Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants

Authors

  • Uhanto Uhanto Graduate School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia
  • Erkata Yandri Graduate School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia; Center of Renewable Energy Studies, School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia
  • Erik Hilmi Graduate School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia
  • Rifki Saiful Graduate School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia
  • Nasrullah Hamja Graduate School of Renewable Energy, Darma Persada University, Jl. Radin Inten 2, Pondok Kelapa, East Jakarta 13450, Indonesia

DOI:

https://doi.org/10.60084/hjas.v2i2.219

Keywords:

Operational data analysis, Failure prediction, Maintenance cost reduction, Component health monitoring, Energy efficiency optimization, Machine learning algorithms

Abstract

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.

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Published

2024-09-28

How to Cite

Uhanto, U., Yandri, E., Hilmi, E., Saiful, R. and Hamja, N. (2024) “Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants”, Heca Journal of Applied Sciences, 2(2), pp. 87–98. doi: 10.60084/hjas.v2i2.219.

Issue

Vol. 2 No. 2 (2024): September 2024

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Copyright (c) 2024 Uhanto Uhanto, Erkata Yandri, Erik Hilmi, Rifki Saiful, Nasrullah Hamja

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