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All Journal International Journal of Engineering, Technology and Natural Sciences (IJETS)
Fredi Prima Sakti
Universitas Teknologi Yogyakarta, UTY
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Predicting Wind Turbine Scheduling Maintenance Using Artificial Neural Network for Preventing Blade Breakage: Case Study Baron Techno-Park Fredi Prima Sakti; Haidar Rahman; Ikrima Alfi; Ridwan Budi Prasetyo
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 3 No 1 (2021): International Journal of Engineering, Technology and Natural Sciences
Publisher : University of Technology Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (324.559 KB) | DOI: 10.46923/ijets.v3i1.115

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.
Co-Authors Haidar Rahman Ikrima Alfi Ridwan Budi Prasetyo