With growing wind energy capacity, especially offshore, reliability of wind turbines (WT) becomes a relevant concern. Poor reliability directly affects their cost effectiveness due to increased operation and maintenance (O&M) costs and reduced availability to generate power because of downtime. This certainly encourages WT operators to employ advanced O&M methodologies and focus on the critical components to reduce failure rate, time to repair and maximizing WT performance. Condition monitoring (CM) of wind turbines for the purpose of prognostics and health management of critical equipments can improve the reliability and reduce maintenance costs by identifying failures at the earliest possible stage and by eliminating unnecessary scheduled maintenance. In contrast to the expensive purpose-built condition monitoring systems, a SCADA (Supervisory Control and Data Acquisition System) data-based condition monitoring system uses data already collected at the wind turbine controller and provides a cost-effective way to monitor wind turbines.
This research focusses on developing a prognostics framework for WT gearboxes, which are one of the costliest subsystems to maintain during a turbine’s life. The framework follows a data-driven approach and combines two machine learning algorithms – Artificial Neural Network and Support Vector Machine to capture anomalous operations of the WT gearbox. A real-time monitoring scheme is developed to track the degradation and set a maintenance alarm as the first evident signature of failure is identified. The framework was implemented using high-frequency SCADA data and was able to detect gearbox failure, a month in advance, providing enough lead time to plan and perform required maintenance activities. Additionally, a sensitivity study is conducted to determine an optimal sampling frequency of SCADA data which can be used for CM purposes as the current industry practice of storing it as 10 min averages leads to a loss of information about the condition of a WT component.
The results show that the feedforward ANN can efficiently learn the complex mapping between the input and output features. To analyse the error between ANN predictions and the in-field measurements, four residual error features maximum error, minimum error, root mean squared error and error distribution are used as inputs for the OCSVM model to understand the complex boundary between normal and anomalous operation. The percentage of anomalies computed for each week of operation, 4 months before failure, show an increasing trend as the turbine approaches failure. To determine a threshold for maintenance alert, a realtime monitoring scheme based on linear regression and bootstrapped confidence intervals is developed to track the progression of anomalies and alarm a maintenance alert as the first indication of incipient fault becomes evident. The scheme alarms for maintenance a month before the actual failure, providing enough lead time to plan and maintain the gearbox.
A sensitivity study is carried out for a range of sampling periods ranging from 100 Hz to 10 min. The results demonstrate that highfrequency SCADA data is beneficial for condition monitoring of the gearbox, but only if the noise in the data can be excluded. On the other hand, despite the loss of information due to the averaging effect for large sampling periods, SCADA data aggregated over a 30 s period could be utilized to predict the gearbox failure a month in advance. Furthermore, the ANN model performance is found to be sensitive to the number of data samples available for training.