Evolutionary robotics in high altitude wind energy applications

Recent years have seen the development of wind energy conversion systems that can exploit the superior wind resource that exists at altitudes above current wind turbine technology. One class of these systems incorporates a flying wing tethered to the ground which drives a winch at ground level. The wings often resemble sports kites, being composed of a combination of fabric and stiffening elements. Such wings are subject to load dependent deformation which makes them particularly difficult to model and control. Here we apply the techniques of evolutionary robotics i.e. evolution of neural network controllers using genetic algorithms, to the task of controlling a steerable kite. We introduce a multibody kite simulation that is used in an evolutionary process in which the kite is subject to deformation. We demonstrate how discrete time recurrent neural networks that are evolved to maximise line tension fly the kite in repeated looping trajectories similar to those seen using other methods. We show that these controllers are robust to limited environmental variation but show poor generalisation and occasional failure even after extended evolution. We show that continuous time recurrent neural networks (CTRNNs) can be evolved that are capable of flying appropriate repeated trajectories even when the length of the flying lines are changing. We also show that CTRNNs can be evolved that stabilise kites with a wide range of physical attributes at a given position in the sky, and systematically add noise to the simulated task in order to maximise the transferability of the behaviour to a real world system. We demonstrate how the difficulty of the task must be increased during the evolutionary process to deal with this extreme variability in small increments. We describe the development of a real world testing platform on which the evolved neurocontrollers can be tested

Direct Nonlinear Trajectory Optimization and State Estimation for a Tethered Underwater Energy Harvesting Kite

This dissertation addresses the coupled challenges of state estimation and trajectory optimization for a marine hydro-kinetic energy harvesting kite. The optimization objective is to maximize the kite's average mechanical power output. This work is motivated by the potential of ``pumping-mode" tethered kites to provide attractive levelized costs of electricity, especially when cross-current motion is exploited to maximize energy harvesting. In ``pumping-mode" kites, the kite is tethered to platform carrying a motor/generator, and electricity generation is achieved by reeling the kite out and in at high and low tether tension levels, respectively. Marine hydro-kinetic (MHK) systems are heavily influenced by wind energy systems. In both contexts, for instance, tethered kites can be used for electricity generation instead of stationary turbines. Similar to airborne wind energy (AWE) systems, the power production capacities of MHK kites are heavily influenced by their flight trajectories. While trajectory optimization is a well-established research area for AWE systems, it is a nascent but growing field for MHK kites. Moreover, although both AWE and MHK kites have the potential to benefit from trajectory optimization, the lessons learned from AWE systems might not be directly applicable to MHK kites, since MHK systems are often close to neutral buoyancy whereas AWE systems are not. Finally, there is little work in the literature that co-optimizes the spooling and cross-current trajectories of a pumping-mode MHK kite. The first contribution of this dissertation is to explore the simultaneous optimization of the cross-current trajectory and the spooling motion of a pumping-mode kite using direct transcription. While the results highlight the degree to which simultaneous optimization can be beneficial for these systems, they also motivate the need for a solution approach that satisfies the constraints imposed by the kite dynamics exactly, as opposed to approximately. This leads to the second contribution of this dissertation, namely, finding an analytic solution to the inverse dynamics of the MHK kite, i.e., mapping a desired combination of kite position, velocity, and acceleration onto the corresponding actuation inputs. The dissertation then proceeds to its third contribution, namely, solving the kite trajectory optimization problem based on the above exact solution of the kite's inverse dynamics. The resulting simulation provides more realistic optimization results. However, all of the above work focuses on the special case where the free-stream fluid velocity is known and spatio-temporally constant. This motivates the fourth and final contribution of this dissertation, namely, the development of an unscented Kalman filter for simultaneously estimating both the kite's state and the free-stream fluid velocity. One interesting outcome of the estimation study is the finding that simple unscented Kalman filtering is not able to estimate the fluid velocity accurately without the direct measurement of the attitude of the kite

A cycle-power optimization strategy for airborne wind energy systems in pumping-kite mode

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.Sistemas de geração baseados em aerofólios cabeados (AWES - Airborne Wind Energy Systems) correspondem a uma nova tecnologia para captação da energia eólica. No modo pumping-kite o aerofólio desenrola um cabo ligado a um tambor em solo, gerando energia através do desenrolamento do cabo sob alta tração. Após um período de geração, o cabo atinge um valor máximo e uma fase de recolhimento é iniciada. Nesta fase a máquina elétrica, antes utilizada como gerador, é acionada como motor gastando uma parcela da energia gerada para enrolar a quantia de cabo desenrolada. Para reduzir a energia gasta e prover um melhor aproveitamento ao sis- tema uma manobra de baixa tração do aerofólio é realizada durante o recolhimento do cabo. A trajetória seguida pelo aerofólio, juntamente com a velocidade de desenrola- mento e enrolamento do cabo e atuações adicionais que afetam o voo do aerofólio formam um conjunto complexo de variáveis que influenciam o saldo energético do sis- tema e, consequentemente, a viabilidade do mesmo. Devido à importância do tema, diversos trabalhos da literatura abordam esta tarefa, no entanto, uma solução definitiva ainda não foi encontrada. Durante a fase de geração, é apresentada em [31] uma expressão para a veloci- dade de desenrolamento que maximiza a potência instantânea gerada. No entanto, uma potência média de ciclo mais elevada é obtida por uma velocidade de desenrola- mento mais baixa, reduzindo a potência instantânea gerada e aumentando a duração da fase de geração. Este resultado é obtido em [12] através de uma otimização it- erativa de todo o ciclo de operação. Neste mesmo trabalho é proposto um sistema de controle para manter o aerofólio em uma trajetória em lemniscata. Este trabalho adapta a otimização proposta em [12] para uma otimização on-line que determina a velocidade de desenrolamento e a elevação da trajetória em lemniscata ótimos. Ao operar com uma otimização on-line, consegue-se adaptar a solução para diferentes condições de vento e incorporar restrições físicas e de operação à solução encon- trada. Poucos trabalhos abordam em detalhes a geração de trajetória para a fase de recol- himento do aerofólio. Diversos trabalhos, como [14] e [17], abordam indiretamente este problema ao proporem um problema de otimização off-line para determinar uma trajetória completa de voo. Estes trabalhos, no entanto, fornecem uma solução para uma única condição de vento e empregam problemas de otimização muito complexos para serem executados em tempo real. Uma segunda abordagem utilizada é definir algumas características das referências utilizadas durante a fase de recolhimento. Em [23], por exemplo, o aerofólio é controlado através da tração e de uma atuação de escoamento de vento, que modifica as propriedades aerodinâmicas do aerofólio e é comumente chamada de depower. Durante a fase de recolhimento, rampas de coe- ficientes fixos são utilizadas e o valor final de tração é determinado através de uma otimização iterativa ao longo de diversos ciclos de operação. Uma abordagem similar é utilizada para otimizar a fase de recolhimento em [12], que dá continuidade ao tra- balho apresentado em [28]. Neste caso as referências de tração e depower também são limitadas a rampas, no entanto, as variáveis de decisão são os coeficientes das rampas. Neste trabalho é proposto o emprego de um controle preditivo não-linear baseado em modelo (NMPC) com um critério econômico para aproximar a solução que otimiza a potência média de ciclo. Já é encontrado frequentemente na literatura o uso de NMPC para seguir trajetórias geradas off-line. Em contraste, neste trabalho propõe-se uma função custo que pondera a potência instantânea gasta e a velocidade de recol- himento a cada instante da trajetória. Esta função custo busca capturar o fator de decisão instantâneo que qualquer algoritmo de geração de trajetória deve realizar. A potência média de ciclo busca ser maximizada através de um breve estudo do efeito resultante da variação dos pesos da função custo. Os resultados obtidos mostram que a solução proposta atinge resultados similares à soluções off-line de otimização sendo suficientemente simples para ser executada on-line. O emprego de um NMPC permite a adição intuitiva de diversas restrições permitindo uma solução flexível e cus- tomizável. A principal contribuição deste trabalho é o projeto de um algoritmo de otimiza- ção on-line para sistemas pumping-kite que apresenta bons resultados para diferentes condições de vento e possibilita a incorporação de diversas restrições de operação.Airborne wind energy systems (AWES) represent a novel high-altitude wind power harnessing technology in which the aerodynamic forces acting on suspended tethered aircraft are employed to produce electricity. In the so-called pumping-kite mode, the effects of such forces on the available aerodynamic surfaces are used to reel-out the tether and drive a generator on the ground, which is known as the traction phase. After a maximum tether length is reached the retraction phase takes place. During this part of the operating cycle, the tether is reeled back in while spending a fraction of the energy produced in the previous phase. In order to reduce the energy consumption and provide a better overall performance for the whole system, the trajectory of the aircraft must be carefully designed. This work proposes an on-line optimization strategy to adapt the airfoil trajectory to the current wind conditions and system parameters during both operation phases. The proposed algorithms, which were designed and tuned targeting an optimal average cycle-power, and also take into account the mutual influence of both phases of the pumping cycle, are shown to achieve performance levels similar to those obtained by more conventional off-line optimization methods while successfully complying with several operation and constructive constraints

Airborne Wind Energy - To fly or not to fly?

This thesis investigates crosswind Airborne Wind Energy Systems (AWESs) in terms of power production and potential role in future electricity generation systems. The perspective ranges from the small scale, modelling AWE as a single system, to the large, implementing AWESs in regional electricity systems. \ua0To estimate the AWES power production, the thesis provides a dynamic system model that serves as the basis for all the work. The model describes the flight dynamics of a rigid wing that is exposed to tether and aerodynamic forces controlled by flight control surfaces. Index-3 Differential Algebraic Equations (DAEs) based on Lagrangian mechanics describe the dynamics. \ua0This model is validated by fitting it to real flight measurements obtained with a pumping-mode AWES, the prototype AP2 by Ampyx Power. The optimal power production of an AWES depends on complex trade-offs; this motivates formulating the power production computation as an Optimal Control Problem (OCP). The thesis presents the numerical methods needed to discretize the OCP and solve the resulting Nonlinear Program (NLP). \ua0Large-scale implementation of AWESs raises challenges related to variability in power production on the time scale of minutes to weeks. For the former, we investigate the periodic fluctuations in the power output of a single AWES. These fluctuations can be severe when operating a wind farm and have to be considered and reduced for an acceptable grid integration. We analyse the option of controlling the flight trajectories of the individual systems in a farm so that the total power output of the farm is smoothed. This controlled operation fixes the system\u27s trajectory, reducing the ability to maximize the power output of individual AWESs to local wind conditions. We quantify the lost power production if the systems are controlled such that the total farm power output is smoothed. Results show that the power difference between the optimal and fixed trajectory does not exceed 4% for the systems modelled in the study.\ua0The variations in AWESs power production on the timescale of hours to weeks are particularly relevant to the interaction between AWE and other power generation technologies. Investigating AWESs in an electricity system context requires power-generation profiles with high spatio-temporal resolution, which means solving a large number of OCPs. In order to efficiently solve these numerous OCPs in a sequential manner, this thesis presents a homotopy-path-following method combined with modifications to the NLP solver. The implementation shows a 20-fold reduction in computation time compared to the original method for solving the NLP for AWES power optimization.\ua0 For large wind-data sets, a random forest regression model is trained to a high accuracy, providing an even faster computation.The annual generation profiles for the modelled systems are computed using ERA5 wind data for several locations and compared to the generation profile for a traditional wind turbine. The results show that the profiles are strongly correlated in time, which is a sobering fact in terms of technology competition. However, the correlation is weaker in locations with high wind shear.\ua0 \ua0The potential role of AWESs in the future electricity system is further investigated. This thesis implements annual AWE-farm generation profiles into a cost-optimizing electricity system model. We find that AWE is most valuable to the electricity system if installed at sites with low wind speed within a region. At greater shares of the electricity system, even if AWESs could demonstrate lower costs compared to wind turbines, AWE would merely substitute for them instead of increasing the total share of wind energy in the system. This implies that the economic value of an AWES is limited by its cost relative to traditional wind turbines

Configuration Optimisation of Kite-based Wind Turbines

In questo lavoro l'autore implementa un modello fisico e economico di un crosswind airborne wind energy system, sviluppato nella tesi, in algoritmo di ottimizzazione gradient-based. Facendo ciò, un design di massima del sistema può essere ottenuto. Attraverso un'analisi di sensitività globale gli ottimi design sono studiati e interpretati, imponendo un'ampia incertezza ai parametri in ingresso del modello.ope

Airborne Wind Energy - to fly or not to fly?

This thesis investigates crosswind Airborne Wind Energy Systems (AWESs) in terms of power production and potential role in future electricity generation systems. The perspective ranges from the small scale, modelling AWE as a single system, to the large, implementing AWESs in regional electricity systems. \ua0To estimate the AWES power production, the thesis provides a dynamic system model that serves as the basis for all the work. The model describes the flight dynamics of a rigid wing that is exposed to tether and aerodynamic forces controlled by flight control surfaces. Index-3 Differential Algebraic Equations (DAEs) based on Lagrangian mechanics describe the dynamics. \ua0This model is validated by fitting it to real flight measurements obtained with a pumping-mode AWES, the prototype AP2 by Ampyx Power. The optimal power production of an AWES depends on complex trade-offs; this motivates formulating the power production computation as an Optimal Control Problem (OCP). The thesis presents the numerical methods needed to discretize the OCP and solve the resulting Nonlinear Program (NLP). \ua0Large-scale implementation of AWESs raises challenges related to variability in power production on the time scale of minutes to weeks. For the former, we investigate the periodic fluctuations in the power output of a single AWES. These fluctuations can be severe when operating a wind farm and have to be considered and reduced for an acceptable grid integration. We analyse the option of controlling the flight trajectories of the individual systems in a farm so that the total power output of the farm is smoothed. This controlled operation fixes the system\u27s trajectory, reducing the ability to maximize the power output of individual AWESs to local wind conditions. We quantify the lost power production if the systems are controlled such that the total farm power output is smoothed. Results show that the power difference between the optimal and fixed trajectory does not exceed 4% for the systems modelled in the study.\ua0The variations in AWESs power production on the timescale of hours to weeks are particularly relevant to the interaction between AWE and other power generation technologies. Investigating AWESs in an electricity system context requires power-generation profiles with high spatio-temporal resolution, which means solving a large number of OCPs. In order to efficiently solve these numerous OCPs in a sequential manner, this thesis presents a homotopy-path-following method combined with modifications to the NLP solver. The implementation shows a 20-fold reduction in computation time compared to the original method for solving the NLP for AWES power optimization.\ua0 For large wind-data sets, a random forest regression model is trained to a high accuracy, providing an even faster computation.The annual generation profiles for the modelled systems are computed using ERA5 wind data for several locations and compared to the generation profile for a traditional wind turbine. The results show that the profiles are strongly correlated in time, which is a sobering fact in terms of technology competition. However, the correlation is weaker in locations with high wind shear.\ua0 \ua0The potential role of AWESs in the future electricity system is further investigated. This thesis implements annual AWE-farm generation profiles into a cost-optimizing electricity system model. We find that AWE is most valuable to the electricity system if installed at sites with low wind speed within a region. At greater shares of the electricity system, even if AWESs could demonstrate lower costs compared to wind turbines, AWE would merely substitute for them instead of increasing the total share of wind energy in the system. This implies that the economic value of an AWES is limited by its cost relative to traditional wind turbines

Custom optimization algorithms for efficient hardware implementation

The focus is on real-time optimal decision making with application in advanced control systems. These computationally intensive schemes, which involve the repeated solution of (convex) optimization problems within a sampling interval, require more efficient computational methods than currently available for extending their application to highly dynamical systems and setups with resource-constrained embedded computing platforms. A range of techniques are proposed to exploit synergies between digital hardware, numerical analysis and algorithm design. These techniques build on top of parameterisable hardware code generation tools that generate VHDL code describing custom computing architectures for interior-point methods and a range of first-order constrained optimization methods. Since memory limitations are often important in embedded implementations we develop a custom storage scheme for KKT matrices arising in interior-point methods for control, which reduces memory requirements significantly and prevents I/O bandwidth limitations from affecting the performance in our implementations. To take advantage of the trend towards parallel computing architectures and to exploit the special characteristics of our custom architectures we propose several high-level parallel optimal control schemes that can reduce computation time. A novel optimization formulation was devised for reducing the computational effort in solving certain problems independent of the computing platform used. In order to be able to solve optimization problems in fixed-point arithmetic, which is significantly more resource-efficient than floating-point, tailored linear algebra algorithms were developed for solving the linear systems that form the computational bottleneck in many optimization methods. These methods come with guarantees for reliable operation. We also provide finite-precision error analysis for fixed-point implementations of first-order methods that can be used to minimize the use of resources while meeting accuracy specifications. The suggested techniques are demonstrated on several practical examples, including a hardware-in-the-loop setup for optimization-based control of a large airliner.Open Acces

Evolution of A Common Vector Space Approach to Multi-Modal Problems

A set of methods to address computer vision problems has been developed. Video un- derstanding is an activate area of research in recent years. If one can accurately identify salient objects in a video sequence, these components can be used in information retrieval and scene analysis. This research started with the development of a course-to-fine frame- work to extract salient objects in video sequences. Previous work on image and video frame background modeling involved methods that ranged from simple and efficient to accurate but computationally complex. It will be shown in this research that the novel approach to implement object extraction is efficient and effective that outperforms the existing state-of-the-art methods. However, the drawback to this method is the inability to deal with non-rigid motion. With the rapid development of artificial neural networks, deep learning approaches are explored as a solution to computer vision problems in general. Focusing on image and text, the image (or video frame) understanding can be achieved using CVS. With this concept, modality generation and other relevant applications such as automatic im- age description, text paraphrasing, can be explored. Specifically, video sequences can be modeled by Recurrent Neural Networks (RNN), the greater depth of the RNN leads to smaller error, but that makes the gradient in the network unstable during training.To overcome this problem, a Batch-Normalized Recurrent Highway Network (BNRHN) was developed and tested on the image captioning (image-to-text) task. In BNRHN, the highway layers are incorporated with batch normalization which diminish the gradient vanishing and exploding problem. In addition, a sentence to vector encoding framework that is suitable for advanced natural language processing is developed. This semantic text embedding makes use of the encoder-decoder model which is trained on sentence paraphrase pairs (text-to-text). With this scheme, the latent representation of the text is shown to encode sentences with common semantic information with similar vector rep- resentations. In addition to image-to-text and text-to-text, an image generation model is developed to generate image from text (text-to-image) or another image (image-to- image) based on the semantics of the content. The developed model, which refers to the Multi-Modal Vector Representation (MMVR), builds and encodes different modalities into a common vector space that achieve the goal of keeping semantics and conversion between text and image bidirectional. The concept of CVS is introduced in this research to deal with multi-modal conversion problems. In theory, this method works not only on text and image, but also can be generalized to other modalities, such as video and audio. The characteristics and performance are supported by both theoretical analysis and experimental results. Interestingly, the MMVR model is one of the many possible ways to build CVS. In the final stages of this research, a simple and straightforward framework to build CVS, which is considered as an alternative to the MMVR model, is presented