Fault detection in photovoltaic (PV) modules is essential for maintaining efficiency, safety, and cost-effectiveness. By identifying issues early, it ensures that solar panels operate at optimal performance levels, preventing energy loss and maximizing output. Furthermore, undetected faults can lead to hazards such as electrical fires or shocks, which is one of the most relevant safety issues in building-, infrastructure- and vehicle-integrated photovoltaic modules. Timely detection of faults allows for improved safety and prompt repairs, reducing maintenance costs and preventing more extensive damage that could incur higher expenses. Additionally, regular monitoring can extend the lifespan of the PV system by ensuring that all components function correctly. It also facilitates performance assessment, enabling better management and optimization of energy production.

This PhD research will focus on the development of procedures and identification/detection algorithms for the diagnosis of a PV module's faults and degradation. The primary requirement for the developed algorithm is that they can be applied online through module-level power converters. Therefore, emphasis must be put on two important aspects: (1) the developed techniques should only use information which is typically available in module-level power converters, namely operating current and voltage, and (2) the algorithms must be able to run on low-cost control devices (e.g. microcontrollers) like the ones typically implemented in module-level power converters.
 
The first objective of the project is to uncover unique and unambiguous fingerprints for the different types of faults or degradation, especially in the form of the signature that they leave on electrical variables which can be measured at module-level. The second objective of the project is to select proper identification techniques to extract such fingerprints from module-level measurements, having in mind that the power converter can be used to provide specific excitation signals for identification purposes. Both time-domain and frequency-domain approaches will be considered. As a byproduct of the first objective, a number of non-electrical fault/degradation fingerprints will be identified, which do not only contribute to the development of module-level fault detection algorithms within this project, but can also serve as input for the design of innovative sensing platforms for PV module diagnosis.
Note that the major focus of the project will be on single-junction crystalline silicon PV modules. However, novel technologies such as perovskite and tandem PV modules will also be considered.
 
Tagline: Utilize module-level power converters to timely detect faults, identify degradation and diagnose the state of health of photovoltaic panels.
 
Type of work: 15% literature review, 40% algorithm development and simulation, 30% experimental work, 15% dissemination of results.
 
Location: This position will be partly at the EnergyVille Campus in Genk, where you will have direct access to state-of-the-art lab infrastructure and the opportunity to collaborate with experts working on PV technology and PV energy systems, and partly at the Hasselt University campus in Diepenbeek.

More information about our research: https://www.uhasselt.be/en/instituten-en/imo-imomec and https://energyville.be/en/.

• You have a master degree in Physics, Electrical (or equivalent)
  Final-year students are (likewise) encouraged to apply.

• You have experience in analysis, modeling and simulation of power converters.

• You have knowledge of system identification techniques.

• You are proficient in software such as C++, MATLAB, python and PLECS. Ideally, you have experience with microcontroller’s programming.

• You have excellent reporting skills and can present scientific results (publications, conferences, seminars).

• You can easily integrate in an international team. Very good English communication skills are required.

• You can work independently and are eager to take initiative and responsibility.

• You have a background in photovoltaic module technology, ideally with focus on reliability and degradation of photovoltaic modules. You show a problem-solving attitude and a strong desire to stay up-to-date with recent advancements.

• You are motivated to contribute to the didactic and outreach tasks of the faculty of Engineering Technology and you can arouse students' enthusiasm.

You will be appointed and paid as PhD student.
Scholarship for 2 x 2 years, after positive intermediate evaluation.

The selection procedure consists of a preselection based on application file and an interview.
All interested applicants are required to submit an application package including but not limited to: 1. Motivation letter, 2. CV and 3. A self-written scientific text of 1-2 pages, describing state-of-the-art fault detection approaches for photovoltaic modules.

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Apply up to 31.01.2025