A scenario based threat assessment using Bayesian networks for a high voltage direct current converter platform

The climate change challenges a variety of aspects in our society. One spect is the energy production and the composition of the energy mix. Through the last years the amount of offshore wind farms has increased as well as the structure of the electricity producing infrastructure has changed from a more centralized (power plant oriented) to a more regional mode (decentral (offshore) wind farms and solar panels) of production. The vulnerability of the power-generating infrastructure is also changing. Therefore a quantification of the threat level is necessary. This paper should evaluate if a Bayesian network as a quantitative risk assessment model can be used to assess the threat level of an offshore wind farm. Common approaches build a Bayesian network based on a qualitative risk assessment. The Bayesian network presented in the paper is build based on a Functional Resonance Analysis Method (FRAM) based process model because a threat is strongly influenced by the scenario under consideration. The developed approach will be applied to the case study “unauthorized access to an high voltage direct current converter platform (HVDCC)”