scholarly journals Fatigue mitigation of wind turbine system using multiple point model predictive control

2021 ◽  
Vol 12 (4) ◽  
pp. 2261
Author(s):  
Mutharasan Anburaj ◽  
Chandrasekar Perumal
Keyword(s):  
Model Predictive Control ◽  
Wind Turbine ◽  
Predictive Control ◽  
Energy System ◽  
Multiple Point ◽  
Fatigue Load ◽  
Point Model ◽  
Power Characteristics ◽  
Wind Turbine System ◽  
Lifetime Analysis

<span lang="EN-US">A multi-point model predictive control (MPMPC) is widely used for many applications, including wind energy system (WES), notably enhanced power characteristics and oscillation regulation. In this work, MPMPC is adapted to condense the fatigue load of the WES and improve the lifetime of the turbine assembly. The lifetime examination is carried out by considering the three chief parameters: basic lifetime until failure, short-time damage equivalent loads (DELs), and lifetime DELs. The simulation study is performed for two cases: blade root bending moments and tower top bending. Further, fatigue load examination is demonstrated to analyze the effectiveness of the proposed controller. The observed results show that the lifetime analysis of the wind turbine system displayed more excellent characteristics, i.e., 49.50% greater than MPC. Also, the fatigue load mitigation showed greater magnitude due to the control action of the proposed controller, about 37.38% grander than MPC. Therefore, the attained outcomes exhibit outstanding performance compared with conventional controllers.</span>

scholarly journals Model-predictive control and reinforcement learning in multi-energy system case studies

2021 ◽  
Vol 303 ◽  
pp. 117634
Author(s):  
Glenn Ceusters ◽  
Román Cantú Rodríguez ◽  
Alberte Bouso García ◽  
Rüdiger Franke ◽  
Geert Deconinck ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Model Predictive Control ◽  
Case Studies ◽  
Predictive Control ◽  
Energy System

An Economic Model Predictive Control Approach for Wind Power Smoothing and Tower Load Mitigation

2018 ◽  
Author(s):  
Mohamed M. Alhneaish ◽  
Mohamed L. Shaltout ◽  
Sayed M. Metwalli
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Wind Power ◽  
Predictive Control ◽  
Economic Model ◽  
Control Algorithm ◽  
Fatigue Load ◽  
Economic Model Predictive Control ◽  
Control Framework

An economic model predictive control framework is presented in this study for an integrated wind turbine and flywheel energy storage system. The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a standard wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is studied. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load at the cost of a minimal reduction of the wind energy harvested.

scholarly journals Data-Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 237 ◽  
Author(s):  
Silvio Simani ◽  
Stefano Alvisi ◽  
Mauro Venturini
Keyword(s):  
Renewable Energy ◽  
Model Predictive Control ◽  
Energy Conversion ◽  
Wind Turbine ◽  
Predictive Control ◽  
Control Strategies ◽  
Data Driven ◽  
Control Techniques ◽  
Energy Conversion Systems ◽  
Hydroelectric Plants

The interest in the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this end, data-driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes of working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Therefore, the paper aims at providing some guidelines on the design and the application of different data-driven control strategies to a wind turbine benchmark and a hydroelectric simulator. They rely on self-tuning PID, fuzzy logic, adaptive and model predictive control methodologies. Some of the considered methods, such as fuzzy and adaptive controllers, were successfully verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some details of the implementation of the proposed control strategies to these energy conversion systems. The simulations will highlight that the fuzzy regulators are able to provide good tracking capabilities, which are outperformed by adaptive and model predictive control schemes. The working conditions of the considered processes will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many plants.

A robustification of the two degree-of-freedom controller based upon multivariable generalized predictive control law and robust H∞ control for a doubly-fed induction generator

2016 ◽  
Vol 40 (3) ◽  
pp. 1005-1017 ◽  
Author(s):  
Mohammed Aidoud ◽  
Moussa Sedraoui ◽  
Abderrazek Lachouri ◽  
Abdelhalim Boualleg
Keyword(s):  
Wind Turbine ◽  
Predictive Control ◽  
Degree Of Freedom ◽  
Induction Generator ◽  
Generalized Predictive Control ◽  
Doubly Fed Induction Generator ◽  
Wind Turbine System ◽  
Youla Parameterization ◽  
Doubly Fed ◽  
Two Degree Of Freedom

A robustification method of primary two degree-of-freedom (2-DOF) controllers is proposed in this paper to control the wind turbine system equipped with a doubly-fed induction generator DFIG. The proposed robustification method should follow the following three step-procedures. First, the primary 2-DOF controller is designed through the initial form of the multivariable generalized predictive control MGPC law to ensure a good tracking dynamic of reference trajectories. Second, the robust [Formula: see text] controller is independently designed for the previous system to ensure good robustness properties of the closed-loop system against model uncertainties, neglecting dynamics and sensor noises. Finally, both above mentioned controllers are combined to design the robustified 2-DOF-MGPC controller using Youla parameterization method. Therefore, the obtained controller conserves the same good tracking dynamic that is provided by the primary 2-DOF-MGPC controller. It ensures the same good robustness properties which are produced by the robust [Formula: see text] controller. A wind turbine system equipped with a DFIG is controlled by the robustified 2-DOF-MGPC controller. Its dynamic behaviour is modelled by an unstructured-output multiplicative uncertainty plant. The controller performances are valid by comparison with those given through both controllers, which are primary 2-DOF-MGPC and robust [Formula: see text] controllers in time and frequency domains.

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