Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/9/3136 |
| _version_ | 1797505028452253696 |
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| author | Chan Roh |
| author_facet | Chan Roh |
| author_sort | Chan Roh |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-10T04:12:49Z |
| format | Article |
| id | doaj.art-23eeff7289cd4e5da59b030762f4f1bc |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:12:49Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-23eeff7289cd4e5da59b030762f4f1bc2023-11-23T08:07:04ZengMDPI AGEnergies1996-10732022-04-01159313610.3390/en15093136Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic ActuatorsChan Roh0Department of Energy Engineering, In-Je University, 197 Inje-ro, Gimhae-si 50834, KoreaThe 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%.https://www.mdpi.com/1996-1073/15/9/3136floating offshore wind turbinepitch controllerhydraulic actuatortime delaydeep learning algorithmlong short-term memory |
| spellingShingle | Chan Roh Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators Energies floating offshore wind turbine pitch controller hydraulic actuator time delay deep learning algorithm long short-term memory |
| title | Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators |
| title_full | Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators |
| title_fullStr | Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators |
| title_full_unstemmed | Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators |
| title_short | Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators |
| title_sort | deep learning based pitch controller for floating offshore wind turbine systems with compensation for delay of hydraulic actuators |
| topic | floating offshore wind turbine pitch controller hydraulic actuator time delay deep learning algorithm long short-term memory |
| url | https://www.mdpi.com/1996-1073/15/9/3136 |
| work_keys_str_mv | AT chanroh deeplearningbasedpitchcontrollerforfloatingoffshorewindturbinesystemswithcompensationfordelayofhydraulicactuators |