Machine Learning for Wind Farm Control

About the project or challenge area

Given the complexity and stochastic nature of the relevant physics, it is possible that the use of machine learning algorithms could provide a practical approach to the optimization of performance of wind farms on a project scale. We want to adapt largely autonomous data-driven new control strategies for wind turbines based on existing sensor information to optimize wind farm scale performance. We will investigate the use of historical wind farm measurements to develop a non-physical model of the system, using a statistical approach to the prediction of future performance from information contained in the wind farm data. A supervised learning algorithm will be utilized to analyse the wealth of operational data recognizing conditional state patterns. This will be coupled with additional expert knowledge of wind farm operation to form the basis of a model to optimize power output and minimize fatigue loading for control.

We propose a model based on training data from the DNV GL dynamic wind farm simulator and real wind farm data from public sources. Wind farm data is used in cross-validation and parameter tuning, thus eliminating any bias from simulated environments. The use of high-frequency SCADA data holds potential for real accuracy gains within this framework. A data-driven machine learning model benefits from real-time updated learning with incoming operational data, reduced computational time for implementation, and a lack of dependence on physical model assumptions. The research problem is nontrivial as it involves complex data, which are characterized by nonlinearity, nonstationary and high dimensionality. The state space is very high-dimensional, which leads to problems in many machine learning methods. Solution methods must deal with incomplete system observations. SCADA has missing or erroneous data. Controllers work on live feeds. Another challenge is given by the existence of different time scales in typical real systems. In particular, both the quick and the slower dynamic components must be captured without devaluing each other as noise.

Why choose this opportunity?

The project will allow you to conduct state-of-the art machine learning research in partnership with DNV GL, a world leading renewable energy consultancy. 

About you

This project would suit a candidate with strong interests in wind energy, machine learning and control methods.

How to apply

All students can apply using the button below, following the Master's by Research Admissions Statement. Please note that this is an advertised project, which means you only have to complete Section A of the Masters by Research Statement Template (Office document, 68kB).

Before applying, we recommend getting in touch with the project's supervisors. If you are interested in this project and would like to learn more about the research you will be undertaking, please use the contact details on this page.

Supervisor

Your supervisor for this project will be Paul Harper, Teaching Fellow in the Faculty of Engineering. You can contact him at +44 (0) 117 95 45159 or email Paul.Harper@bristol.ac.uk.

Co-supervisor

Your co-supervisor for this project will be Ervin Bossanyi, Visiting Professor in the Faculty of Engineering. You can email him at ervin.bossanyi@bristol.ac.uk.

Find out more about your prospective research community

The Low Carbon Energy theme is a vibrant community of researchers who integrate expertise across multiple disciplines to develop sustainable energy policy and technologies which are crucial to providing a safe, reliable and low-cost energy supply for a growing global population. We innovate in every part of the energy system, from generation and storage, to regulation and end-user demand Find out more about the Low Carbon Energy theme.

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