For this purpose, four databases containing time series of wind speed and direction belonging to a target site were used. Firstly, nine different MCP methods were tested and compared, of which the Vertical Slice method implemented on the software Windographer outperformed all the others according to the Mean Absolute Error and Root Mean Square Error metrics. Subsequently, the databases were set to simulate campaigns with time spans varying from 2 to 6 years, in such a way to evaluate the behavior of the uncertainty in the long-term wind speed and to analyze how this uncertainty impacts the calculation of the energy production estimation of an array of wind turbines hypothetically placed on that target site. From the analyzed data and cases, it was verified that, as the wind measurement campaigns time span was increased, the uncertainty in the long-term wind speed was significantly diminished, thereby reducing the overall uncertainty that pervades the wind power harnessing. Furthermore, the energy production estimation of the exemplified hypothetical wind farm also decreased, allowing an improvement on the accuracy of the energy generation prediction and benefiting the reliability of wind power in the Brazilian electric system.

Studien handlade om att belysa hur UAV:s kan komma till nytta i meteorologisk forskning och att ta reda på hur bra drönare är på att mäta meteorologiska storheter. Drönare, som också kallas UAV:s (Unmanned Aerial Vehicle) är mindre obemmanade luftfartyg som kan flyga autonomt eller fjärrstyras. Idag används drönare alltmer i meteorologi vilket beror mycket på den tekniska utvecklingen. Exempel på meteorologiska applikationer är mätning av vindhastighet och koncentrationen av koldioxid i luften som kan användas för att studera de lägsta atmosfäriska skikten. Storheter som mättes i detta projekt var vindhastighet, vindriktning, temperatur och relativ fuktighet på olika höjder. Mätningarna gjordes två gånger i en mätstation i Marsta, som ligger utanför Uppsala. Efteråt jämfördes datan från drönaren med data från en instrumenterad mast vid väderstationen. Resultaten visade att drönarens vindprofiler stämde ganska väl överens med mastens vindprofiler och den logaritmiska vindlagen. Under den första fältmätningen uppskattade drönaren att vindhastigheten var 8.13 ± 1.33 m/s vid hovring på tio meters höjd medan masten angav 8.41 ± 0.958 m/s. Drönarens mätvärden för vindriktningen var ibland bra och ibland mindre bra. Mätvärdena för temperaturen och den relativa fuktigheten avvek med upp mot 1 ◦C respektive 10 procentenheter. / The aim of this report was to study how UAV:s can be applied in meteorological research and find out how good drones are at measuring meteorological parameters. A drone, also known as an UAV (Unmanned Aerial Vehicle) is a smaller unmanned aircraft that can fly autonomously or under remote control. Today, drones are used more frequently in meteorology, mostly due to the recent technological development. Examples of meteorological applications include measurements of wind speed and the amount of carbon dioxide in the air which can be used to analyze the lower parts of the atmosphere. In this project, the wind speed and its direction, the temperature and the relative humidity were measured at different heights. The measurements took place twice in Marsta which is a field station outside Uppsala. Then, the data from the drone was compared to the data from a weather tower at the instrumented station. The results showed that the drone’s wind profiles were relatively similar to the profiles according to the tower and the logarithmic wind profiles. For instance, during the first flight the drone estimated the wind speed to be 8.13 ± 1.33 m/s while hovering ten metres above the ground. The tower measured 8.41 ± 0.958 m/s at the same height. The drone’s estimations of the wind direction were sometimes accurate and sometimes not accurate. The temperature and relative humidity however was different by 1 ◦C and ten percentage units respectively.

This study attempted to explain an apparent inverse relationship between pack ice proximity and breeding success of Black Guillemots (Cepphus grylle) on Cooper Island, a barrier island in the western Beaufort Sea near Barrow, AK. I elucidated the first linear relationship between energy density and body size for the elusive Arctic Cod (Boreogadus saida). I discovered and ground-truthed the existence of previously unknown guillemot foraging habitat on small 50 m2 ice floes distant from the pack ice. I developed new daily metrics for quantifying the provisioning to linear (8 d – 18 d) and Post-Linear (19 d – fledge) chicks. I found daily consensus between Linear and Post-Linear chicks about the level of provisioning at the colony. Finally, I explained those daily changes with significant correlations with wind speed and direction. / October 2007

This study attempted to explain an apparent inverse relationship between pack ice proximity and breeding success of Black Guillemots (Cepphus grylle) on Cooper Island, a barrier island in the western Beaufort Sea near Barrow, AK. I elucidated the first linear relationship between energy density and body size for the elusive Arctic Cod (Boreogadus saida). I discovered and ground-truthed the existence of previously unknown guillemot foraging habitat on small 50 m2 ice floes distant from the pack ice. I developed new daily metrics for quantifying the provisioning to linear (8 d – 18 d) and Post-Linear (19 d – fledge) chicks. I found daily consensus between Linear and Post-Linear chicks about the level of provisioning at the colony. Finally, I explained those daily changes with significant correlations with wind speed and direction.