Recent Progress and Performance Analysis on Durability Evaluation and rRemaining useful life Prediction Technology Development for the Life Extension of Wind Turbines in Korea

The digitalization of wind power generation is rapidly progressing with the development of ICT technologies such as big data, IoT, and artificial intelligence, along with the trend of large-scale wind power generation and the expansion of offshore wind power generation. As the number of wind turbines installed in power generation complexes decreases due to the increasing size of turbines, the availability, reliability, efficiency, and lifespan of each wind turbine are becoming increasingly important. In particular, digital twin technology for high-efficiency operation is expected to accelerate due to the large-scale expansion of offshore wind power and the increase in operation and maintenance (O&M) costs resulting from the scale and aging of turbines. In this paper, we intend to establish a foundation for extending the lifespan of wind turbines through performance analysis and durability evaluation of Korean wind turbines, as well as the development of remaining life prediction technology. To predict the remaining life of a wind turbine, we selected crucial components based on failure rates and downtime, focusing on identifying the core parts of the turbine. Actual SCADA and CMS operational data for key components were acquired, and system performance was analyzed using artificial intelligence and knowledge-based condition diagnosis algorithms. The remaining life prediction program will be enhanced using real-time sensor data obtained from the following performance analysis. The final outcome will be applied to real wind turbines in Korea for a long-term demonstration.