A Multi-Objective Memetic Optimization Method for Power Network Cascading Failures Protection

Yan-Fu Li (1) , Enrico Zio (1) , Giovanni Sansavini (2) , L. R. Golea (2)

1 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
2 Dipartimento di Energia [Milano]

Reliable and safe power grid operation requires the anticipation of cascading failures and the establishment of appropriate protection plans for their management. In this paper, we address this latter problem by line switching and propose a multi-objective memetic algorithm (MOMA), which combines the binary differential evolution algorithm with the non-dominated sorting mechanism and the Lamarckian local search. The 380 kV Italian power transmission network is used as a realistic test case.

Keywords: Lamarckian local search; cascading events; line switching; multi-objective optimization; memetic algorithm; differential evolution; non-dominated sorting

Domains: Computer Science [cs]; Modeling and Simulation; Engineering Sciences [physics]; Electric power; Computer Science [cs]; Neural and Evolutionary Computing [cs.NE]

Yan-Fu Li, Enrico Zio, Giovanni Sansavini, L. R. Golea. A Multi-Objective Memetic Optimization Method for Power Network Cascading Failures Protection. PSAM11 & ESREL 2012, Jun 2012, Finland. pp.1-10. ⟨hal-00737086⟩