Potential Failure Detection Study in a 7 MW Offshore Wind Turbine Using Data-Driven Three-Stage Model Based on Pitch Anglea

Offshore wind energy is gaining significant global attention, making it essential to accurately predict potential faults in offshore wind turbines (OWTs) to ensure the stability of power grid operations. The pitch control system is a critical component that governs two key parameters: the blade twist angle and the pitch angle. The pitch angle, being dynamic, serves as a sensitive indicator for detecting subtle variations in blade orientation, which can reveal potential faults that may not be evident in the blade twist angle. This paper presents a three-stage, data-driven methodology for detecting potential failures in the pitch control system of specific 7 MW OWTs through dynamic pitch angle analysis. Stage 1 involves preprocessing raw supervisory control and data acquisition (SCADA) data, which includes anomaly detection and feature extraction. This process filters out obvious anomalies before training the model. Stage 2 involves developing a pitch angle prediction model that utilizes the relevant features identified in Stage 1 to forecast the pitch angle within a specified time interval. This model aims to accurately reflect the optimal operating conditions of the wind turbine by excluding data related to target faults. Stage 3 integrates the predicted pitch angles from Stage 2, which are dynamic parameters, along with selected features from Stage 1, into a model for predicting alarm signals. This model is designed to generate alarm signals for potential faults in the targeted OWT. Comparisons with six other sequential models demonstrate a higher accuracy, while reducing the number of feature extraction parameters. This indicates that the method can efficiently identify potential faults within the pitch control system by utilizing dynamic pitch angle parameters.