A Novel Proposal in Wind Turbine Blade Failure Detection: An Integrated Approach to Energy Efficiency and Sustainability

TL;DR

This paper introduces an integrated computational learning approach for early fault detection in wind turbine blades, combining clustering and logistic regression to improve reliability and sustainability in wind energy systems.

Contribution

It presents a novel methodology that integrates clustering and logistic regression models for more effective wind turbine blade fault detection.

Findings

Logistic regression outperformed neural networks, decision trees, and naive Bayes.

Clustering achieved higher precision and better data segmentation.

The approach enhances early fault detection and system reliability.

Abstract

This paper presents a novel methodology for detecting faults in wind turbine blades using com-putational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure.