09:00 - 10:30 Loads and fatigue
Room: Hall G2
In this session, participants will hear about the latest developments in wind turbine loading and system behaviour. Presentations will cover a variety of topics - from modelling approaches to turbine control. The research work presented brings new approaches which will help the industry increase the reliability of turbines and reduce installation costs, especially offshore. Presented results will rely on real-world measurements and operational data.
You attended this session?
- Understand how to realise dynamic model validation;
- Get into an approach to describe wind-wave-correlation for simplified but still accurate loads simulation;
- Learn about the effect of tuned mass damper on offshore wind turbine (OWT) tower loads;
- Learn about load reduction by means of individual pitch control based on simulation results and measurements.
Sebastian Kaus (1) F Bert Gollnick (1) Niko Mittelmeier (1) Tomas Blodau (1)
(1) Senvion, Hamburg, Germany
Presenter's biographyBiographies are supplied directly by presenters at WindEurope Summit 2016 and are published here unedited
Mr. Sebastian Kaus studied ‘Renewable Energies’ and ‘Sustainable Electrical Energy Supply’ at the University of Stuttgart from 2009 until 2015. He’s currently working at Senvion in Hamburg as Specialist Wind Farm Performance Monitoring.
His main emphasis during his studies were grid integration of renewable energies and wind energy. He was also project head in a student project which developed and built a wind powered vehicle.
Now he focuses on machine learning and data science for performance of wind turbines .
Machine learning algorithms for wind turbine power performance monitoring – tracking optimal yaw alignment based on SCADA data
Stakeholders in wind energy are interested in optimal wind farm operation. Key to an optimal operational wind farm performance is high availability and good quality of power performance in operation.
Wind turbines provide a large amount of data which is regularly used for availability calculation and ad-hoc analyses of specific questions.
An increasing number of wind farm operators as well as consultants are analysing the performance of wind turbines based on SCADA data. Occasionally results show sub-optimal performance. There are multiple reasons why an OEM cannot directly apply parameter changes concluded from third party analysis: contractual arrangements, uncertain technical expertise of consultant or unclear methodologies. Hence, time-consuming validation of these results by the OEM is necessary, which causes delays and may result in yield loss.
To obtain good and reliable results, a deep understanding of turbine technology is required for modelling. Machine learning helps to create more sophisticated models. These algorithms create knowledge based on experience and permanently update this knowledge. In general, the more data available and taken into account, the better the model and results. OEMs have a large amount of data and possess deep knowledge of their turbines ideally positioning them to implement machine learning techniques.
Machine learning aggregates different statistical approaches for supervised and unsupervised learning based on large datasets. This methodology is already applied in wind energy, e.g. for predictive maintenance. Similar to predictive maintenance, we show that machine learning can also be applied to maintain optimal turbine performance.
A combination of different data sources are used in an ensemble of machine learning algorithms. 10 minute averaged sensor data, wind turbine parameters and operation counters are aggregated and used to calculate key performance indicators for wind turbines. Instead of using a single set of turbine data, a data set of the entire fleet of identical turbine types is taken into account. Fleet characteristics are established and wind turbines are regularly investigated by the algorithm. Turbines showing suspicious operational characteristics are checked for validity in the field and the calculated key performance indicators are added to the training set. This way, machine learning algorithms are trained on an increasing size of data sets and results improve over time.
Main body of abstract
Turbine yaw misalignment is seen as one of the largest sources of power losses during operation. Detection of turbines with yaw errors in the field is not trivial. Typically there is no met mast and thus no reliable reference for wind direction.
In a wind farm with twelve turbines a yaw error was manually triggered on two turbines for a fixed period of time shorter than a month. The presented model was able to successfully detect the turbines with yaw error based on the described approach. The number of training sets as well as the test data is presented and a validation case is demonstrated.
Further case studies show detection of e.g. sensor faults and changes in parameters.
OEMs possesses large amounts of data and a deep understanding of their turbine technology. Both was used to develop statistical models for wind turbine power performance monitoring. Model precision increases with increasing number of training data. The presented methods were able to learn autonomously. This analysis process is highly automatable and can be easily applied to new sites. Corrective actions can be taken immediately – no third party is involved and no additional check of analysis required. Our results indicate that it is possible to detect errors in an early phase to maintain optimal performance.
Monitoring an operational wind farm with respect to maintain optimal turbine power performance. Turbine yaw misalignments can be detected by machine learning algorithms.