Application of Bayesian network to reliability assessment of PV systems

Reliability is an important issue in large-scale photovoltaic (PV) systems as their operations rely on business plans developed over periods of time of at least twenty years which often assume fault-free functioning. This paper presents a Bayesian network (BN) reliability model combined with Fault tree (FT) for assessing the reliability of PV systems. A fault tree was built based on electrical architecture diagram of a generic PV system firstly, and then it was mapped into a Bayesian network. The conditional probability table is derived by employing the bucket elimination according to the fault probability of each component for reliability assessment of the system. The paper shows that the basic inference techniques of BN may be used to obtain classical reliability parameters (i.e. reliability of the top event or any sub-system) and identify the weak components of PV system. Moreover, the framework based on the BN formalism is intuitive and easy to be applied into the reliability assessment of PV system, and it provides a basis for more advanced and useful analyses in fault diagnosis of systems.