Energy Capture Maximization Research Articles

Multi-objective optimal control of a hybrid offshore wind turbine platform integrated with multi-float wave energy converters

Offshore floating wind turbines will play a pivotal role in achieving net-zero emissions. This study explores a hybrid platform combining an offshore floating wind turbine with wave energy converters (WECs) from an active control perspective to mitigate turbine damage risk while maximizing wave energy conversion. Initially, the control-oriented state-space modelling of the hybrid platform and the validation of the time-domain Cummins-type models of different orders are performed by the Eulerian-Lagrangian method with model linearization and downscaling. Then, a multi-objective non-causal optimal controller is introduced, balancing wave energy capture and penalizing the nacelle acceleration. When wave energy capture maximization is set as the control target, the proposed optimal controller can improve energy output by 184% as compared against a well-tuned passive damper across all peak periods tested. However this causes peak hub acceleration to increase marginally beyond the desirable limits of 3–4 m/s2 for wind turbine operation. When hub acceleration is penalized, the multi-objective optimal controller can reduce peak values below limits by between 40% and 61% with trivial loss of wave energy capture.

An iterative data-driven learning algorithm for calibration of the internal model in advanced wind turbine controllers

Modern industrial wind turbine controllers for partial-load region control are becoming increasingly complex by progressively relying on modeled aerodynamic characteristics. These advanced turbine controllers generally consist of a combined wind speed estimator and tracking controller, allowing for a granular trade-off between energy capture maximization and (fatigue) load minimization. Because of the limited measurements available to the controller, the control scheme's internal model quality is of utmost importance in satisfying performance and stability requirements. Therefore, the calibration thereof is of particular interest. To date, little work has been performed on the direct calibration of the model information. This work proposes a data-driven iterative learning algorithm for calibrating the internal physical model parameters. The learning algorithm uses generally available closed-loop turbine measurements, complemented with an external measurement of the rotor effective wind speed (REWS), and is thereby largely nondisruptive. The algorithm is based on steady-state assumptions and performs iterative batch-wise updates of the internal control model toward convergence. As the algorithm corrects at the actual turbine operating point, short-term relocations of the turbine's operating point can be used to calibrate in a broader operational domain. Results show outstanding learning capabilities for an aerodynamically degraded wind turbine under realistic turbulent wind conditions. Moreover, a sensitivity study is performed to expose the algorithm's susceptibility to measurement errors, algorithm tuning, and the size of the data set.

Proof-of-concept of a reinforcement learning framework for wind farm energy capture maximization in time-varying wind

In this paper, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.

Measurements and model simulations of solar radiation at tilted planes, towards the maximization of energy capture

The ideal inclination of tilted surfaces, used to maximize the capture of surface solar irradiance, is determined by latitude and time of the year. Although several algorithms are used for its estimation, the effect of clouds is difficult to be taken into account and causes large deviations from the “clear sky scenario”. Our aim is to investigate the solar irradiance at inclined surfaces on real atmospheric conditions. A set of four pyranometers, located at the National Observatory of Athens, is used to measure the solar radiation for one year and at 1-min frequency. We use the Global Horizontal (GHI) and the Global Irradiance (GIβ) reaching surfaces tilted at a β angle, with different orientations, in order to quantify the energy benefits of different installations. Furthermore, model calculations were used to simulate GIβ at different tilt angles. GHI measurements agree within the theoretical calculations on cloudless days, receiving more irradiance than the inclined surface during summer months. However, the GIβ reach higher values than GHI in wintertime. Model calculations for various tilt angles reveal that the optimum one is around 30° on year basis.

Iterative Sequential Optimization Method for Maximizing the Wind Farm Generation Considering the Wake Effect

The wake effect due to aerodynamic interaction among wind turbines makes the wind farm control a challenging problem. The issues on the wind farm control considering the wake effect include the power generation control, reduction of structural loads, and maximization of energy capture. Among these issues, this paper focuses on the maximization of energy capture and proposes an iterative sequential optimization method (ISOM). The ISOM is not limited to a particular wake model and is based on the actual measurement of the generated power. The ISOM aims to reduce the number of iterations by exploiting the properties of the maximization problem, which include the iterative single-variable optimizations and the sequential optimization in the direction from upwind turbines to downwind ones. Further, the ISOM achieves a significant level of performance in terms of the generated power at the early stage of the iteration process. The effectiveness of the ISOM is verified by the simulations using a four-turbine wind farm and the Horns Rev wind farm. The results show that the ISOM can effectively search the optimal solution with the trade-off between the number of iterations and the precision of the solution.

Linear parameter varying control of a doubly fed induction generator based wind turbine with primary grid frequency support

SUMMARYThis paper proposes a control method for a doubly fed induction generator (DFIG) driven by a wind turbine, whose rotor is connected to the power grid via two back‐to‐back pulse‐width modulation power converters. First, we design a rotor current controller for this system using the linear matrix inequality based approach to linear parameter varying systems, which takes into account the nonlinear dynamics of the system. We propose a two‐loop hierarchical control structure. The inner‐loop current controller, which considers the synchronous speed and the generator rotor speed as the components of the parameter vector, achieves tracking of the rotor current reference signals. The outer‐loop electrical torque controller aims for wind energy capture maximization and also grid frequency support, and it generates the reference rotor current. We perform a controller reduction for the inner‐loop linear parameter varying controller, which is not doable by conventional model‐reduction techniques, because the controller is parameter dependent. In simulation, the reduced‐order controller has been tested on a nonlinear fourth‐order DFIG model with a two‐mass model for the drive train. Stability and high performance have been achieved over the entire operating range of the DFIG in the wind turbine. Simulation results have demonstrated the capability of the proposed two‐loop control system to implement also grid frequency support. Copyright © 2013 John Wiley & Sons, Ltd.

Wind Turbine Control Strategy Enabling Mechanical Stress Reduction Based on Dynamic Model Including Blade Oscillation Effects

The paper presents a coupled electrical aerodynamic model for a three blade wind-turbine dynamic analysis. The model is based on a blade element representation of the aerodynamic load part combined with an aeroelastic beam element for the dynamic analysis of a real rotor blade, including top tower acceleration. The model involves reduced computation time enabling to be applied in control system hardware. Such an analysis is very promising for obtaining controllers involving compromises among contradictory targets such as energy capture maximization and mechanical stresses reduction in the aerodynamic part.