@Article{cmes.2026.082608,
AUTHOR = {Guglielmo D’Amico, Edoardo Lui, Filippo Petroni},
TITLE = {Instantaneous Mobility Indicators for Risk Management in Wind Farms: A Computer Modeling Approach},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {148},
YEAR = {2026},
NUMBER = {1},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v148n1/68189},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2026.082608}
}