Pressure and acoustic Smart Sensors Network for Wind Turbines Monitoring
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 behavior of the blades, to improve the performance and reduce the operating costs.
In this project, we aim 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 utilization and value creation. Its modular and scalable design will allow wind turbine monitoring on an entirely new scale.
The student(s) will participate in the design of a low power smart device that includes the sensor and it is able to perform signal processing and send data in a wireless manner. Thus, the candidate will work with micro-controllers, sensors, wireless communication at firmware level as well as data analysis tools and training tools on the PC/cloud. The hardware and software load of the thesis will be balanced according to the skills and preferences of the candidate students when the details task description will be provided before the student project will start. The field measurements of the system will be performed from the students as an important activity in order to evaluate power consumption, reliability, functionality, classification accuracy, and energy efficiency and to further optimize the system. Energy Harvesting can be also employed to design the sensor node, and this will be decided according to the skills and motivation of the student.
Depending on the applicant's profile and project type, thier tasks may involve some of the following:
- lab. testing/characterization of sensors and embedded systems: verification of the prototype's characteristics w.r. design specification (simulations), measuring power-consumption, and assessing detection performance in lab. conditions
- High-level software programming, signal processing, machine learning, wireless communication
- programming the circuit for a specific application, field testing, data acquisition
- Machine Learning for microcontrollers.
- PCB design to build a working prototype which includes all the subsystems
The project is done in collaboration with Dr. Sarah Barber Programme Leader Wind Energy at HSR Hochschule für Technik Rapperswil https://www.iet.hsr.ch/index.php?id=18317&L=4.
Figure Source: https://www.ilika.com/latest-news/wind-turbine-monitoring
- Looking for Semester and Master Project Students
- Supervisors: Michele Magno;
(not all need to be met by the single candidate)
- Experience using the laboratory instrumentation - signal generators, oscilloscopes, DAQ cards, Matlab etc.
- analog electronics and signal conditioning with operational amplifiers: amplifiers, filters, integrators etc.
- knowledge of microcontroller programming and PC programming (C/C++, preferably microcontroller with Bluetooth Low Energy but it is not mandatory)
- basic knowledge on signal processing and machine learning is a plus.
- plus is knowledge on printed circuit board (PCB) using Altium.
- 35% Theory
- 45% Implementation
- 20% Testing