Open Access

ARTICLE

Instantaneous Mobility Indicators for Risk Management in Wind Farms: A Computer Modeling Approach

Guglielmo D’Amico^1,*, Edoardo Lui², Filippo Petroni¹

1 Department of Economics, University G. d’Annunzio, Pescara, Italy
2 Department of Physics, University of Genoa, Genoa, Italy

* Corresponding Author: Guglielmo D’Amico. Email:

(This article belongs to the Special Issue: Stochastic Modeling and Reliability Assessment in Industrial Engineering Systems)

Computer Modeling in Engineering & Sciences 2026, 148(1), 25 https://doi.org/10.32604/cmes.2026.082608

Received 19 March 2026; Accepted 29 June 2026; Issue published 27 July 2026

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.

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Keywords

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