Predicting wind turbines faults using Multi-Objective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@Article{Daaji:2025:eswa,

• author = "Marwa Daaji and Mohamed-Amin Benatia and Ali Ouni and Mohamed Mohsen Gammoudi",
• title = "Predicting wind turbines faults using Multi-Objective Genetic Programming",
• journal = "Expert Systems with Applications",
• year = "2025",
• volume = "281",
• pages = "127487",
• keywords = "genetic algorithms, genetic programming, Wind turbine, Faults, Evolutionary search, Multi-objective optimization, ANN",
• ISSN = "0957-4174",
• URL = "https://www.sciencedirect.com/science/article/pii/S0957417425011091",
• DOI = "doi:10.1016/j.eswa.2025.127487",
• abstract = "Wind turbines are a key component of renewable energy, converting wind into electricity with minimal environmental impact. Ensuring their continuous operation is crucial for maximizing energy production and reducing costly downtimes. To extend their operational lifespan, proactive maintenance strategies that predict and address potential faults are essential. While Machine Learning (ML) and Deep Learning (DL) algorithms have demonstrated significant promise in detecting wind turbine faults, they often prioritize maximizing the detection of failures without giving sufficient attention to false alarms. In practice, false alarms are just as problematic as undetected failures, as they reduce efficiency and waste resources. In this paper, we propose a novel optimisation approach using Multi-Objective Genetic Programming (MOGP) to predict wind turbine faults. Our approach seeks to identify the best combination of features and their threshold values by optimising two conflicting objectives: maximizing fault detection while minimizing false alarms. This dual-objective strategy ensures reliable fault prediction while minimizing unnecessary maintenance actions. We assess the effectiveness of our approach using real-world Supervisory Control and Data Acquisition (SCADA) data from a wind turbine in southern Ireland. The results demonstrate the efficiency of our approach in fault identification, achieving a competitive balance between recall (91percent) and false positive rate (21percent). While machine learning (ML), specifically Random Forest (RF), shows promising performance with a recall of 91percent and a 10percent false alarm rate, it remains a black-box model. RF lacks interpretability, making it challenging to extract meaningful insights into the relationships between sensor features and fault occurrences",

}

Genetic Programming entries for Marwa Daaji Mohamed-Amin Benatia Ali Ouni Mohamed Mohsen Gammoudi

Citations