Wind Turbine Controls for Farm and Offshore Operation

Development of advanced control techniques is a critical measure for reducing the cost of energy for wind power generation, in terms of both enhancing energy capture and reducing fatigue load. There are two remarkable trends for wind energy. First, more and more large wind farms are developed in order to reduce the unit-power cost in installation, operation, maintenance and transmission. Second, offshore wind energy has received significant attention when the scarcity of land resource has appeared to be a major bottleneck for next level of wind penetration, especially for Europe and Asia. This dissertation study investigates on several wind turbine control issues in the context of wind farm and offshore operation scenarios. Traditional wind farm control strategies emphasize the effect of the deficit of average wind speed, i.e. on how to guarantee the power quality from grid integration angle by the control of the electrical systems or maximize the energy capture of the whole wind farm by optimizing the setting points of rotor speed and blade pitch angle, based on the use of simple wake models, such as Jensen wake model. In this study, more complex wake models including detailed wind speed deficit distribution across the rotor plane and wake meandering are used for load reduction control of wind turbine. A periodic control scheme is adopted for individual pitch control including static wake interaction, while for the case with wake meandering considered, both a dual-mode model predictive control and a multiple model predictive control is applied to the corresponding individual pitch control problem, based on the use of the computationally efficient quadratic programming solver qpOASES. Simulation results validated the effectiveness of the proposed control schemes. Besides, as an innovative nearly model-free strategy, the nested-loop extremum seeking control (NLESC) scheme is designed to maximize energy capture of a wind farm under both steady and turbulent wind. The NLESC scheme is evaluated with a simple wind turbine array consisting of three cascaded variable-speed turbines using the SimWindFarm simulation platform. For each turbine, the torque gain is adjusted to vary/control the corresponding axial induction factor. Simulation under smooth and turbulent winds shows the effectiveness of the proposed scheme. Analysis shows that the optimal torque gain of each turbine in a cascade of turbines is invariant with wind speed if the wind direction does not change, which is supported by simulation results for smooth wind inputs. As changes of upstream turbine operation affects the downstream turbines with significant delays due to wind propagation, a cross-covariance based delay estimate is proposed as adaptive phase compensation between the dither and demodulation signals. Another subject of investigation in this research is the evaluation of an innovative scheme of actuation for stabilization of offshore floating wind turbines based on actively controlled aerodynamic vane actuators. For offshore floating wind turbines, underactuation has become a major issue and stabilization of tower/platform adds complexity to the control problem in addition to the general power/speed regulation and rotor load reduction controls. However, due to the design constraints and the significant power involved in the wind turbine structure, a unique challenge is presented to achieve low-cost, high-bandwidth and low power consumption design of actuation schemes. A recently proposed concept of vertical and horizontal vanes is evaluated to increase damping in roll motion and pitch motion, respectively. The simulation platform FAST has been modified including vertical and horizontal vane control. Simulation results validated the effectiveness of the proposed vertical and horizontal active vane actuators

Wind energy-harvesting technologies and recent research progresses in wind farm control models

In order to sustain the overall competitiveness of the wind power industry, unrelenting focus is required on working toward the advancement of enabling technologies and research studies that are associated with wind farm systems. First, wind farm technologies that include various turbine generator systems coupled with different power transmission configurations have enormous impact in determining the quality of wind power production. In addition, modern wind farms are expected to implement robust power control algorithms to meet more advanced requirements of electricity generation. Accordingly, this study explores the statuses of wind energy harvesting technologies and wind farm control strategies by discussing their recent and future impact on transforming the wind power industry. Doubly fed induction generator (DFIG)-based wind energy harvesting technology is well-matured and has exhibited an excellent track-record in past and recent experiences, but its capability of being further scalable for large-scale power production is limited as it is largely incompatible with high-voltage power transmission networks. On the other hand, permanent magnet synchronous generator (PMSG)-based technology is making significant advancements to attain the maximum possible efficiency level in greatly facilitating larger scale power generation, although the construction of bulky and costly power transmission systems is required. In this regard, future technological advances in the wind farm industry are expected to reasonably optimize the design and cost of high-voltage power transmission systems. Similarly, an increasing number of research studies are introducing a number of power optimization-based control models to create an ideal integration of the aforementioned wind farm technologies so as to ultimately enhance the reliability of electricity production by maintaining the systems’ safety. Yet, additional work is still expected to be undertaken in the future for a more extended evaluation of the performances of many different control models under a similar environment

Empowering wave energy with control technology: Possibilities and pitfalls

With an increasing focus on climate action and energy security, an appropriate mix of renewable energy technologies is imperative. Despite having considerable global potential, wave energy has still not reached a state of maturity or economic competitiveness to have made an impact. Challenges include the high capital and operational costs associated with deployment in the harsh ocean environment, so it is imperative that the full energy harnessing capacity of wave energy devices, and arrays of devices in farms, is realised. To this end, control technology has an important role to play in maximising power capture, while ensuring that physical system constraints are respected, and control actions do not adversely affect device lifetime. Within the gamut of control technology, a variety of tools can be brought to bear on the wave energy control problem, including various control strategies (optimal, robust, nonlinear, etc.), data-based model identification, estimation, and forecasting. However, the wave energy problem displays a number of unique features which challenge the traditional application of these techniques, while also presenting a number of control ‘paradoxes’. This review articulates the important control-related characteristics of the wave energy control problem, provides a survey of currently applied control and control-related techniques, and gives some perspectives on the outstanding challenges and future possibilities. The emerging area of control co-design, which is especially relevant to the relatively immature area of wave energy system design, is also covered

Analysis of Atmospheric Planetary Boundary Layer - Terrain Interactions. Wind Industry Implications

The development of wind energy has a direct effect on the reduction of carbon dioxide emissions from the energy sector. This industry is one of the largest anthropogenic contributors to the global problem of climate change. Numerical modeling is a tool that forms part of the present and future of this sector; because it is able to reproduce the effect of wind farms on the atmosphere and to obtain its short-term production prediction. The present thesis aims to achieve a detailed quantification and understanding of the main interactions between atmospheric planet boundary layer and terrain, focusing on the behavior of wind flows at different scales

Advanced wind farm control strategies for enhancing grid support

Nowadays, there is rising concern among Transmission System Operators about the declining of system inertia due to the increasing penetration of wind energy, and other renewable energy systems, and the retirements of conventional power plants. On the other hand, by properly operating wind farms, wind generation may be capable of enhancing grid stability and ensuring continued security of power supply. In this doctoral thesis, new control approaches for designing wind farm optimization-based control strategies are proposed to improve the participation of wind farms in grid support, specially in primary frequency support.Hoy en día, existe una significativa preocupación entre los Operadores de Sistemas de Transmisión sobre la cresciente penetración de le energía eólica y la tendiente eliminación de las centrales eléctricas convencionales que implica la disminución de la inercia del sistema eléctrico. Operando adecuadamente los parques eólicos, la generación eólica puede mejorar la estabilidad de la red eléctrica y garantizar la seguridad y un continuo suministro de energía. Esta tesis doctoral propone nuevas estrategias para diseñar controladores basados en optimización dinámica para parques eólicos y mejorar la participación de los parques eólicos en el soporte de la red eléctrica. En primer lugar, esta tesis doctoral presenta los enfoques clásicos para el control de parques y turbinas eólicas y cómo los conceptos de control existentes pueden ser explotados para hacer frente a los nuevos desafíos que se esperan de los parques eólicos. Esta tesis doctoral asigna un interés especial a cómo formular la función objetivo de que la reserva de potencia sea maximizada, para ayudar por el suporte de frequencia, y al mismo tiempo seguir la potencia demandada por la red. Sin embargo, el impacto de la estela de viento generada por una turbina sobre otras turbinas necesita ser minimizado para mejorar la reserva de potencia. Por lo tanto, a lo largo de esta tesis se proponen estrategias de control centralizado para parques eólicos enfocadas en distribuir óptimamente la energía entre las turbinas para que el impacto negativo de la estela en la reserva de potencia total se reduzca. Se discuten dos técnicas de control para proporcionar los objetivos de control mencionados anteriormente. Un algoritmo de control óptimo para encontrar la mejor distribución de potencia entre las turbinas en el parque mientras se resuelve un problema iterativo de programación lineal. En segundo lugar, se utiliza la técnica de control predictivo basada en modelo para resolver un problema de control multi-objetivo que también podría incluir, junto con la maximización de reserva de potencia, otros objetivos de control, tales como la minimización de las perdidas eléctricas en los cables de la red de interconexión entre turbinas y un controlador/supervisor. Además, la investigación realizada resalta la capacidad de las estrategias de control propuestas en esta tesis para proporcionar mayor reserva de potencia respecto a los conceptos comúnmente usados para distribuir la potencia total del parque eólico. La idea principal detrás del diseño de una estrategia de control de parques eólico es de encontrar una solución óptima dentro de un cálculo computacional relativamente bajo. Aunque los controladores centralizados propuestos en esta tesis reaccionan rápidamente a los cambios en la potencia de referencia enviada desde el controlador, algunos problemas pueden ocurrir cuando se consideran parques eólicos de gran escala debido a los retrasos existentes por el viento entre turbinas. Bajo estas circunstancias, la producción de energía de cada turbina está altamente acoplada con la propagación de la estela y, por ende, con las condiciones de funcionamiento de las otras turbinas. Esta tesis doctoral propone un esquema de control de parques eólicos no centralizados basado en una estrategia de partición para dividir el parque eólico en sub-conjuntos independientes de turbinas. Con la estrategia de control propuesta, el tiempo de cálculo se reduce adecuadamente en comparación con la estrategia de control centralizado mientras que el rendimiento en la distribución óptima de potencia es ligeramente afectado. El rendimiento de todas las estrategias de control propuestas en esta tesis se prueba con un simulador de parque eólico que modela el comportamiento dinámico del efecto de estela mediante el uso de un conocido y consolidado modelo dinámico de estela y, para un análisis más realista, algunas simulaciones se realizan con software avanzado basado en la técnica de Large Eddy Simulation

Advanced wind farm control strategies for enhancing grid support

Aplicat embargament des de la data de defensa fins al maig 2020Nowadays, there is rising concern among Transmission System Operators about the declining of system inertia due to the increasing penetration of wind energy, and other renewable energy systems, and the retirements of conventional power plants. On the other hand, by properly operating wind farms, wind generation may be capable of enhancing grid stability and ensuring continued security of power supply. In this doctoral thesis, new control approaches for designing wind farm optimization-based control strategies are proposed to improve the participation of wind farms in grid support, specially in primary frequency support.Hoy en día, existe una significativa preocupación entre los Operadores de Sistemas de Transmisión sobre la cresciente penetración de le energía eólica y la tendiente eliminación de las centrales eléctricas convencionales que implica la disminución de la inercia del sistema eléctrico. Operando adecuadamente los parques eólicos, la generación eólica puede mejorar la estabilidad de la red eléctrica y garantizar la seguridad y un continuo suministro de energía. Esta tesis doctoral propone nuevas estrategias para diseñar controladores basados en optimización dinámica para parques eólicos y mejorar la participación de los parques eólicos en el soporte de la red eléctrica. En primer lugar, esta tesis doctoral presenta los enfoques clásicos para el control de parques y turbinas eólicas y cómo los conceptos de control existentes pueden ser explotados para hacer frente a los nuevos desafíos que se esperan de los parques eólicos. Esta tesis doctoral asigna un interés especial a cómo formular la función objetivo de que la reserva de potencia sea maximizada, para ayudar por el suporte de frequencia, y al mismo tiempo seguir la potencia demandada por la red. Sin embargo, el impacto de la estela de viento generada por una turbina sobre otras turbinas necesita ser minimizado para mejorar la reserva de potencia. Por lo tanto, a lo largo de esta tesis se proponen estrategias de control centralizado para parques eólicos enfocadas en distribuir óptimamente la energía entre las turbinas para que el impacto negativo de la estela en la reserva de potencia total se reduzca. Se discuten dos técnicas de control para proporcionar los objetivos de control mencionados anteriormente. Un algoritmo de control óptimo para encontrar la mejor distribución de potencia entre las turbinas en el parque mientras se resuelve un problema iterativo de programación lineal. En segundo lugar, se utiliza la técnica de control predictivo basada en modelo para resolver un problema de control multi-objetivo que también podría incluir, junto con la maximización de reserva de potencia, otros objetivos de control, tales como la minimización de las perdidas eléctricas en los cables de la red de interconexión entre turbinas y un controlador/supervisor. Además, la investigación realizada resalta la capacidad de las estrategias de control propuestas en esta tesis para proporcionar mayor reserva de potencia respecto a los conceptos comúnmente usados para distribuir la potencia total del parque eólico. La idea principal detrás del diseño de una estrategia de control de parques eólico es de encontrar una solución óptima dentro de un cálculo computacional relativamente bajo. Aunque los controladores centralizados propuestos en esta tesis reaccionan rápidamente a los cambios en la potencia de referencia enviada desde el controlador, algunos problemas pueden ocurrir cuando se consideran parques eólicos de gran escala debido a los retrasos existentes por el viento entre turbinas. Bajo estas circunstancias, la producción de energía de cada turbina está altamente acoplada con la propagación de la estela y, por ende, con las condiciones de funcionamiento de las otras turbinas. Esta tesis doctoral propone un esquema de control de parques eólicos no centralizados basado en una estrategia de partición para dividir el parque eólico en sub-conjuntos independientes de turbinas. Con la estrategia de control propuesta, el tiempo de cálculo se reduce adecuadamente en comparación con la estrategia de control centralizado mientras que el rendimiento en la distribución óptima de potencia es ligeramente afectado. El rendimiento de todas las estrategias de control propuestas en esta tesis se prueba con un simulador de parque eólico que modela el comportamiento dinámico del efecto de estela mediante el uso de un conocido y consolidado modelo dinámico de estela y, para un análisis más realista, algunas simulaciones se realizan con software avanzado basado en la técnica de Large Eddy Simulation.Postprint (published version

Model Predictive Control of Impedance Source Inverter for Photovoltaic Applications

A model predictive controlled power electronics interface (PEI) based on impedance source inverter for photovoltaic (PV) applications is proposed in this disssertation. The proposed system has the capability of operation in both grid-connected and islanded mode. Firstly, a model predictive based maximum power point tracking (MPPT) method is proposed for PV applications based on single stage grid-connected Z-source inverter (ZSI). This technique predicts the future behavior of the PV side voltage and current using a digital observer that estimates the parameters of the PV module. Therefore, by predicting a priori the behavior of the PV module and its corresponding effects on the system, it improves the control efficacy. The proposed method adaptively updates the perturbation size in the PV voltage using the predicted model of the system to reduce oscillations and increase convergence speed. The experimental results demonstrate fast dynamic response to changes in solar irradiance level, small oscillations around maximum power point at steady-state, and high MPPT effectiveness from low to high solar irradiance level. The second part of this work focuses on the dual-mode operation of the proposed PEI based on ZSI with capability to operate in islanded and grid-connected mode. The transition from islanded to grid-connected mode and vice versa can cause significant deviation in voltage and current due to mismatch in phase, frequency, and amplitude of voltages. The proposed controller using MPC offers seamless transition between the two modes of operations. The main predictive controller objectives are decoupled power control in grid-connected mode and load voltage regulation in islanded mode. The proposed direct decoupled active and reactive power control in grid connected mode enables the dual-mode ZSI to behave as a power conditioning unit for ancillary services such as reactive power compensation. The proposed controller features simplicity, seamless transition between modes of operations, fast dynamic response, and small tracking error in steady state condition of controller objectives. The operation of the proposed system is verified experimentally. The final part of this dissertation focuses on the low voltage ride through (LVRT) capability of the proposed PV systems during grid faults such as voltage sag. In normal grid condition mode, the maximum available power from the PV panels is injected into the grid. In this mode, the system can provide reactive power compensation as a power conditioning unit for ancillary services from DG systems to main ac grid. In case of grid faults, the proposed system changes the behavior of reactive power injection into the grid for LVRT operation according to the grid requirements. Thus, the proposed controller for ZSI is taking into account both the power quality issues and reactive power injection under abnormal grid conditions

A robust multi-objective statistical improvement approach to electric power portfolio selection

Motivated by an electric power portfolio selection problem, a sampling method is developed for simulation-based robust design that builds on existing multi-objective statistical improvement methods. It uses a Bayesian surrogate model regressed on both design and noise variables, and makes use of methods for estimating epistemic model uncertainty in environmental uncertainty metrics. Regions of the design space are sequentially sampled in a manner that balances exploration of unknown designs and exploitation of designs thought to be Pareto optimal, while regions of the noise space are sampled to improve knowledge of the environmental uncertainty. A scalable test problem is used to compare the method with design of experiments (DoE) and crossed array methods, and the method is found to be more efficient for restrictive sample budgets. Experiments with the same test problem are used to study the sensitivity of the methods to numbers of design and noise variables. Lastly, the method is demonstrated on an electric power portfolio simulation code.PhDCommittee Chair: Mavris, Dimitri; Committee Member: Duncan, Scott; Committee Member: Ender, Tommer; Committee Member: German, Brian; Committee Member: Paredis, Chri