AI for Wind Energy Challenge
Extend the lifetime of wind turbine blades by identifying the damage based on ambient vibration data.
Extend the lifetime of wind turbine blades by identifying the damage
To achieve the EU-climate goal of 100% CO2-free energy production in 2050, 15% to 50% of Europe’s electricity is to come from wind energy. This means 50.000+ new offshore turbines needed for 22x increase in European offshore wind power capacity.
Blades are the last frontier for predictive maintenance, causing major reduced power output and efficiency as wind parks age. The result is immense costs of inadequate detection of blade damage. The periodic and visual damage using specialists or drones on-site occurs only at intervals, requires stoppage and does not monitor inside the blade for structural health monitoring (SHM) predictive maintenance. Damage identification based on ambient vibration data is one of the most effective SHM technologies that allows the continuous acquisition of the structural response.
The lack of an – in all weather conditions reliable – SHM is one of the reasons hindering the widespread adoption of these techniques for industrial applications. It is crucial that a developed SHM is reliable and has a low amount of false alarms. Wrong interpretations of data can lead to unnecessary inspections driving up the cost. Differentiating between novelties caused due to the inﬂuence of Environmental and Operational Variables (EOVs) or damage in the structure can help reduce false alarms.
Rather than working together to solve what’s clearly an industry-wide problem, everyone appears to be scrambling to figure it out on their own. Trade secrets don’t often pass between corporations, yet that may be exactly what is needed to rid wind power of some of these efficiency problems.
Our challenge partners
GOAL: Identifying damage of wind turbine blades with ambient vibration data
This challenge can be broken down in the following steps:
- Preprocessing of raw vibration data (e.g. with high low pass filters used by the industry)
- Discover which ML model is best at extracting damage sensitive features from the influence of environmental and operational variables.
- Replicate and build upon the state-of-the-art damage detection algorithm.
Damage is represented as the modal parameters (frequency, shape and damping) shifts on the wind turbine blade. The difficulty for the algorithm is that the environment and operational variables (EOVs) impact these parameters (5-10% variations in frequency), so the solution needs to be:
- Robust to nonstationary conditions. Nonstationarity primarily stems from constantly-changing wind and loading conditions (gusts, turbulence, etc.).
- Localize damage. Damage localisation is challenging for operational wind turbines, since this is a function of excitation frequency and number of sensors, as well as data-acquisition rates and sensor placement.
Who are we looking for?
We expect some experience with programming languages and an interest in machine learning. Anyone with the right motivation and ‘proof’ of understanding of the core concepts found in the application form can sign up!
During this challenge you will use / learn these tech skills:
- Convolutional Neural Networks
- XGBoost with SHAP
- outlier analysis
You can join as a contributor (12 hours per week commitment for 2 months), coach (2-4 hours per week, only for experienced ML professionals) and teacher (give one relevant ML / domain masterclass).
You will collaborate with a diverse team of over 35 international data specialists and domain experts in 3 subteams - data- engineering, feature engineering and modeling. By the end of the challenge you’ll be certified as an AI for Energy engineer by our community & partners!
You will be coached by experts from the field, like:
- Industry expert: HBK Senior Engineer Dmitri Tcherniak
- CTO Jesse van Kempen, CEO Hans van Beek at Tarucca
Did you know
💨 To achieve the EU-climate goal of 100% CO2-free energy production in 2050, 15% to 50% of Europe’s electricity is to come from wind energy.
🇪🇺 This means 50.000+ new offshore turbines needed for 22x increase in European offshore wind power capacity.
🛠 Blades are the last frontier for predictive maintenance, causing major reduced power output and efficiency as wind parks age.
🌅 Only 21% of people want modern turbines in sight, whilst 71% of people support more turbines being built.
🚤 Moving turbines off-shore exacerbates the maintenance challenges. Engineers need to boat up there, shut everything down & inspect them manually.
Application Deadline: 15 November 2021
Challenge Kick-off: 17 November 2021
Midterm Presentation: 20 December 2021
Final Presentations: 7 February 2022