Evolutionary optimization of an affine model for vulnerability characterization in smart grids
n this paper we present an interesting application of the Decision Support System, known as the OCC_System, designed for faults recognition and classification within the real-world Medium Voltage power grid of Rome, Italy, managed by the Azienda Comunale Energia e Ambiente (ACEA) company. Given a historical data set consisting of fault patterns described by heterogeneous features related to endogenous and exogenous factors, the recognition system is trained to classify fault states assigning them a probability of fault. Disambiguating the external causes, whose dynamic is fast, and the constitutive parameters of the power grid, whose dynamic is slow, an affine algebraic model is estimated through an evolutionary technique in order to obtain a vulnerability index related to power grid equipment. The estimation procedure allows obtaining even a correlation matrix among external causes and constitutive parameters useful to better characterize the fault phenomenon under study.