TY  - EJOU
AU  - D’Amico, Guglielmo 
AU  - Lui, Edoardo 
AU  - Petroni, Filippo 

TI  - Instantaneous Mobility Indicators for Risk Management in Wind Farms: A Computer Modeling Approach
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 148
IS  - 1
SN  - 1526-1506

AB  - This paper develops an operational framework for short-horizon risk management in multistate stochastic systems, with application to wind farm performance. We focus on instantaneous mobility-based indicators derived from finite-state continuous-time Markov chains, which capture the local propensity of a system to transition between states. Unlike classical reliability and availability measures, these indicators provide a dynamic description of system behavior. The indicators are interpreted as policy signals to support decision-making under budget constraints. We introduce a state-conditional expected short-horizon loss, representing non-production risk, and use it to evaluate ranking-based intervention strategies. The framework is applied to a global dataset of wind farms. Results show that mobility-based indicators, especially those related to transition intensity, outperform standard availability proxies in identifying high-risk conditions and concentrating expected losses among top-ranked observations. This supports their use as effective tools for data-driven, policy-oriented risk management.
KW  - Wind farms; operational risk; non-production risk; Markov processes; semi-Markov processes; instantaneous failure and repair rates; ROCOF; ROCOR; ROI; mobility; energy-at-risk; revenue-at-risk

DO  - 10.32604/cmes.2026.082608