Accéder directement au contenu Accéder directement à la navigation
Nouvelle interface
Article dans une revue

Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews

Abstract : The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews.
Type de document :
Article dans une revue
Liste complète des métadonnées

https://hal-mines-paristech.archives-ouvertes.fr/hal-03481360
Contributeur : Magalie Prudon Connectez-vous pour contacter le contributeur
Soumis le : mercredi 15 décembre 2021 - 12:00:49
Dernière modification le : samedi 22 octobre 2022 - 05:18:28

Lien texte intégral

Identifiants

Citation

Luca Pinciroli, Piero Baraldi, Guido Ballabio, Michele Compare, Enrico Zio. Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews. Energies, 2021, 14 (20), pp.6743. ⟨10.3390/en14206743⟩. ⟨hal-03481360⟩

Partager

Métriques

Consultations de la notice

13