Automatic Retraction and Full Cycle Operation for a Class of Airborne Wind Energy Generators

Airborne wind energy systems aim to harvest the power of winds blowing at altitudes higher than what conventional wind turbines reach. They employ a tethered flying structure, usually a wing, and exploit the aerodynamic lift to produce electrical power. In the case of ground-based systems, where the traction force on the tether is used to drive a generator on the ground, a two phase power cycle is carried out: one phase to produce power, where the tether is reeled out under high traction force, and a second phase where the tether is recoiled under minimal load. The problem of controlling a tethered wing in this second phase, the retraction phase, is addressed here, by proposing two possible control strategies. Theoretical analyses, numerical simulations, and experimental results are presented to show the performance of the two approaches. Finally, the experimental results of complete autonomous power generation cycles are reported and compared with first-principle models.Comment: This manuscript is a preprint of a paper submitted for possible publication on the IEEE Transactions on Control Systems Technology and is subject to IEEE Copyright. If accepted, the copy of record will be available at IEEEXplore library: http://ieeexplore.ieee.or

Visual motion tracking and sensor fusion for kite power systems

An estimation approach is presented for kite power systems with groundbased actuation and generation. Line-based estimation of the kite state, including position and heading, limits the achievable cycle efficiency of such airborne wind energy systems due to significant estimation delay and line sag. We propose a filtering scheme to fuse onboard inertial measurements with ground-based line data for ground-based systems in pumping operation. Estimates are computed using an extended Kalman filtering scheme with a sensor-driven kinematic process model which propagates and corrects for inertial sensor biases. We further propose a visual motion tracking approach to extract estimates of the kite position from ground-based video streams. The approach combines accurate object detection with fast motion tracking to ensure long-term object tracking in real time. We present experimental results of the visual motion tracking and inertial sensor fusion on a ground-based kite power system in pumping operation and compare both methods to an existing estimation scheme based on line measurements

Predictive Guidance Control for Autonomous Kites with Input Delay

In this paper we consider the design of a model predictive guidance controller in a cascaded control scheme for an autonomous kite with significant input delay. The input rate of the guidance is bounded to ensure robust performance of the underlying tracking controller. This is achieved by analysisng the limitations of the tracking controller arising from model parameter uncertainty and input delay. The delay is accounted for in the control design by predicting the values of the feedback variables ahead of time based on the past inputs and the system models. To account for changing operating conditions the model parameters are updated online. The proposed method has been tested in a real-time hardware-in-the-loop simulation study

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

Design of a kite controller for airborne wind energy

Airborne wind energy is a field of technology being developed to make use of the vast, renewable wind power resource which is above the reach of traditional wind turbines, without the need for a large tower. Much analytical research has been undertaken in recent years to better understand the problem space. However, there are relatively few working systems that demonstrate their functioning and can be compared with simulations and theory. Off-grid power systems still rely heavily on diesel generators, so devices that tap renewable energy sources with similar ease of deployment and lower cost of energy would help this sector to reduce its reliance on expensive, polluting, fossil fuels. The development of these systems is often performed by teams with business interests leaving little open access content available regarding the design process of such devices or the data that they provide. A kite control pod has been designed for the remote control of a standard kitesurfing kite and a prototype has been demonstrated stably flying such a kite on a fixed length tether. This pod and kite would be tethered to a winch and as the kite flies across the wind, the lift force generated is applied to the winch which is reeled out and electrical power generated. Once fully extended, the tether would be reeled in with the kite de-powered, using some of the generated energy, stored in a battery. This system can then be used as a test bed for the further development of a compact, autonomous, airborne wind energy system for off-grid applications

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

A Review and an Approach of Flying Electric Generators as Alternate Source of Energy

This paper presents a review of flying electric generators which are used to harness kinetic energy in powerful, persistent high altitude winds. It has been found that FEGs could give individual output of up to 40MW. It is a lighter wind turbine that rotates about a horizontal axis in response to wind, generating electrical energy. This electrical energy is transferred down for immediate use, or to a set of batteries for later use, or to the power grid. This paper presents the critical analysis of existing literature which is relevant to flying electric generator Though, the literature consists of a lot many research contributions, but, here, we have analyzed some important research and review papers. The existing approaches are categorized based on the basic concepts involved in the mechanisms. The emphasis is on the concepts used by the concerned authors, the database used for experimentations and the performance evaluation parameters. Their claims are also highlighted. Finally, the findings are summarized related to the studied and analyzed research papers. Paper concludes with the motivation behind identified problem

Crosswind Kite Control - A Benchmark Problem for Advanced Control and Dynamic Optimization

This article presents a kite control and optimization problem intended as a benchmark problem for advanced control and optimization. We provide an entry point to this exciting renewable energy system for researchers in control and optimization methods looking for a realistic test bench, and/or a useful application case for their theory. The benchmark problem in this paper can be studied in simulation, and a complete Simulink model is provided to facilitate this. The simulated scenario, which reproduces many of the challenges presented by a real system, is based on experimental studies from the literature, industrial data and the first author’s own experience in experimental kite control. In par- ticular, an experimentally validated wind turbulence model is included, which subjects the kite to realistic disturbances. The benchmark problem is that of controlling a kite such that the average line tension is maximized. Two different models are provided: A more comprehensive one is used to simulate the ’plant’, while a simpler ’model’ is used to design and implement control and optimization strategies. This way, uncertainty is present in the form of plant-model mismatch. The outputs of the plant are corrupted by measurement noise. The maximum achievable average line tension for the plant is calculated, which should facilitate the performance comparison of different algorithms. A simple control strategy is implemented on the plant and found to be quite suboptimal, even if the free parameters of the algorithm are well tuned. An open question is whether or not more advanced control algorithms could do better

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