An Automatic Data Cleaning and Operating Conditions Classification Method for Wind Turbines Scada System

Abstract

The Supervisory Control and Data Acquisition (SCADA) system can provide significant information about the wind turbine health monitoring. However, the SCADA data contains enormous abnormal state data due to wind curtailment, equipment or sensor malfunction. In addition, complicated and constant changing operating conditions pose great challenges to effective and reliable fault detection. In this work, an automatic data cleaning and operating conditions classification approach is proposed. First, fundamental characteristics of different operating condition are analyzed according to control strategy, and data cleaning rules are constructed to remove abnormal state data. Then, change-point method is adopted to further clean residual abnormal state data that is stacked below the normal generation state data in the generator rotor speed-power curve. Second, the operating conditions classification criteria are constructed based on fundamental characteristics of different operating stages, and the classification parameter values are obtained adaptively according to data characteristics of different operating stages. Finally, the data cleaning and operating conditions classification approach is evaluated on the real-world SCADA dataset that was collected from a wind farm in East China. The results demonstrate that the proposed method can effectively distinguish normal power generation state data from abnormal power state data and realize operating conditions classification for different wind turbines automatically.