The deployment of the Gen-IV Molten Salt Fast Reactor (MSFRs) requires the demonstration of its enhanced safety features with respect to other reactor concepts. To this aim, a power plant simulator including the primary system, the secondary system and the balance-of-plant has been developed in the framework of the SAMOFAR EU project. This simulator allows to describe the plant (transient) response to a variety of (normal and abnormal) operating conditions. Within this framework, the objectives of the present thesis are: 1) to propose a simulation-based method to characterize the system behaviour with respect to variations in physical and operational parameters, by means of a thorough exploration of the MSFR power plant state space; 2) to develop a data-driven algorithm for the efficient detection and classification of incidents, relying on a k-Nearest Neighbors (kNN) classifier.