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Digital twins are becoming a business imperative, covering the entire lifecycle of an asset, and forming the foundation for connected products and services. Companies that fail to respond will be left behind. Implementing a dynamic cloud model of a physical thing or system such as wind turbine that relies on live streaming data will help to understand its states, respond to changes, improve operations, and add value to its Key Performance Indicators (KPIs) such as reliability, availability, maintenance cost and associated risks. This paper contributes to build a power prediction digital twin for a wind turbine’s generic model guided by IEC 61400-25, IEC 61400-27-1-2020 (Type 4A) via utilizing the data analytics of Microsof to Azure IoT mechanisms along with decentralized decisions of Machine Learning (ML) in such way utilizing its strengths in physics-based, data-driven modeling and the hybrid analysis approaches. The proposed modeling technique can help the scientific community in building long-term maintenance models for wind farms considering maintenance opportunities and condition prediction, as well as evaluating the machine performance including maintenance costs and production losses.
Issa et al. (Sun,) studied this question.
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