A global sustainable energy supplier minimized downtime and boosted turbine performance by leveraging AI-powered data analysis for predictive maintenance.

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Our client, a leading player in the wind energy industry, was able to manage its wind turbine fleets thanks to the implementation of the Internet of Things. Our engineers have developed a system in which sensors on the turbines collect data on the condition of the wind turbines, which is transmitted to a centralised system. This system analyses the data and generates reports to optimise maintenance.


The client is a company that plays an important role in the development of renewable energy and the fight against climate change. The company manufactures, sells, installs and maintains wind turbines. The company is the world’s largest wind turbine manufacturer and has installed more than 173 GW of wind turbines in 88 countries, preventing 1.9 billion tonnes of CO2 emissions.


The client, as a leading player in the wind energy sector, aims to maximize the performance of its wind turbines and minimize downtime. However, the company has encountered a range of issues related to turbine inspection data management:

  • Inspection reports were provided in different formats, such as JSON and PDF, complicating their collection, analysis, and storage.
  • Reports were stored in various locations, making access challenging and time-consuming.
  • Manual analysis of inspection data was labour-intensive, inefficient, and prone to errors.


Computools has developed and implemented a comprehensive IoT-based management system to optimise the handling of wind turbine inspection reports. It includes:

  • Centralised storage: all CIR (Critical Inspection Reports) and BIR (Basic Inspection Reports) are stored in one place for easy access and analysis.
  • Automated Analysis: The system automatically extracts key data from the reports, such as blade damage categories and assessment statuses, allowing you to quickly and efficiently assess the condition of your turbines.
  • Business logic module: the system uses business rules to prioritise maintenance tasks, allowing you to optimise resources and increase efficiency.
  • User-friendly interface: The system provides a user-friendly interface that allows users to easily access and manage inspection data.


The use of real-time sensors contributed to a 5% increase in mean time between failures (MTBF), reduced operational costs and time with less number of involved personal. This, in turn, led to an increase in annual electricity generation and revenue from electricity sales. The client also increased safety by removing the necessity of high-altitude work tasks.


The client was looking for a solution to manage wind turbine inspection data. They held a tender, inviting proposals from various companies, including off-the-shelf solutions. We ran a pilot project that demonstrated our skills and fit with the client’s requirements. Different people from the client’s company were involved in the decision making process, ensuring a thorough selection. Our experience in developing similar systems and knowledge of wind energy also played a role.

We immediately established open communication using Slack and Jira. At first we focused on centralised storage of all reports (CIR & BIR) and automatic extraction of key data from them. The client appreciated the open communication and co-operation. The pilot project showed our competence. We were quick to address his key concerns.

In the end, we were chosen due to our experience, pilot project and open approach to work.

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The wind turbine manufacturer, our client, faced a significant challenge in managing their wind turbine inspection data. This data is crucial for preventive maintenance and keeping their turbines running smoothly.

Approach to solution

Our client explored various solutions to address their challenges. Initially, they considered the possibility of finding an off-the-shelf solution. However, after careful consideration of their needs, they ultimately decided to engage an external contractor for the development of the system.

Computools role

Computools, as a partner for the development and implementation of a comprehensive data management system, assessed the client’s requirements, developed a technical specification, designed the system, and then proceeded with its development and implementation. Throughout the project, our engineers made several key decisions:

  • Utilizing Java, JSON, and MSSQL DB for system development.
  • Developing a business logic module to automate data analysis.
  • Implementing a caching mechanism to enhance performance.

Key decisions and outcomes

Our team proposed a solution based on the implementation of sensor devices to help the client monitor the condition of wind turbines, predict breakdowns, and address them promptly. After identifying the client’s needs, our engineers developed the solution. To date, Computools provides technical support for the system and assists in its further enhancement. Both parties are discussing the possibility of expanding collaboration in developing new IoT solutions.

The new data management system enabled the client to enhance the efficiency of managing their wind turbine park and improve their financial metrics.


During the development of the data management system, Computools placed significant emphasis on design considerations. The goal was to create an intuitive and user-friendly interface, enabling users to work with data easily and efficiently.

drone analytics user persona


Detailed profiles to guide solutions for turbine data management.

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Hierarchical structure outlining seamless access to inspection reports and analytics

drone analytics wireframes


Schematic representations for efficient layout and interaction planning of the system

drone analytics user interface


Streamlined design for users to interact seamlessly with turbine inspection data



The project was managed using the Scrum methodology, chosen for its structured and iterative approach to project management. Key aspects of Scrum were incorporated to ensure efficient planning, execution, and adaptation throughout the project lifecycle. Scrum was selected for its ability to provide clear roles, responsibilities and sprint-based planning, which facilitated effective collaboration and project progress tracking.



project timeline drone analytics


Before finalizing our choice with the Computools team, we initiated a tender, inviting proposals from various potential partners. The decisive factor in choosing Computools was a pilot project that allowed us to assess the team's practical capabilities and their alignment with our requirements.

On our part, the decision to collaborate involved the company owner, product coordinator, subject matter expert, technical lead, and the SRE team. This collective approach ensured a comprehensive evaluation, considering various perspectives and criteria. The contractor's expertise in developing similar data management systems and the ability to offer solutions tailored to the specific needs of the wind energy sector influenced our decision.

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