Study on the Application of Neural Network in the Prognostic and Health Management of Wind Turbine

This research proposed a back-propagation neural network algorithm to establish Wind Power Forecasting model and implement the Health Assessment of wind turbine by Health Index which is defined using the error between forecast power and actually power. Based on wind turbine NO.4 in Linkou of Taipower, the system of prognostics and health management was set up with the data collected by the supervisory control and data acquisition(SCADA) system from 2012 to 2015. Then the Elman neural network was used to get the degradation of health index. Finally, health remaining useful life time of the wind turbine was predicted from the SCADA data. The analysis shows that the health remaining useful life time of the wind turbine NO.4 in Linkou of Taipower is about 15 years if the health index is defined as 0.15. The health assessment and health remaining useful life time of the wind turbine can be forecasted by the proposed neural network prognostics model and the criteria of health index. The developed prognostics and health management model can be used for wind turbine maintenance.