Predicting Wind Turbine Scheduling Maintenance Using Artificial Neural Network for Preventing Blade Breakage: Case Study Baron Techno-Park

  • Fredi Prima Sakti Universitas Teknologi Yogyakarta, UTY
  • Haidar Rahman Ridwan Budi Prasetyo
  • Ikrima Alfi Universitas Teknologi Yogyakarta, UTY
  • Ridwan Budi Prasetyo Badan Pengkajian dan Penerapan Teknologi, BPPT & Universitas Teknologi Yogyakarta, UTY
Keywords: Maintenance Strategy, Blade Breakages, Wind Turbine, Artificial Neural Network, Prediction

Abstract

One of the main issue that Baron Techno-Park (hybrid power plant) is facing are the practices of finding a suitable maintenance strategy. Operation and maintenance (O&M) of wind turbines are heavily affected by weather condition, particularly wind conditions. Blade failures, such as blade breakages, can lead to catastrophic consequences. The causes of blade breakages in Baron Techno-Park is due to unpredictable high wind speed from different directions. A technique that this research propose to implement a maintenance strategy in order to create an efficient O&M and also prevent the breakage of the wind turbine blades, is by using the Artificial Neural Network (ANN). ANN performance is satisfactory with the wind speed error of 30.25 % and wind direction error of 13.74 %. Also, R2 has a highest prediction of 0.998. Analyzing the survival wind speed of 60 m/s which is specify in the wind turbine specification. Analyzing the prediction results. It is safe to say that during the month of July 2021, it is not necessary for a maintenance schedule.

Submitted
2021-04-06
Accepted
2021-07-12
How to Cite
Sakti, F. P., Rahman, H., Alfi, I., & Prasetyo, R. B. (2021). Predicting Wind Turbine Scheduling Maintenance Using Artificial Neural Network for Preventing Blade Breakage: Case Study Baron Techno-Park. International Journal of Engineering Technology and Natural Sciences, 3(1), 23 - 26. https://doi.org/10.46923/ijets.v3i1.115