A Dynamic Hesitant Fuzzy CREAM Approach for Quantifying Human Error in Substation Operation

Abstract Human error probabilities (HEP) are heavily based on the expert’s knowledge and experience in real-world scenarios. When professionals employ linguistic labels to assess human failure occurrences, uncertainty and hesitation often occur. This work seeks to quantitatively analyze human errors using a new model based on hesitant fuzzy sets theory and the Cognitive reliability and error analysis method (CREAM). To enhance the traditional model, a dynamic weight adjustment mechanism is introduced to capture the time-varying importance of Common Performance Conditions (CPCs) across different phases of an accident. This model utilizes a structured expert assessment protocol (detailed in Appendix A) to collect data. The evaluations from three domain experts are recorded using a hesitant fuzzy matrix (HFM) to capture their hesitancy. The model then dynamically calculates CPC weights through a constrained optimization algorithm and derives a continuous HEP estimation formula. Finally, the effectiveness and practicality of the proposed DHFM-CREAM model are demonstrated by analyzing an electrical maloperation accident in a substation.