Monitoring Wind Turbines' Unhealthy Status: A Data-Driven Approach

Condition monitoring plays an important role in wind turbine maintenance, wherein monitoring turbines’ unhealthy statuses, mainly those not-runnable statuses causing turbines stop working, could be beneficial in both maintenance cost and wind power generation. A data-driven approach based on support vector data description (SVDD) and extreme learning machine (ELM) algorithms is proposed in this paper to realize effective monitoring on wind turbines’ unhealthy status. The SVDD algorithm is applied to separate data of unhealthy statuses from the healthy one. The ELM algorithm is used to construct a classifier for monitoring unhealthy (not-runnable) statuses of wind turbines. Industrial data from real wind farms are studied. Numerical results illustrate the feasibility of the proposed approach, and comparison studies with six monitoring algorithms validate that the proposed two-phase model could obtain better performance than other models.