of present installations, there comes an increasing need for optimal operation and maintenance, and a growing interest in asset lifetime extension. According to IRENA’s report on Generation Costs in Renewable Energy, O&M costs can represent even up to 25% of the Levelized Cost of Electricity. Besides, turbine downtime represents a significant portion of costs too. Some components, like turbine gearbox, break down extremely rarely and it is not financially justified to store them on-site. On the other hand, lack of a part for replacement can cause a turbine a pause in operation for many days or even months, generating monetary losses. On top of that, a single wind turbine produces hundreds of data readings every minute, usually used for manual monitoring and stored on the wind farm.

What is our product?

A thorough consideration leads to the conclusion that a solution bridging the gap between available data and its easy manipulation is greatly needed. Proper data handling may not only enhance the management of the wind farm but also lead to valuable insights, improving its overall performance. The existing advancements in machine learning and big data analytics allow to unlock the potential of renewables, decreasing their overall costs and improving their integration into the power system.

WindAlly is a comprehensive software that gathers and analyses data collected from the wind farm, providing valuable functionalities to all stakeholders engaged in the process of wind asset management. The system offers a broad range of capabilities, including wind turbine failure detection, forecasts of wind energy production, facilitated inventory management as well as maintenance work optimization. Resulting predictive maintenance leads to a smaller number of failures in a wind turbine, therefore increasing the odds for asset lifetime extension. All improvements can generate savings up to €20k per megawatt of wind turbine installed for each year over its lifetime.

Concluding, WindAlly is aimed at enhancing the work of wind asset managers, wind operation managers and maintenance team, ultimately decreasing the O&M costs of a wind farm and improving its performance.

How does WindAlly work?

At WindAlly, we are pursuing a holistic approach to wind farm management. The software that we offer is based on four main components:

SCADA integration – by integrating data primarily managed by various SCADA systems, the software is aimed to give an access to powerful and concrete insights coming from the data. Failures detection - machine learning-based algorithms are responsible for early failure detection, thus enabling predictive maintenance and consequent cost reductions. Energy production forecasts - advanced models based on artificial intelligence ensure accurate electricity generation forecasts, increasing the ability to avoid costly under- or over-productions. Inventory management - constant insight into spare components in stock along with an updated comparison of available offers enables optimization and automation of upcoming orders. The combination of energy production and maintenance forecasts empowers the wind farm managers to optimize maintenance works by aligning O&M with low energy production, assuring the least costly operation. Further, reduced number of unexpected failures, together with improved inventory management, allow for a decrease in turbine downtime and resulting cut of expenses.

The software’s interface is flexible and simple. It is designed to be tailored for each actor engaged in the process of wind farm management and to provide an easy access to the most important information. Additionally, a compatible architecture of our software allows for an integration of any tools that the user currently works with into the platform, eliminating the need for using multiple systems.

Leveraging leading market solutions like Amazon AWS and Microsoft Azure, gives WindAlly virtually unlimited data processing resources. With the use of products like S3, ingestion of large quantities of data is simple and efficient, allowing developers to focus on developing advanced machine learning solutions to provide industry-leading models.