Neural control for voltage dips ride-through of oscillating water column-based wave energy converter equipped with doubly-fed induction generator

A Linear Quadratic Gaussian Approach for Power Transfer Maximization of a Point Absorber Wave Energy Converter

“…Hence, it is clear that the average generated power can be greatly enhanced using the proposed optimization method compared with the case of without optimization for different mooring constant. Table 4 provides a brief comparison between the proposed technique in this article and other approaches previously presented in the literature [16,17] [17,19,22] in the following ways: a. Very simple and straightforward control law is used if it compared to the more sophisticated controllers structure in [17,19,22].…”

“…They include fuzzy logic control [17], robust control [18], and artificial neural networks control [19]. The main idea beyond these recent methods, to maximize the power output of the wave energy converter (WEC), lies in explicitly regulating the PTO force via a feedback control system with appropriate constraints.…”

Kalmen estimator as a robust solution for output power maximization of wave energy converter

“…Our work is different from those in Refs in the following ways: A very simple and straightforward control law is used when compared to the more sophisticated controller structure in Refs . The resulting optimization problem in Ref for controlling the same PA‐WEC model as used in our paper is very large for solving online within a small step time, which may lead to a real‐time applicability problem that can be avoided using the proposed LQG framework in this paper. The NMPC proposed in requires the solution of a nonlinear problem. In general, these problems are nonconvex, so it cannot be ensured that a global optimum can be found.…”

“…[8] that the velocity and hence the absorbed power from waves extracted by Over the course of the last four decades, advanced control techniques have been applied successfully to the WEC systems to maximize the extracted power and improve the system performance. They include fuzzy logic control (FL) [16], robust fuzzy logic control (RFL) [17], artificial neural networks control [18], and nonlinear model predictive control (NMPC) [19]. The main idea beyond these recent methods is to maximize the power output of the WEC by explicitly regulating the PTO force via a feedback and/or predicted feedback control system with appropriate constraints.…”

Multi-objective Fuzzy-optimization of Crowbar Resistances for the Low-Voltage Ride-through of Doubly Fed Induction Wind Turbine Generation Systems

“…However, since the stator of a DFIG is directly connected to the grid, this promising wind generator technology is vulnerable to grid disturbances, especially to voltage sag faults [2]. A grid voltage sag imposed at the connection point of a DFIG to the grid could easily induce a DFIG rotor overcurrent, DC-link overvoltage, and torque oscillations that could lead to the destruction of the rotor-side converter, the DC-link capacitor, and other mechanical parts, respectively [3]. Thus, it is desirable that wind turbines remain connected to the grid and actively contribute to the system stability during and after grid voltage sag faults and disturbances.…”