Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power Generation
Presented in this study is an artificial intelligence approach to pitch angle control in wind turbines for the enhancement of the power generation efficiency of wind energy conversion systems. A two-input neural network model was developed and trained using backward propagation technique to adjust...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
UJ Press
2023-12-01
|
| Series: | Journal of Digital Food, Energy & Water Systems |
| Subjects: | |
| Online Access: | https://journals.uj.ac.za/index.php/DigitalFoodEnergy_WaterSystems/article/view/2892 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841555039104008192 |
|---|---|
| author | Titus Ajewole Mutiu Agboola Kabiru Hassan Adedapo Alao Omonowo Momoh |
| author_facet | Titus Ajewole Mutiu Agboola Kabiru Hassan Adedapo Alao Omonowo Momoh |
| author_sort | Titus Ajewole |
| collection | DOAJ |
| description |
Presented in this study is an artificial intelligence approach to pitch angle control in wind turbines for the enhancement of the power generation efficiency of wind energy conversion systems. A two-input neural network model was developed and trained using backward propagation technique to adjust the pitch angle of the turbine in response to the speed of turbine generator and the rate of change of the speed. Ten-year real-life data on the wind speeds of a study location was used to validate the approach. It was found that the method performs well in controlling the mechanical power developed by the turbine above the turbine’s rated wind speed, and with fast processing time. 44.44%improvement was achieved in the mechanical power developed by the turbine. The control approach is thus recommended for the effective management of wind energy conversion systems towards enhancing availability and reliability of electric power supply.
|
| format | Article |
| id | doaj-art-c57ebecfc3634ea69ff42a808f7289a9 |
| institution | Kabale University |
| issn | 2709-4510 2709-4529 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | UJ Press |
| record_format | Article |
| series | Journal of Digital Food, Energy & Water Systems |
| spelling | doaj-art-c57ebecfc3634ea69ff42a808f7289a92025-01-08T06:18:56ZengUJ PressJournal of Digital Food, Energy & Water Systems2709-45102709-45292023-12-014210.36615/digital_food_energy_water_systems.v4i2.2892Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power GenerationTitus Ajewole0https://orcid.org/0000-0001-7051-4304Mutiu Agboola1Kabiru Hassan2Adedapo Alao3Omonowo Momoh4Osun State UniversityFederal Polytechnic EdeFederal Polytechnic EdeOsun State Broadcasting CorporationPurdue University, Fort Wayne, USA Presented in this study is an artificial intelligence approach to pitch angle control in wind turbines for the enhancement of the power generation efficiency of wind energy conversion systems. A two-input neural network model was developed and trained using backward propagation technique to adjust the pitch angle of the turbine in response to the speed of turbine generator and the rate of change of the speed. Ten-year real-life data on the wind speeds of a study location was used to validate the approach. It was found that the method performs well in controlling the mechanical power developed by the turbine above the turbine’s rated wind speed, and with fast processing time. 44.44%improvement was achieved in the mechanical power developed by the turbine. The control approach is thus recommended for the effective management of wind energy conversion systems towards enhancing availability and reliability of electric power supply. https://journals.uj.ac.za/index.php/DigitalFoodEnergy_WaterSystems/article/view/2892pitch control, wind turbine, neural network, model training, model validation |
| spellingShingle | Titus Ajewole Mutiu Agboola Kabiru Hassan Adedapo Alao Omonowo Momoh Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power Generation Journal of Digital Food, Energy & Water Systems pitch control, wind turbine, neural network, model training, model validation |
| title | Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power Generation |
| title_full | Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power Generation |
| title_fullStr | Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power Generation |
| title_full_unstemmed | Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power Generation |
| title_short | Neural Network Approach to Pitch Angle Control in Wind Energy Conversion Systems for Increased Power Generation |
| title_sort | neural network approach to pitch angle control in wind energy conversion systems for increased power generation |
| topic | pitch control, wind turbine, neural network, model training, model validation |
| url | https://journals.uj.ac.za/index.php/DigitalFoodEnergy_WaterSystems/article/view/2892 |
| work_keys_str_mv | AT titusajewole neuralnetworkapproachtopitchanglecontrolinwindenergyconversionsystemsforincreasedpowergeneration AT mutiuagboola neuralnetworkapproachtopitchanglecontrolinwindenergyconversionsystemsforincreasedpowergeneration AT kabiruhassan neuralnetworkapproachtopitchanglecontrolinwindenergyconversionsystemsforincreasedpowergeneration AT adedapoalao neuralnetworkapproachtopitchanglecontrolinwindenergyconversionsystemsforincreasedpowergeneration AT omonowomomoh neuralnetworkapproachtopitchanglecontrolinwindenergyconversionsystemsforincreasedpowergeneration |