In this study, a linear quadratic regulator based on the fuzzy logic (LQRF) control algorithm for a variable-speed variable-pitch wind turbine was designed. In addition, to verify the optimum performance of the controller, simulations and wind tunnel tests were conducted. In the simulation, the performances of the proportional-integral (PI) and LQRF algorithms were compared in the transition region and the rated power region. In the wind tunnel test, the applicability of the LQRF algorithm was verified by comparing it with the conventional PI algorithms. The results showed that when compared with the PI control, the proposed LQRF control reduced the tower vibration by up to 12.50% depending on the operating region. Furthermore, the power deviation was reduced by 38.93%. These tests confirmed that the proposed LQRF control increases the power performance and structural stability of wind turbines compared with conventional PI controls.

The recent emergence of the Internet of Things (IoT) and the rise of artificial intelligence have led to substantial interest in gathering information through various sensing techniques. In particular, environmental sensing technologies implemented through the IoT have important roles in automated smart platforms. In this review, we introduce research trends and prospects for sensing technologies based on organic materials that collect information from surrounding environmental media. State-of-the-art chemical, optical, and physical organic sensors hold great promise for the realization of smart environmental systems combined with IoT network platforms.

In this study, a pitch H-infinity control algorithm was developed for variable-speed–variable-pitch (VSVP) wind turbines to improve the rotor standard deviation of the wind turbines under normal and extreme wind conditions. The pitch H-infinity control algorithm only uses H-infinity control in the blade pitch control loop in the rated power region, and conventional torque gain scheduling algorithms are applied in the partial power region. The performance of this controller was verified using simulations of a 5 MW wind turbine using the commercial aeroelastic simulation code Bladed. The performance of the pitch H-infinity controller was compared with that of the conventional proportional-integral (PI) control algorithm under three different operating conditions: normal operating conditions without sensor noise, normal operating conditions with sensor noise, and extreme operating conditions without sensor noise based on the wind turbine design standard by IEC. Based on the simulation results with two different wind speed regions, namely, the transition region and the rated power region, it was found that the proposed pitch H-infinity controller showed better rotor speed standard deviation performance in the three operating conditions and achieved lower standard deviations of the rotor speed and the electrical power without affecting the mean electrical power.

In this study, a demanded power point tracking (DPPT) control algorithm was designed for the application of multiple-input multiple-output (MIMO) modern control algorithms. The proposed DPPT control algorithm has been newly implemented as a multiple reference trajectory method for applying an MIMO control algorithm without mode switches. Dynamic simulations and wind tunnel experiments were performed using a scaled wind turbine to validate the proposed control algorithm. The wind speeds were 4.6 and 7.3 m/s, the average wind speeds corresponding to region 2 and region 3, respectively, with a turbulence intensity of 10%. Both sets of results demonstrated satisfactory performance for tracking the power commands transmitted from the wind farm controller. Furthermore, the proposed control algorithm was compared and validated with a DPPT control algorithm proposed in previous studies, and its improved control performance and validity were confirmed.

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