Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators

Abstract

The pitch controller of a floating offshore wind power system has an important influence on the power generation and movement of the floating body. It drives the turbine blade pitch using a hydraulic actuator, whose inherent characteristics cause a delay in response, which increases with the system capacity. As a result, the power generation is reduced, and the pitch motion of the floating body is increased. This paper proposes an advanced pitch controller designed to compensate for the delay in the hydraulic actuator response. The proposed pitch controller applies an artificial-intelligence-based deep learning algorithm to predict the delay time in the hydraulic actuator. This delay is compensated for by preferentially predicting the blade pitch control angle even if a delay occurs in the hydraulic actuator. The performance of the proposed pitch controller was analyzed using the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) v8 model developed by the US National Renewable Energy Laboratory and was compared against that of the ideal pitch controller and the pitch controller that reflects the response delay. Compared with the latter, the proposed method increased the average power generation by approximately 5% and reduced the standard deviation of the floating body’s pitch motion by approximately 50%.

A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed

“…In [105], an innovative CBP controller was introduced to address hydraulic actuator response delays. Leveraging a deep learning-based algorithm, the controller predicts delay times, thereby enabling the proactive adjustment of blade pitch control angles.…”

Forecasting Pitch Response of Floating Offshore Wind Turbines with a Deep Learning Model

“…The study used a GRU neural network to predict blade pitch control, countering the inherent delay in hydraulic actuator systems and consequently boosting turbine power generation efficiency. This approach improved power output by 5% and reduced pitch motion by about 50%, significantly enhancing FOWT structural integrity and performance [30].…”

Enhancing Power Generation Stability in Oscillating-Water-Column Wave Energy Converters through Deep-Learning-Based Time Delay Compensation

“…In order to overcome the challenges posed by wave variability, several studies have proposed the use of artificial intelligence (AI)-based deep learning techniques in renewable energy applications [22][23][24][25][26]. Asrari et al [22] proposed a method for predicting the hourly sunshine state for photovoltaic power generation; Mendonça de Paiva et al [23] conducted a study on the improvement of wind speed estimation performance for wind turbine systems; and Roh [27] conducted a study on large-scale wind turbine control. Ju et al [28] investigated an operation control algorithm to predict the generation output of renewable energy and increase the economic feasibility.…”