• Energy Research

In recent years, the rapidly-increasing demand for energy generation from renewable resources has been noticeable. Additional requirements are consequently set on Wind Turbine (WT) systems, primarily reflected in WT size and power rating increases. With the size increase of WT, structural loads/fatigue stress on the wind turbine become larger, simultaneously leading to its accelerated aging and the shortening of its lifetime. The primary goal of this contribution is to establish an approach for structural load reduction while retaining or slightly sacrificing the power production requirements. The approach/control strategy includes knowledge about current fatigue damage and/or damage increments and consists of multi-input multi-output controllers with variable control parameters. By the appropriate selection of the designed Multi-Input Multi-Output (MIMO) controllers, the mitigation of structural loads in accordance with a predefined range of accumulated fatigue damage or damage increments, exactly to the extent required to provide a predefined service lifetime, is obtained. The validation of the aforementioned control strategy is based on the simulation results and the WT model developed by National Renewable Energy Laboratory (NREL). The obtained results prove the efficiency of the proposed control strategy with respect to the reduction of rotor blade bending moments, simultaneously exhibiting no significant impact on the resulting power generation.

SummaryStructural loads of wind turbines are becoming critical because of the growing size of wind turbines in combination with the required dynamic output demands. Wind turbine tower and blades are therefore affected by structural loads. To mitigate the loads while maintaining other desired conditions such as the optimization of power generated or the regulation of rotor speed, advanced control schemes have been developed during the last decade. However, conflict and trade‐off between structural load reduction capacity of the controllers and other goals arise; when trying to reduce the structural loads, the power production or regulation performance may be also reduced. Suitable measures are needed when designing controllers to evaluate the control performance with respect to the conflicting control goals. Existing measures for structural loads only consider the loads without referring to the relationship between loads and other control performance aspects. In this contribution, the conflicts are clearly defined and expressed to evaluate the effectiveness of control methods by introducing novel measures. New measures considering structural loads, power production, and regulation to prove the control performance and to formulate criteria for controller design are proposed. The proposed measures allow graphical illustration and numerical criteria describing conflicting control goals and the relationship between goals. Two control approaches for wind turbines, PI and observer‐based state feedback, are defined and used to illustrate and to compare the newly introduced measures. The results are obtained by simulation using Fatigue, Aerodynamics, Structures, and Turbulence (FAST) tool, developed by the National Renewable Energy Laboratory (NREL), USA.

SummaryDisturbance accommodating control (DAC) has been developed in the last decades for wind turbines to control the rotor/generator speed and to reduce structural loads. The method allows accommodating unknown disturbance effects by using the combination of disturbance observers and disturbance rejection controllers. The actual main problem of DAC is to define suitable disturbance observer and controller gain matrices to achieve the desired overall performance including turbine speed regulation in combination with structural load mitigation. The disturbance rejection controller is often designed and tuned separately for individual applications and operating conditions. The closed‐loop system stability and uncertainties due to the use of the linearized reduced‐order model in controller synthesis procedure are not fully considered. This paper introduces a method to design DAC by optimizing the observer and controller parameters simultaneously to guarantee system performance respecting to structural loads mitigation, power regulation, and robustness. To eliminate the rotor speed control steady‐state error due to model uncertainties, partial integral action is included. Simulation results using NREL reference wind turbine models show that the proposed method successfully regulates the rotor speed without error despite the presence of the model uncertainties. Structural loads are also reduced using proposed method compared to DAC designed by Kronecker product method. The proposed approach is able to define a stable and robust DAC controller by solving a non‐smooth H∞ optimization problem with structure and stability constraints.

The comprehensive change from known, classical energy production methods to the increased use of renewable energy requires new methods in the field of efficient application and use of renewable energy. The urban energy supply presents complex challenges in improving efficiency; therefore, the prediction of the dynamical availability of energy is required. Several approaches have been explored, including statistical models and machine learning using historical data and numerical weather prediction models using mathematical models of the atmosphere and weather conditions. Accurately forecasting renewable energy production involves analyzing factors such as related weather conditions, conversion systems, and their locations, which influence both energy availability and yield. This study focuses on the short-term forecasting of wind and photovoltaic (PV) energy using historical data and machine learning approaches, aiming for accurate 8 h predictions. The goal is to develop models capable of producing accurate short-term forecasts of energy production from both resources (solar and wind), suitable for later use in a model predictive control scheme where generation and demand, as well as storage, must be considered together. Methods include regression trees, support vector regression, and regression neural networks. The main idea in this work is to use past and future information in the model. Inputs for the PV model are past PV generation and future solar irradiance, while the wind model uses past wind generation and future wind speed data. The performance of the model is evaluated over the entire year. Two scenarios are tested: one with perfect future predictions of wind speed and solar irradiance, and another considered realistic situation where perfect future prediction is not possible, and uncertain prediction is accounted for by incorporating noise models. The results of the second scenario were further improved using the output filtering method. This study shows the advantages and disadvantages of different methods, as well as the accuracy that can be expected in principle. The results show that the regression neural network has the best performance in predicting PV and wind generation compared to other methods, with an RMSE of 0.1809 for PV and 5.3154 for wind, and a Pearson coefficient of 0.9455 for PV and 0.9632 for wind.

The main advantage of hybrid powertrains is based on the efficient transfer of power and torque from power sources to the powertrain as well as recapturing of reversible energies without effecting the vehicle performance. The benefits of hybrid hydraulic powertrains can be better utilized with an appropriate power management. In this paper, different types of power management algorithms like off-line and on-line methods are briefly reviewed and classified. Finally, the algorithms are evaluated and compared. Therefore, different related criteria are evaluated and applied.

Abstract. With growth in the physical size of wind turbines, an increased structural loading of wind turbine components affecting operational reliability is expected. To mitigate structural loading in wind turbines, a novel strategy for structural load mitigation and rotor speed regulation of utility-scale wind turbines in above-rated wind speed region is proposed in this contribution. Spatial and temporal variation of wind speed is responsible for fatigue loading during power production. Previous attempts have proposed advanced control schemes incorporating disturbance models for cancelling the effects of wind disturbances. These controllers are usually designed based on reduced order models of nonlinear wind turbines, hence do not account for modeling errors and nonlinearities. Although robust controllers have been proposed to handle nonlinearities during wind turbine operation, these controllers are designed about specific operating points, hence suffer performance deterioration in changing operating conditions. In this contribution, a robust disturbance accommodating controller (RDAC), which is robust against modeling errors and nonlinearities, is combined with an adaptive independent pitch controller (aIPC), designed to be adaptive to changing operating points due to wind speed variability, to mitigate structural loads in rotor blades and tower and to regulate rotor speed. The proposed control scheme is tested on a 1.5 MW National Renewable Energy Laboratory (NREL) reference wind turbine (RWT). Simulation results show that the proposed method successfully mitigates structural loading in rotor blades and tower without sacrificing rotor speed and power regulation performance in the presence of model uncertainties and changing operating conditions.

As the size of wind turbines (WTs) increase, an additional increase in the structural load on the WT components is to be expected. This will have an impact on operational safety in terms of damage and service life. Spatial and temporal fluctuations in wind speed are responsible for the fatigue load during power generation. To minimize the effects of varying stresses, advanced control systems that incorporate appropriate models of the disturbances are proposed. These controllers are usually developed based on reduced-order models of nonlinear WTs, hence are affected by uncertainties such as modeling errors. Although robust controllers are able to deal with uncertainties, they are still only developed for design situations. Therefore, their performance can deteriorate significantly under very uncertain operating conditions. On the other hand, adaptive controllers are designed to consider multiple operating points in the design. However, most of these methods do not consider the optimization of different objectives in the design for structural load reduction or speed control of WTs. In this paper, a novel adaptive robust observer-based control strategy for structural load reduction and rotor speed regulation in commercial WTs operating at high wind speed regime is proposed. To achieve this, a robust disturbance accommodating controller (RDAC) is combined with an adaptive pitch controller (aIPC), which adapts to changing operating points. The proposed control method is tested on a 1.5 MW reference WT (RWT) developed by the National Renewable Energy Laboratory (NREL). The simulation results show that, compared with the state of the art presented on a gain-scheduled proportional integral (GSPI) and RDAC controllers, the proposed control method reduces the structural load on the rotor blades by 10.7 % and 9.2 %, respectively, and on the tower by 36.2 % and 8.4 %, respectively. Therefore, it makes a key contribution to mitigating the structural dynamic loads on WTs by reducing the load on multiple components. This is achieved without any significant impact on the rotor speed and power regulation performance or the generated power under changing operating conditions.

Real-time power management in the presence of one or more reversible energy storage systems is a current issue with hybrid electric vehicles (HEVs). To evaluate the potentials of rule-based power management, optimization with respect to two conflicting objectives, fuel consumption and state of charge (SoC) deviation, is considered in this contribution. A modular structure of power management with decoupled offline and online parts is presented. The online part incorporates look-up tables (LUTs) with parameters from the offline optimization part. This permits an inclusion of more LUTs corresponding to different drive patterns. The goal of this contribution is to combine the real-time applicability of rule-based power management and the multi-objective optimization property of genetic algorithms in a single control strategy. Component aging problems are addressed by suitable design. The influence of sizing is investigated. Finally, an experimental setup consisting of components capable of realizing the dynamics of real powertrain components is realized and introduced. A verification/plausibility assessment of modeled dynamics based on the literature is considered. This newly-introduced concept represents a class of power management, which is easy to implement, can tackle different objectives in real time, and adapt itself to unknown driver demands.