Journal of Fundamentals of Renewable Energy and Applications
Open Access

Recent advances of predictive big data analytics and cyber physical systems for smart wind turbine systems

World Congress and Exhibition on Wind & Renewable Energy

July 28-30, 2016 Berlin, Germany

Jay Lee

University of Cincinnati, USA

Posters & Accepted Abstracts: J Fundam Renewable Energy Appl

Abstract :

In today�s competitive wind energy business environment, companies are facing challenges in dealing with big data issues for rapid decision making for improved performance and asset management. Many wind turbine systems arenot ready to manage big data due to the lack of smart analytics tools. U.S. has been driving the Cyber Physical Systems (CPS) and Industrial Internet to advance future industry. It is clear that as more predictive analytics software and embedded IoT are integrated in today�s industrial products and systems, predictive technologies can further intertwine intelligent algorithms to predict windturbine performance degradation and autonomously manage and optimize service needs. The presentation will address the trends of predictive big data analytics as well as the readiness of smart predictive tools to manage wind turbine big data to achieve resilient life cycle management with improved service value.

Biography :

Email: jay.lee@uc.edu