cff-version: 1.2.0 abstract: "<p>The dataset refers to the research activity performed in the framework of the EU project SAMOSAFER, Task 6.3 - Innovative control model and strategy development</p><p>and applications to MSFR.</p><p><br></p><p>In this activity, an innovative incident detection method has been developed, aiming at improving the safety and reliability of the Molten Salt Fast Reactor</p><p>power plant, focusing on operational scenarios involving some deviations from normal operational conditions.</p><p><br></p><p>The data-driven incident detection and classification methodology (based on the kNN algorithm) aims at identifying abnormal plant conditions thanks to a</p><p>continuous monitoring of some measurable system parameters and variables (e.g., the molten salt temperatures in the secondary circuit).</p><p><br></p><p>In order to train the algorithm, a set of numerical, time-dependent simulation is carried out at the system-level (primary circuit, secondary circuit and</p><p>balance of plant) with the Modelica language.</p>" authors: - family-names: Abrate given-names: Nicolò orcid: "https://orcid.org/0000-0002-6416-7059" - family-names: Caruso given-names: Nicolò - family-names: Dulla given-names: Sandra orcid: "https://orcid.org/0000-0002-3019-1472" - family-names: Pedroni given-names: Nicola orcid: "https://orcid.org/0000-0002-0636-613X" - family-names: Lorenzi given-names: Stefano orcid: "https://orcid.org/0000-0003-2747-1825" title: "Data underlying the research of Innovative control model and strategy development and applications to MSFR" keywords: version: 1 identifiers: - type: doi value: 10.4121/0ae20eee-97a6-4634-9f57-eb1887018fc2.v1 license: CC BY 4.0 date-released: 2023-12-06