The push for cleaner energy sources, coupled with declining costs, has facilitated the massive deployment of solar photovoltaic (PV) systems in electric grids worldwide. At the heart of any PV system is the inverter, a device responsible for converting DC power captured by solar cells into AC power suitable for grid use. In recent years, reliability concerns have emerged regarding inverters, with multiple reports identifying central and string inverters as the primary culprits in most forced outages in PV systems. Inverter failures significantly hinder energy production, potentially reducing it to zero. Thus, estimating the reliability of these devices is crucial for forecasting the long-term performance of PV systems.In this thesis, we develop a state-space reliability model to characterize the failure behavior of string inverters, using a limited and heterogeneous failure dataset from residential and commercial PV systems in the U.S. Despite the data constraints, the proposed model successfully captures both decreasing and increasing failure rate behaviors observed in the data. Additionally, we derive an exponential approximation of the model, enabling system-level reliability evaluation via Markov Reward Models (MRM). To address the uncertainty inherent in limited datasets, we adopt a Bayesian framework, which is better suited for uncertainty quantification under data scarcity. This approach allows us to compute credible intervals on expected energy production by propagating parameter uncertainty through the MRM. Our findings indicate that, although parameter uncertainty is non-negligible, its impact on expected long-term energy yield remains limited—primarily due to the relatively fast replacement of inverters compared to their average time to failure.Lastly, since the failure rate is an important quantity for reliability optimization and risk assessment, we establish a method for detailed failure rate estimation, providing deeper insights into the failure process. Following this approach, without relying on any major assumptions, the model estimations confirm our assumptions of a bathtub-like failure rate behavior.
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