EARLY ANOMALY WARNING OF THE WIND TURBINE CONTROL SYSTEM BASED ON ADABOOST ALGORITHM
Journal
Journal of Taiwan Society of Naval Architects and Marine Engineers
Journal Volume
41
Journal Issue
3
Date Issued
2022-01-01
Author(s)
Abstract
The 15 wind turbines’ SCADA data from a Taipower wind farm were analyzed in this study. First, an exemplary wind turbine was chosen by the least square error with the guaranteed power curve (GPC) among the 15 wind turbines. The generator speed versus wind speed data of the exemplary wind turbine was learned based on the Adaboost algorithm to predict the generator speed of the other wind turbines. The residues between the generator speed of the exemplary turbine and the other turbines were calculated. By figuring out the percent of the residue that is outside the exponentially weighted moving average control chart and then using one standard deviation of the proportional data as the threshold, the wind turbine’s proportion of residue beyond the threshold would be further analyzed. In the second stage, the wind speed was classified into 4 categories according to the control logic. The relation-ship between the generator speed and the blade pitch angle of the exemplary wind turbine was learned to predict the wind speed category. By comparing the discrepancy between the actual categories and that of the predicted categories with the confusion matrix, the fault of the wind speed category could be found, and the control system anomaly could be identified. Therefore, it could be used as the early anomaly warning of the wind turbine control system.
Subjects
Adaptive Boosting Algorithm | Confusion Matrix | Early Anomaly Warning | Exponentially Weighted Moving Average Control chart | Wind Turbine Control System
Type
journal article