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.

The wake steering technique consists in misaligning turbines with respect to the incoming wind with the goal of displacing their wake region and reducing the wake wind speed deficit, thus increasing the power production of other turbines downwind. In this paper, an algorithm is proposed to estimate the amount of production gain a wind farm could achieve by employing this technique. Details about data treatment are provided and an analytical wake model that can represent the wakes displacement is described. A minimization algorithm to calibrate the wake model parameters based on SCADA data is presented, and the complete math needed to solve this problem is developed. Similarly, to find the optimal yaw angle values that maximize the wind farm production, a maximization problem is described along with the full development of the equations behind it. Insights on an efficient computational implementation to solve these problems are shown, based on matrix representation of the described variables. A case study is presented for a small wind farm owned by Voltalia, consisting of five turbines. Results point to an AEP gain in the order of 1%, a relevant value over the course of a project’s lifetime.

This paper proposes a failure prediction system for wind turbines using the Normal Behavior Model (NBM) approach. By using available SCADA data, the NBMs are trained to make predictions that reflect what would be a turbine’s normal operating condition. They are able to identify when a given operating condition is abnormal, which points towards probable component degradation. Alerts are raised based on the daily-averaged prediction error to help the O&M team in identifying turbines that need maintenance. The NBMs are comprised of numerous linear models with different inputs and training sets, according to an ensemble approach that aims to avoid overfitting and to reduce the amount of false-positive predictions. Description and insights on various development steps are presented, such as data treatment, model selection, error calculation and alerts generations. Two test cases are shown using operational data from existing wind turbines, highlighting the system’s ability to generate alerts weeks before a severe fault occurs.

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