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AeroSense: a novel MEMS-based surface pressure and acoustic IoT measurement system for wind turbines

English title AeroSense: a novel MEMS-based surface pressure and acoustic IoT measurement system for wind turbines
Applicant Barber Sarah
Number 187087
Funding scheme Bridge - Discovery
Research institution Hochschule für Technik Rapperswil
Institution of higher education University of Applied Sciences Ostschweiz - FHO
Main discipline Mechanical Engineering
Start/End 01.05.2020 - 30.04.2023
Approved amount 1'617'756.00
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All Disciplines (3)

Discipline
Mechanical Engineering
Electrical Engineering
Fluid Dynamics

Keywords (6)

Energy costs; Machine learning; Digital twin; MEMS; Monitoring; Wind energy

Lay Summary (German)

Lead
Windenergie ist eine Schlüsseltechnologie zur Erreichung der UN-Ziele für nachhaltige Entwicklung und der EU-Energiestrategie 2030. Da die Windenergieindustrie reift und die Windturbinen wachsen, besteht ein zunehmender Bedarf an kostengünstigen Lösungen für die Überwachung und Datenanalyse, um das komplexe aerodynamische und akustische Verhalten der Blätter zu verstehen, die Leistung zu verbessern und die Betriebskosten zu senken.
Lay summary

Das Ziel dieses Projekts ist die Entwicklung eines ersten MEMS-basierten Oberflächendruck- und akustischen intelligenten Messsystems für Windenergieanlagen, welches dünn, nicht instrusiv, robust, modular, energiesparend, autark, drahtlos, einfach zu installieren und kostengünstig st. Das System wird neuartige eingebettete Signalverarbeitungslösungen zur On-Board-Kalibrierung und -Korrektur der Messgrössen sowie eine Digital Twin-Plattform für eine effektive Datennutzung integrieren. Sein modularer und skalierbarer Aufbau wird die Überwachung von Windenergieanlagen in einem völlig neuen Massstab ermöglichen.

Die Forschungsziele sind:

  • Forschungsziel 1: Definition der Systemanforderungen für die Hauptkundengruppen.
  • Forschungsziel 2: Entwicklung intelligenter Sensorkorrektur- und Kalibrierverfahren.
  • Forschungsziel 3: Entwurf & Entwicklung eines autarken Messsystems für die höchsten Anforderungen, die in Forschungsziel 1 definiert wurden.
  • Forschungsziel 4: Entwurf & Entwicklung einer Digital Twin-Plattform mit On-Board-Signalverarbeitung.
  • Forschungsziel 5: Aufbau, Test & Validierung eines Prototypsystems (Labor, Windkanal & Feld).
  • Forschungsziel 6: Veröffentlichung eines einzigartigen Datensatzes von aerodynamischen & akustischen Feldmessdaten.
  • Forschungsziel 7: Demonstration des potenziellen Mehrwerts des Systems.
  • Forschungsziel 8: Entwicklung von Business Cases und Kommerzialisierungsplänen.
Direct link to Lay Summary Last update: 04.03.2020

Responsible applicant and co-applicants

Employees

Abstract

Wind energy is a key technology for reaching the UN's sustainable development goals and the EU Energy Strategy 2030. As the wind energy industry is maturing and wind turbines are growing, there is an increasing need for cost-effective monitoring and data analysis solutions to understand the complex aerodynamic and acoustic behaviour of the blades, to improve the performance and reduce the operating costs. The aim of this project is to develop a first ever MEMS-based surface pressure and acoustic smart measurement system that is thin, non-intrusive, robust, modular, low power and self-sustaining, wirelessly transmitting, easy to install and cost-effective for wind turbines. The system will integrate novel embedded signal processing solutions, including artificial intelligence if necessary, for on-board calibration and correction of the measured quantities and a digital twin platform for effective data utilisation and value creation. Its modular and scalable design will allow wind turbine monitoring on an entirely new scale. The research goals are:•Research goal 1: Definition of the system requirements for the main customer groups.•Research goal 2: Development of smart sensor correction and calibration methods.•Research goal 3: Design & development of a self-sustainable measurement system for most stringent requirements defined in research goal 1.•Research goal 4: Design & development of a digital twin platform including on-board signal processing. •Research goal 5: Build, test & validation of a prototype system (laboratory, wind tunnel & field).•Research goal 6: Public release of a unique set of aerodynamic & acoustic field measurement data.•Research goal 7: Demonstration of the potential added value of the system.•Research goal 8: Development of business cases & commercialisation plans.In order to reach these goals, the three applicants complement each other ideally, combining vital experience in the wind energy industry and in R&D management (Sarah Barber, HSR), as well as strong academic backgrounds in structural monitoring and machine learning (Eleni Chatzi, ETHZ CSMM) and in embedded systems and smart sensor design (Michele Magno, ETHZ IIS). Additionally, an Advisory Board consisting of experts from the industry and research will provide inputs during the development of this innovation. BRIDGE Discovery has been chosen for this project due to the high innovation potential and scientific impact of the planned product combined with the volume of further research and development required. The research goals shall be reached with the following work:•Work Package 1: Project management. (requested as Innosuisse Salary Compensation).•Work Package 2: Research and development. (a) New sensor correction and calibration methods; (b) Smart electronics and software; (c) Digital twin and machine learning.•Work Package 3: Prototype test and demonstration. (a) Functional tests; (b) Wind tunnel tests; (c) Two sets of field measurements on operating wind turbines.•Work Package 4: Value creation. Evaluation of results in Value Proposition Design workshops followed by creation of at least one business plan for each customer group.•Work Package 5: Dissemination. (a) Website and social media; (b) Conferences and other forums; (c) Peer-reviewed journal papers, (d) Open-access data.The main outcome of the project will be a new measurement system with integrated on-board machine learning processing and a digital twin platform for effective data utilisation that has been validated and demonstrated in a relevant environment. Additionally, new business plans for the final commercialisation step of a new product and/or service will be delivered, as well as a range of new data correction, calibration and analysis techniques and a range of open access data.
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