Data-Driven Switch Fault Diagnosis for DC/DC Boost Converters in Photovoltaic Applications

Switch semiconductors are essential components in power electronics stages for photovoltaic (PV) systems, which are prone to failures caused mainly by thermal stress, thereby affecting PV energy production. Specifically, dc/dc boost power converters are commonly used as maximum power point tracking (MPPT) systems. Several works have been reported so far in the literature for switch fault detection and isolation (FDI) purposes in boost power converters. However, most of them require additional sensors and circuitry to those included in any PV system, which generate extra costs on the overall system. Hence, this work presents an efficient and low-cost switch fault diagnosis proposal, which requires only the most common measurements in PV systems, namely, PV current and voltage. This work departs from a data-driven FDI methodology for diagnosing switch open- and short-circuit faults in dc/dc boost converters. The experimental results have been evaluated against sudden irradiance changes and switch faults in a test bench of 350
$\text {W}$
operating under a closed-loop nonlinear control action for MPPT purposes by using the dSpace 1104 board.