Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement Learning
Hydrokinetic turbines (HKTs) are a promising renewable energy source due to the consistency and high energy density in river and tidal resources. One of the primary barriers to the widespread adoption of HKT technologies is a high levelized cost of energy (LCOE). Considering the marine operating env...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/5/2512 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850050910042980352 |
|---|---|
| author | Samuel Barton Ted K. A. Brekken Yue Cao |
| author_facet | Samuel Barton Ted K. A. Brekken Yue Cao |
| author_sort | Samuel Barton |
| collection | DOAJ |
| description | Hydrokinetic turbines (HKTs) are a promising renewable energy source due to the consistency and high energy density in river and tidal resources. One of the primary barriers to the widespread adoption of HKT technologies is a high levelized cost of energy (LCOE). Considering the marine operating environment, the operation and maintenance costs are substantial. The power electronic converter, a key element in the electrical energy conversion system, is a common point of failure in direct-drive turbine applications—leading to increased maintenance efforts. This work presents a reinforcement learning (RL) method built within a quadratic feedback torque control framework to balance energy generation with power electronic device lifetime. The effectiveness of the RL-based control scheme is compared against a static baseline controller through two year-long tidal case studies. The results showed that the proposed method reduced cumulative damage on the device by upwards of 75% but reduced energy generation by up to 25.2%. Using a custom real-time cost estimation function that considers the sale of energy and an estimate of the costs associated with operating a device at a given temperature, it was found that the RL method can increase net income by up to 45.4% depending on the energy market conditions. |
| format | Article |
| id | doaj-art-63cd608009604b0a9d2d8e1d7e7dd067 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-63cd608009604b0a9d2d8e1d7e7dd0672025-08-20T02:53:19ZengMDPI AGApplied Sciences2076-34172025-02-01155251210.3390/app15052512Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement LearningSamuel Barton0Ted K. A. Brekken1Yue Cao2School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR 97331, USASchool of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR 97331, USASchool of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR 97331, USAHydrokinetic turbines (HKTs) are a promising renewable energy source due to the consistency and high energy density in river and tidal resources. One of the primary barriers to the widespread adoption of HKT technologies is a high levelized cost of energy (LCOE). Considering the marine operating environment, the operation and maintenance costs are substantial. The power electronic converter, a key element in the electrical energy conversion system, is a common point of failure in direct-drive turbine applications—leading to increased maintenance efforts. This work presents a reinforcement learning (RL) method built within a quadratic feedback torque control framework to balance energy generation with power electronic device lifetime. The effectiveness of the RL-based control scheme is compared against a static baseline controller through two year-long tidal case studies. The results showed that the proposed method reduced cumulative damage on the device by upwards of 75% but reduced energy generation by up to 25.2%. Using a custom real-time cost estimation function that considers the sale of energy and an estimate of the costs associated with operating a device at a given temperature, it was found that the RL method can increase net income by up to 45.4% depending on the energy market conditions.https://www.mdpi.com/2076-3417/15/5/2512reinforcement learninghydrokinetic turbinepower electronicslifetimemarine energy |
| spellingShingle | Samuel Barton Ted K. A. Brekken Yue Cao Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement Learning Applied Sciences reinforcement learning hydrokinetic turbine power electronics lifetime marine energy |
| title | Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement Learning |
| title_full | Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement Learning |
| title_fullStr | Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement Learning |
| title_full_unstemmed | Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement Learning |
| title_short | Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement Learning |
| title_sort | extending power electronic converter lifetime in marine hydrokinetic turbines with reinforcement learning |
| topic | reinforcement learning hydrokinetic turbine power electronics lifetime marine energy |
| url | https://www.mdpi.com/2076-3417/15/5/2512 |
| work_keys_str_mv | AT samuelbarton extendingpowerelectronicconverterlifetimeinmarinehydrokineticturbineswithreinforcementlearning AT tedkabrekken extendingpowerelectronicconverterlifetimeinmarinehydrokineticturbineswithreinforcementlearning AT yuecao extendingpowerelectronicconverterlifetimeinmarinehydrokineticturbineswithreinforcementlearning |