Nonlinear State Estimation and Noise Adaptive Kalman Filter Design for Wind Turbines
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Bester Preis: € 34,86 (vom 02.07.2020)1
Nonlinear State Estimation and Noise Adaptive Kalman Filter Design for Wind Turbines
~EN PB NWISBN: 9783752965452 bzw. 3752965452, vermutlich in Englisch, Epubli, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Nonlinear State Estimation and Noise Adaptive Kalman Filter Design for Wind Turbines: Modern wind turbines have grown significantly over the last three decades and will probably do so in the future. Additionally, more and more turbines are mounted in shallow waters on offshore monopiles and also on floating platforms in the deep sea. These technological developments implicate that the complex structural composition of the turbines becomes more flexible and simultaneously more and competing control objectives appear above the horizon. In this context, advanced state-feedback based control schemes have emerged in the wind sector in order to tackle these new challenges effectively though requiring the mostly hidden and immeasurable information about the dynamic state of the wind turbine. In order to obtain this valuable information without additional measurements and sensors, the present thesis bridges the scientific gap between the nonlinear estimation theory on the one hand and the practical application to wind turbine control systems on the other hand. This approach includes the investigation of the nonlinear filter algorithms, the control-oriented physical models and the design methodology needed to make nonlinear state estimation techniques ready for wind turbine application. The results of this approach are so-called virtual (model-based) sensors that are employed for multiple estimation tasks, such as the observation of unknown disturbances and the online reconstruction of mechanical loads. These sensors are applicable whenever it is impossible or too expensive to measure the desired quantities directly. This book provides the theoretical foundations, the practical application and also the simulative proof of concept in order to bring wind turbine state estimation successfully into practice. Thereby, these virtual sensors shall level the ground not only for advanced state-feedback control, but also provide further insight into the system`s internal behavior. Englisch, Taschenbuch.
Nonlinear State Estimation and Noise Adaptive Kalman Filter Design for Wind Turbines: Modern wind turbines have grown significantly over the last three decades and will probably do so in the future. Additionally, more and more turbines are mounted in shallow waters on offshore monopiles and also on floating platforms in the deep sea. These technological developments implicate that the complex structural composition of the turbines becomes more flexible and simultaneously more and competing control objectives appear above the horizon. In this context, advanced state-feedback based control schemes have emerged in the wind sector in order to tackle these new challenges effectively though requiring the mostly hidden and immeasurable information about the dynamic state of the wind turbine. In order to obtain this valuable information without additional measurements and sensors, the present thesis bridges the scientific gap between the nonlinear estimation theory on the one hand and the practical application to wind turbine control systems on the other hand. This approach includes the investigation of the nonlinear filter algorithms, the control-oriented physical models and the design methodology needed to make nonlinear state estimation techniques ready for wind turbine application. The results of this approach are so-called virtual (model-based) sensors that are employed for multiple estimation tasks, such as the observation of unknown disturbances and the online reconstruction of mechanical loads. These sensors are applicable whenever it is impossible or too expensive to measure the desired quantities directly. This book provides the theoretical foundations, the practical application and also the simulative proof of concept in order to bring wind turbine state estimation successfully into practice. Thereby, these virtual sensors shall level the ground not only for advanced state-feedback control, but also provide further insight into the system`s internal behavior. Englisch, Taschenbuch.
2
Nonlinear State Estimation and Noise Adaptive Kalman Filter Design for Wind Turbines
~EN NW ABISBN: 9783752965452 bzw. 3752965452, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Schweiz, Lieferzeit: 2 Tage, zzgl. Versandkosten.
Modern wind turbines have grown significantly over the last three decades and will probably do so in the future. Additionally, more and more turbines are mounted in shallow waters on offshore monopiles and also on floating platforms in the deep sea. These technological developments implicate that the complex structural composition of the turbines becomes more flexible and simultaneously more and competing control objectives appear above the horizon. In this context, advanced state-feedback based control schemes have emerged in the wind sector in order to tackle these new challenges effectively though requiring the mostly hidden and immeasurable information about the dynamic state of the wind turbine. In order to obtain this valuable information without additional measurements and sensors, the present thesis bridges the scientific gap between the nonlinear estimation theory on the one hand and the practical application to wind turbine control systems on the other hand. This approach includes the investigation of the nonlinear filter algorithms, the control-oriented physical models and the design methodology needed to make nonlinear state estimation techniques ready for wind turbine application. The results of this approach are so-called virtual (model-based) sensors that are employed for multiple estimation tasks, such as the observation of unknown disturbances and the online reconstruction of mechanical loads. These sensors are applicable whenever it is impossible or too expensive to measure the desired quantities directly.This book provides the theoretical foundations, the practical application and also the simulative proof of concept in order to bring wind turbine state estimation successfully into practice. Thereby, these virtual sensors shall level the ground not only for advanced state-feedback control, but also provide further insight into the system’s internal behavior.
Modern wind turbines have grown significantly over the last three decades and will probably do so in the future. Additionally, more and more turbines are mounted in shallow waters on offshore monopiles and also on floating platforms in the deep sea. These technological developments implicate that the complex structural composition of the turbines becomes more flexible and simultaneously more and competing control objectives appear above the horizon. In this context, advanced state-feedback based control schemes have emerged in the wind sector in order to tackle these new challenges effectively though requiring the mostly hidden and immeasurable information about the dynamic state of the wind turbine. In order to obtain this valuable information without additional measurements and sensors, the present thesis bridges the scientific gap between the nonlinear estimation theory on the one hand and the practical application to wind turbine control systems on the other hand. This approach includes the investigation of the nonlinear filter algorithms, the control-oriented physical models and the design methodology needed to make nonlinear state estimation techniques ready for wind turbine application. The results of this approach are so-called virtual (model-based) sensors that are employed for multiple estimation tasks, such as the observation of unknown disturbances and the online reconstruction of mechanical loads. These sensors are applicable whenever it is impossible or too expensive to measure the desired quantities directly.This book provides the theoretical foundations, the practical application and also the simulative proof of concept in order to bring wind turbine state estimation successfully into practice. Thereby, these virtual sensors shall level the ground not only for advanced state-feedback control, but also provide further insight into the system’s internal behavior.
3
Nonlinear State Estimation and Noise Adaptive Kalman Filter Design for Wind Turbines
~EN NWISBN: 9783752965452 bzw. 3752965452, vermutlich in Englisch, Epubli, neu.
Lieferung aus: Österreich, Lieferzeit 1-3 Werktage, Versandkostenfrei innerhalb von Deutschland.
Modern wind turbines have grown significantly over the last three decades and will probably do so in the future. Additionally, more and more turbines are mounted in shallow waters on offshore monopiles and also on floating platforms in the deep sea. These technological developments implicate that the complex structural composition of the turbines becomes more flexible and simultaneously more and competing control objectives appear above the horizon. In this context, advanced state-feedback based control schemes have emerged in the wind sector in order to tackle these new challenges effectively though requiring the mostly hidden and immeasurable information about the dynamic state of the wind turbine. In order to obtain this valuable information without additional measurements and sensors, the present thesis bridges the scientific gap between the nonlinear estimation theory on the one hand and the practical application to wind turbine control systems on the other hand. This approach includes the investigation of the nonlinear filter algorithms, the control-oriented physical models and the design methodology needed to make nonlinear state estimation techniques ready for wind turbine application. The results of this approach are so-called virtual (model-based) sensors that are employed for multiple estimation tasks, such as the observation of unknown disturbances and the online reconstruction of mechanical loads. These sensors are applicable whenever it is impossible or too expensive to measure the desired quantities directly. This book provides the theoretical foundations, the practical application and also the simulative proof of concept in order to bring wind turbine state estimation successfully into practice. Thereby, these virtual sensors shall level the ground not only for advanced state-feedback control, but also provide further insight into the system's internal behavior.
Modern wind turbines have grown significantly over the last three decades and will probably do so in the future. Additionally, more and more turbines are mounted in shallow waters on offshore monopiles and also on floating platforms in the deep sea. These technological developments implicate that the complex structural composition of the turbines becomes more flexible and simultaneously more and competing control objectives appear above the horizon. In this context, advanced state-feedback based control schemes have emerged in the wind sector in order to tackle these new challenges effectively though requiring the mostly hidden and immeasurable information about the dynamic state of the wind turbine. In order to obtain this valuable information without additional measurements and sensors, the present thesis bridges the scientific gap between the nonlinear estimation theory on the one hand and the practical application to wind turbine control systems on the other hand. This approach includes the investigation of the nonlinear filter algorithms, the control-oriented physical models and the design methodology needed to make nonlinear state estimation techniques ready for wind turbine application. The results of this approach are so-called virtual (model-based) sensors that are employed for multiple estimation tasks, such as the observation of unknown disturbances and the online reconstruction of mechanical loads. These sensors are applicable whenever it is impossible or too expensive to measure the desired quantities directly. This book provides the theoretical foundations, the practical application and also the simulative proof of concept in order to bring wind turbine state estimation successfully into practice. Thereby, these virtual sensors shall level the ground not only for advanced state-feedback control, but also provide further insight into the system's internal behavior.
4
Nonlinear State Estimation and Noise Adaptive Kalman Filter Design for Wind Turbines
~EN PB NWISBN: 3752965452 bzw. 9783752965452, vermutlich in Englisch, epubli, Taschenbuch, neu.
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