@inproceedings{b14290c449c64cf4a4da8061d441684f,
title = "Implementation of Deep Generative Model for Generating Synthetic Wind Speed Data for Offshore Wind Turbine Maintenance Exploration",
abstract = "The optimal maintenance strategy for wind turbines relies heavily on estimation by exploring wind speed scenarios, considering the dynamic nature of environmental conditions. In this study, we implement a deep generative model leveraging the Generative Adversarial Network (GAN) algorithm, emphasizing its application for generating synthetic time-series data, such as wind speed, known as DoppelGANger architecture. By generating synthetic wind speed scenarios that incorporate the complexity and variability of real-world wind patterns, the synthetic data provide a better understanding of how maintenance strategies can be tailored to different operational scenarios.",
keywords = "GAN, scenario generation, synthetic data, time-series, wind speed",
author = "Hendradewa, \{Andrie Pasca\} and Shen Yin",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 33rd International Symposium on Industrial Electronics, ISIE 2024 ; Conference date: 18-06-2024 Through 21-06-2024",
year = "2024",
doi = "10.1109/ISIE54533.2024.10595733",
language = "英语",
series = "IEEE International Symposium on Industrial Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 33rd International Symposium on Industrial Electronics, ISIE 2024 - Proceedings",
address = "美国",
}