Project

Master's project - Sensor Validation and Fusion for Safe Wind Turbine Operation

Publisher

Companies / Organizations

Location

Greater Copenhagen area

This project is intended as a Master's project for students with a background in control, sensing or mathematical modelling.

As part of the project it is encouraged for the student to spend 1-2 weeks at Vestas R&D headquarters in Aarhus.

Background

As a wind turbine is a highly dynamic structure, sufficient state information is key to secure an optimal control solution. Usually, such state information is obtained by means of selected sensors measuring e.g. rotor speed, wind speed, blade pitch angle, nacelle yaw direction, and blade loading, combined with state estimation algorithms.

Unreliable state information can affect the structural safety of the turbine if not handled correctly and provisions for handling faults and errors in the sensing systems must be present as part of the turbine control system. Redundant, multi-channel sensor system architectures is one way of securing reliable state information, but may be constrained by cost, space restrictions, and mounting possibilities. Therefore, it is of interest to seek solutions that provide the necessary fault detection capabilities while avoiding adding cost.

Scope

This project is focused on developing fault detection and sensor fusion algorithms with low detection time and low false detection rates. Both model-based methods such as Kalman filters and data driven methods such as Deep Neural Networks are of interest, but the solution should handle the challenge that the turbine dynamics are highly non-linear and that the turbine and environmental properties vary significantly.

An example could be verifying the blade load sensors measurements by consistency with the rotor, speed, pitch angle, produced power and measured wind speed.

It is of relevance also to investigate how the proposed solutions comply with relevant standards for functional safety, such as ISO 13849, IEC 62061, and IEC 61508. In this context, architecture and avoidance of common-cause failures is of interest.

Requirements

Master's student with focus on control, sensing or mathematical modelling

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