Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study

Abstract

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method based on a digital ecosystem is developed and demonstrated. The method is centred around specific “challenges”, which are defined by “challenge providers” within a topical “space” and made available to participants via a digital platform. The data required in order to solve a particular “challenge” are provided by the “challenge providers” under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the solutions perform significantly better than EDP’s existing solution in terms of Total Prediction Costs (saving up to €120,000). The digital ecosystem is found to be a promising solution for enabling co-innovation in wind energy in general, providing a number of tangible benefits for both challenge and solution providers.

Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation

“…Finally, it can enforce transparency and robustness requirements for challenge solutions. In previous work, the method was tested on a case study, the "EDP Wind Turbine Fault Detection Challenge" [2]. The aim of this WeDoWind Challenge was "to identify failures in five of the major wind turbine components and advise an intervention to the wind farm operators in order to reduce corrective maintenance costs".…”

“…For example, the wide range of comparison metrics and Key Performance Indicators (KPIs) make it difficult to assess which metric or KPI is most suitable for a given application, especially when the heterogeneity of the models is high. As well as this, the conversion of these metrics into financial gains involves a number of different assumptions, and varying these assumptions in the EDP Challenge leads to a large variation in the results [2]. Finally, the quality of the evaluation results was thought to be dependent on the quality and quantity of provided data.…”

“…However, various recent studies have concluded that more data should be made available and comparisons with a wide range of different data should be carried out in order to do this, e.g. [26], [2], [27]. We aim to contribute to this gap in the present work.…”

“…In order to close this gap, a new collaboration method called WeDoWind was developed in recent work by the present lead author [2], as described below. The main innovation of this method is the way it creates tangible incentives to motivate and empower different types of people from all over the world to actually share data and knowledge in practice.…”

“…The method positively demonstrates the prediction failure of wind turbine 114, which is necessary to understand the behaviour of the wind turbine for a long period before the turbine failure and not just the exact instant that turbine failure. The same method was applied to the EDP Wind Turbine Fault Detection Challenge [2], where the behaviour of the turbines was different from this current challenge, with only one FP detected before the turbine failure. Therefore, the complexity and variety of the behaviours before the turbine's failure demand more data sets for training the Machine Learning algorithm and for the expert to understand and learn different FPs that may be considered as TP alarms and take the right decision to prevent the turbine failure.…”