Wind Turbine Health Assessment Framework Based on Power Analysis Using Machine Learning Method

This paper proposes a performance assessment framework to estimate operation status of wind turbines. The overall objective is to propose a method for health assessment to support preventive maintenance strategies for wind turbines. The framework uses the data in the supervisory control and data acquisition systems as input. The framework consists of three main stages: power curve prediction, sliding window method analysis and performance assessment. At the first stage, k-means and density-based clustering are applied to eliminate noisy measurements. Then both parametric and non-parametric methods are applied to estimate the ideal power curve, which is used as a reference value to assess the actual one. At the second stage, the sliding window method is used to calculate the deviation between actual power data and ideal values, which indicates the real time performance of wind turbines. At the third stage, different performance zones are defined to assess health conditions. The proposed approach has been applied with the experience data of six onshore wind turbines from a single wind farm. The results indicate that the introduced framework can monitor the operation conditions and evaluate the performance of wind turbines.