A Hybrid Approach Combining Scenario Deduction and Type-2 Fuzzy Set-Based Bayesian Network for Failure Risk Assessment in Solar Tower Power Plants

Under extreme operating conditions such as high temperatures, strong corrosion, and cyclic thermal shocks, key equipment in solar tower power plants (STPPs) is prone to severe accidents and results in significant losses. To systematically quantify potential failure risks and address the methodological gaps in existing research, this study proposes a risk assessment framework combining a novel scenario propagation model covering triggering factors, precursor events, accident scenarios, and response measures with an interval type-2 fuzzy set (IT2FS) Bayesian network. This framework establishes equipment failure evolution pathways and consequence evaluation criteria. To address data scarcity, the methodology integrates actual case data and expert elicitation to obtain assessment parameters. Specifically, an IT2FS-based similarity aggregation method quantifies expert opinions for prior probability estimation. Additionally, to reduce computational complexity and enhance reliability in conditional probability acquisition, the IT2FS-integrated best–worst method evaluates the relative importance of parent nodes, combined with a leakage-weighted summation algorithm to generate conditional probability tables. The model was applied to a western Chinese STPP and the results show the probabilities of receiver blockage, pipeline blockage, tank leakage, and heat exchanger blockage are 0.061, 0.059, 0.04, and 0.08, respectively. Under normal operating conditions, the occurrence rates of level II accident consequences for all four equipment types remain below 6%, with response measures demonstrating significant suppression effects on accidents. The research results provide critical decision-making support for risk management and mitigation strategies in STPPs.