Robust data driven H-infinity control for wind turbine

Kim, Youngman

doi:10.1016/j.jfranklin.2016.06.009

Cited by 28 publications

(11 citation statements)

References 28 publications

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“…where w is a vector of external input including non-linearity effects and disturbances, z is a vector of signals describing the desired performance of the closed-loop system, u is the vector of control signals, and y is the vector of measured outputs (Brezina and Brezina, 2011; Kim, 2016). Then an optimization process is performed over all the controllers u =− Kx (Figure 2a) that stabilize the closed-loop system with lowest optimum λ 0 , as given below:…”

Section: Robust Designsmentioning

confidence: 99%

Lateral control of an Aerosonde facing tolerance in parameters and operation speed: performance robustness study

Seyedtabaii

Zaker

2018

Transactions of the Institute of Measurement and Control

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The aim is to acquire low variance roll responses (performance robustness) of control of an Aerosonde despite the high level of tolerances in aerodynamic parameters and working speed. In this respect, fractional-order proportional plus integral and derivative (FOPID) is a valuable option; others are H∞ and μ synthesis. FOPID can tolerate system uncertainty by maintaining a wide open-loop flat phase margin band. All three methods are worked out using the linearized system model and deliver (at least initially) high-integer-order controllers. The uncertainty level is not explicitly considered in H∞, but it may be presented in the μ synthesis and FOPID. The uncertainty presentation in the modified fractional-order controller (mFOC) design is through a Φd curve. The Φd curve is fitted to the mean of the upper and lower bands of the phase margins distribution map of the random systems. It is shown that the mFOC design perfectly secures the desired phase margin flatness. The controllers are applied to the roll of an unmanned aircraft vehicle with a 30% tolerance in the aerodynamic parameters, and operation speed and robustness in performance is evaluated. The simulation results indicate that the mFOC design renders more coherent responses than what H∞ and µ synthesis design deliver. This is confirmed through extensive simulations.

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Section: Robust Designsmentioning

confidence: 99%

Lateral control of an Aerosonde facing tolerance in parameters and operation speed: performance robustness study

Seyedtabaii

Zaker

2018

Transactions of the Institute of Measurement and Control

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“…Thus, vast amount of research efforts have been poured into that area and much of tackled. [1][2][3][4][5][6][7][8][9][10] However, technical developments outside fault detection community, such as machine learning and statistics, induced new insights about understanding the nature of faults and it has triggered this research. For example, the sparsity, which has been an important topic in estimating system state and controlling a system with minimal actuator operation, [11][12][13][14] can represent the nature of abruptly changing fault.…”

Section: Introductionmentioning

confidence: 99%

Robust Fault Detection Based on l1 Regularization

Kim

2020

Advanced Intelligent Systems

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Herein, l1 regularization‐based fault detection technique for stochastic discrete time systems in state space form is discussed. Compared with the deterministic nature of a fault which usually causes an abrupt change, the state of a system smoothly evolves in most cases and the disturbance in the process and the sensor measurement has a stochastic characteristic. The modeling uncertainty in the state space of a system can induce a bias in deterministic way and it can be combined to a fault. In the fault detection community, the modeling uncertainty is called a multiplicative fault and the abruptly‐changing fault is called an additive fault. Inspired by the fact that l2 norm is useful for estimating smoothly evolving states and reducing a bias and l1 norm for detecting abrupt change with spare structure, this research develops the technique for detecting fault which changes abruptly under stochastic disturbance and modeling uncertainty. The l2 norm for bias compensation (due to modeling uncertainty and/or additive fault) is combined with l1 norm for fault detection (especially abruptly changing fault) and the two norms are set up as a regularization problem. The regularization problem is convex thus, global solution can be found in efficient way.

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“…The application of different advanced control strategies to the operation and grid connection of wind turbines has been found in the literature. Kim presents a data‐driven robust H∞ controller which is aimed to improve the operation of a wind turbine. A nonlinear control strategy for variable speed wind turbines based on Fuzzy Logic is proposed in the work of Liu et al Jafarnejadsani et al present in their work a gain scheduled optimal control of a wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

et al. 2019

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The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)-based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5-MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.

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Robust data driven H-infinity control for wind turbine

Cited by 28 publications

References 28 publications

Lateral control of an Aerosonde facing tolerance in parameters and operation speed: performance robustness study

Lateral control of an Aerosonde facing tolerance in parameters and operation speed: performance robustness study

Robust Fault Detection Based on l1 Regularization

Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

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