Abstract
Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.
Highlights
A significant proportion of the UK energy mix is currently made up of wind energy, both onshore and offshore, and this is only likely to increase
This paper presents a method of condition monitoring using the anomaly detection model of Isolation Forest [11]
This paper examines the turbines with electric pitch systems to compare the model effectiveness for different components; Examines different aggregate window lengths, these being daily, weekly, and monthly anomaly proportions to assess what window length improves; Compares healthy and unhealthy turbine performance, to assess if the model can differentiate
Summary
A significant proportion of the UK energy mix is currently made up of wind energy, both onshore and offshore, and this is only likely to increase. The transition from a wind electricity market dominated by onshore turbines to one made up with mostly offshore generation will bring about greater challenges, such as increased downtime and the associated costs. These costs can be prohibitive to wind farm developers, and the uncertainty around when downtime can occur adds to this. Operations and maintenance (O&M) is an important area of wind energy research This is due to the previously stated O&M costs, which can be between 20% and 25% of total levelised cost of energy (LCOE) for current wind turbine projects [2], whilst LCOE has been dropping from over $100 per MWh for onshore since 2009 to roughly $50 per MWh in 2019 [3].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
