Adaptive control algorithm for improving power capture of wind turbines in turbulent winds

Abstract — The standard wind turbine (WT) control law modifies the torque applied to the generator as a quadratic function of the generator speed (Kω 2) while blades are positioned at some optimal pitch angle (β ∗). The value of K and β ∗ should be properly selected such that energy capture is increased. In practice, the complex and time-varying aerodynamics a WT face due to turbulent winds make their determination a hard task. The selected constant parameters may maximize energy for a particular, but not all, wind regime conditions. Adaptivity can modify the controller to increase power capture under variable wind conditions. This paper present new analysis tools and an adaptive control law to increase the energy captured by a wind turbine. Due to its simplicity, it can be easily added to existing industry-standard controllers. The effectiveness of the proposed algorithm is assessed by simulations on a high-fidelity aeroelastic code. Index Terms — Wind Turbines, Adaptive Control, efficiency. I

Integrating Structural Health Management with Contingency Control for Wind Turbines

Maximizing turbine up-time and reducing maintenance costs are key technology drivers for wind turbine operators. Components within wind turbines are subject to considerable stresses due to unpredictable environmental conditions resulting from rapidly changing local dynamics. In that context, systems health management has the aim to assess the state-of-health of components within a wind turbine, to estimate remaining life, and to aid in autonomous decision-making to minimize damage to the turbine. Advanced contingency control is one way to enable autonomous decision-making by providing the mechanism to enable safe and efficient turbine operation. The work reported herein explores the integration of condition monitoring of wind turbine blades with contingency control to balance the trade-offs between maintaining system health and energy capture. Results are demonstrated using a high fidelity simulator of a utility-scale wind turbine

Performance Comparison of Control Schemes for Variable-Speed Wind Turbines

We analyze the performance of different control schemes when applied to the regulation problem of a variable-speed representative wind turbine. In particular, we formulate and compare a wind-scheduled PID, a LQR controller and a novel adaptive non-linear model predictive controller, equipped with observers of the tower states and wind. The simulations include gusts and turbulent winds of varying intensity in nominal as well as off-design operating conditions. The experiments highlight the possible advantages of model-based non-linear control strategies

Improved Wind Turbine Control Strategies for Maximizing Power Output and Minimizing Power Flicker

For reducing the cost of energy (COE) for wind power, controls techniques are important for enhancing energy yield, reducing structural load and improving power quality. This thesis presents the control strategies studies for wind turbine both from the perspectives of both maximizing power output and reducing power flicker and structural load, First, a self-optimizing robust control scheme is developed with the objective of maximizing the power output of a variable speed wind turbine with doubly-fed induction generator (DFIG) operated in Region 2. Wind power generation can be divided into two stages: conversion from aerodynamic power to rotor (mechanical) power and conversion from rotor power to the electrical (grid) power. In this work, the maximization of power generation is achieved by a two-loop control structure in which the power control for each stage has intrinsic synergy. The outer loop is an Extremum Seeking Control (ESC) based generator torque regulation via the rotor power feedback. The ESC can search for the optimal torque constant to maximize the rotor power without wind measurement or accurate knowledge of power map. The inner loop is a vector-control based scheme that can both regulate the generator torque requested by the ESC and also maximize the conversion from the rotor power to grid power. In particular, an ∞ controller is synthesized for maximizing, with performance specifications defined based upon the spectrum of the rotor power obtained by the ESC. Also, the controller is designed to be robust against the variations of some generator parameters. The proposed control strategy is validated via simulation study based on the synergy of several software packages including the TurbSim and FAST developed by NREL, Simulink and SimPowerSystems. Then, a bumpless transfer scheme is proposed for inter-region controller switching scheme in order to reduce the power fluctuation and structural load under fluctuating wind conditions. This study considers the division of Region 2, Region 2.5 and Region 3 in the neighborhood of the rated wind speed. When wind, varies around the rated wind speed, the switching of control can lead to significant fluctuation in power and voltage supply, as well as structural loading. To smooth the switch and improve the tracking, two different bumpless transfer methods, Conditioning and Linear Quadratic techniques, are employed for different inter-region switching situations. The conditioning bumpless transfer approach adopted for switching between Region 2 maximum power capture controls to Region 2.5 rotor speed regulation via generator torque. For the switch between Region 2.5 and Region 3, the generator torque windup at rated value and pitch controller become online to limit the load of wind turbine. LQ technique is posed to reduce the discontinuity at the switch between torque controller and pitch controller by using an extra compensator. The flicker emission of the turbine during the switching is calculated to evaluate power fluctuation. The simulation results demonstrated the effectiveness of the proposed scheme of inter-region switching, with significant reduction of power flicker as well as the damage equivalent load

A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation

The existing model predictive control algorithm based on continuous control using quadratic programming is currently one of the most used modern control strategies applied to wind turbines. However, heavy computational time involved and complexity in implementation are still obstructions in existing model predictive control algorithm. Owing to this, a new switched model predictive control technique is developed for the control of wind turbines with the ability to reduce complexity while maintaining better efficiency. The proposed technique combines model predictive control operating on finite control set and artificial intelligence with reinforcement techniques (Markov Chains, MC) to design a new effective control law which allows to achieve the control objectives in different wind speed zones with minimization of computational complexity. The proposed method is compared with the existing model predictive control algorithm, and it has been found that the proposed algorithm is better in terms of computational time, load mitigation, and dynamic response. The proposed research is a forward step towards refining modern control techniques to achieve optimization in nonlinear process control using novel hybrid structures based on conventional control laws and artificial intelligence.© 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)fi=vertaisarvioitu|en=peerReviewed

A novel correlation model for horizontal axis wind turbines operating at high-interference flow regimes

Driven by economics-of-scale factors, wind-turbine rotor sizes have increased formidably in recent years. Larger rotors with lighter blades of increased flexibility will experiment substantially higher levels of deformation. Future turbines will also incorporate advanced control strategies to widen the range of wind velocities over which energy is captured. These factors will extend turbine operational regimes, including flow states with high interference factors. In this paper we derive a new empirical relation to both improve and extend the range of Blade Element Momentum (BEM) models, when applied to high interference-factor regimes. In most BEM models, these flow regimes are modeled using empirical relations derived from experimental data. However, an empirical relation that best represents these flow states is still missing. The new relation presented in this paper is based on data from numerical experiments performed on an actuator disk model, and implemented in the context of a novel model of the BEM family called the DRD-BEM (Dynamic Rotor Deformation—BEM), recently introduced in Ponta, et al., 2016. A detailed description of the numerical experiments is presented, followed by DRD-BEM simulation results for the case of the benchmark NREL-5MW Reference Wind Turbine with this new polynomial curve incorporated