Project R-16360

Photovoltaic modules fault detection through module-level power converters (Research)

Fault detection in photovoltaic (PV) modules is critical to ensure high efficiency, operational safety, and long-term cost effectiveness, particularly in building, infrastructure, and vehicle-integrated applications. Undetected faults can lead to performance degradation, energy losses, and serious safety hazards such as electrical fires or electric shocks. This PhD project addresses these challenges by developing advanced procedures and algorithms for the online diagnosis of faults and degradation in PV modules. The research focuses on module-level fault detection techniques that can be implemented directly within module-level power converters. To ensure practical applicability, the proposed algorithms are constrained to use only locally available electrical measurements, namely module voltage and current, and to operate on low-cost embedded control platforms such as microcontrollers. The first objective is to identify distinct and unambiguous fault and degradation fingerprints, particularly those manifested in measurable electrical variables at the module level. The second objective is to design and select suitable identification and signal-processing techniques capable of extracting these fingerprints, leveraging both time-domain and frequency-domain methods. The use of controlled excitation signals generated by the power converter itself will be explored to enhance fault identifiability. As a complementary outcome, the project will also identify non-electrical fault and degradation indicators that can inform the development of novel sensing solutions for PV diagnostics. While the primary focus is on single-junction crystalline silicon PV modules, the applicability of the proposed methodologies to emerging technologies, including perovskite and tandem PV modules, will also be investigated. Overall, the project aims to enable reliable, low-cost, and scalable fault detection solutions that improve the safety, performance, and lifespan of PV systems.

01 December 2025 - 30 November 2029