Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in the academic community. We look forward to seeing you there!
PO025: Improving data sharing in practice - power curve benchmarking case study
Sarah Barber, Head of Wind Energy Innovation Division, Eastern Switzerland University of Applied Sciences
Abstract
A lack of data sharing is a widely-recognised challenge in the wind energy sector today, as demonstrated in a recent review paper. In order to improve data sharing, the WeDoWind framework has recently been developed and demonstrated in two use cases related to wind turbine fault detection. The main innovation of this framework is the way it creates tangible incentives to motivate different types of people to actually share data in practice. It helps people and organisations solve the most pressing problems collaboratively based on "challenges" and data provided by the industry. A recent assessment of participants' surveys filled out after the two case studies allowed several challenges related to data sharing to be identified, including the need to provide more clear comparison and evaluation criteria, to better integrate students into the process, to well-describe the data through standardised metadata and to prepare sufficient data for a given task. In this work, a further WeDoWind data-sharing case study is carried out, aiming to investigate and demonstrate how data sharing can be improved in practice in the wind energy sector. A "challenge" on the topic of wind turbine power curve benchmarking is created and implemented within the framework. The results allow five different data-driven power curve prediction methods to be compared. The best method reduces the model error by as much as 70% in terms of MAE and 45% in terms of RMSE compared to the standard industry method. The results of a survey filled out by the participants shows that data sharing can be improved in practice by providing clear comparison and evaluation criteria, as well as by better integrating students into the process via lectures and student projects.
