Authors :
Suresh; Vishali K; G. M V N Pavan Kumar; Jeeva Shirish Kumar Gonala; Bharani Kumar Depuru
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/45e9uxvk
Scribd :
https://tinyurl.com/37p6ypnn
DOI :
https://doi.org/10.5281/zenodo.10782281
Abstract :
In the field of sustainable energy guaranteeing dependability and effectiveness of wind turbines is paramount. This project addresses the significant challenge of unplanned wind turbine engine failures, which incur substantial economic losses and hinder electricity production. With the primary objective to reduce these failures by at least 30% and achieve annual cost savings of $2M through minimized downtime, we have harnessed advanced machine learning (ML) techniques to predict and prevent such incidents thereby aligning with both the business and economic success criteria.
Our methodology encompassed the creation of a framework for predictive maintenance that uses leveraging both present and historical operational data from wind turbines to forecast potential malfunctions prior to their manifestation. Through the utilization of diverse machine learning algorithms such as regression analysis, decision trees, and neural networks, the model is programmed to recognize irregularities and forecast points of failure with notable precision. This proactive maintenance approach not only strives to diminish unforeseen downtimes but also optimizes power output aligning with the project’s specifications.
Preliminary results indicate a promising reduction in the frequency of unplanned failures surpassing the initial target of 30%, which substantiates the effectiveness of our ml-based approach. Additionally, the project forecasts surpassing the anticipated economic savings, indicating a notable yield on investment and improved operational effectiveness.
This study shows how machine learning has a substantial influence on transforming wind turbine upkeep, it stands as a guiding example for similar initiatives across the renewable energy field offering key insights into how to achieve sustainable and reliable power production.
Keywords :
Predictive Maintenance, Machine Learning, Wind Turbines, Energy Efficiency, Failure Reduction, Renewable Energy, Turbine Reliability, Economic Savings, Anomaly Detection, Operational Efficiency.
In the field of sustainable energy guaranteeing dependability and effectiveness of wind turbines is paramount. This project addresses the significant challenge of unplanned wind turbine engine failures, which incur substantial economic losses and hinder electricity production. With the primary objective to reduce these failures by at least 30% and achieve annual cost savings of $2M through minimized downtime, we have harnessed advanced machine learning (ML) techniques to predict and prevent such incidents thereby aligning with both the business and economic success criteria.
Our methodology encompassed the creation of a framework for predictive maintenance that uses leveraging both present and historical operational data from wind turbines to forecast potential malfunctions prior to their manifestation. Through the utilization of diverse machine learning algorithms such as regression analysis, decision trees, and neural networks, the model is programmed to recognize irregularities and forecast points of failure with notable precision. This proactive maintenance approach not only strives to diminish unforeseen downtimes but also optimizes power output aligning with the project’s specifications.
Preliminary results indicate a promising reduction in the frequency of unplanned failures surpassing the initial target of 30%, which substantiates the effectiveness of our ml-based approach. Additionally, the project forecasts surpassing the anticipated economic savings, indicating a notable yield on investment and improved operational effectiveness.
This study shows how machine learning has a substantial influence on transforming wind turbine upkeep, it stands as a guiding example for similar initiatives across the renewable energy field offering key insights into how to achieve sustainable and reliable power production.
Keywords :
Predictive Maintenance, Machine Learning, Wind Turbines, Energy Efficiency, Failure Reduction, Renewable Energy, Turbine Reliability, Economic Savings, Anomaly Detection, Operational Efficiency.
