Open AccessPareto optimal allocation of resistive‐type fault current limiters in active distribution networks with inverter‐interfaced and synchronous distributed generators
Author(s) -
Chen Lei,
Zhang Xuyang,
Chen Hongkun,
Li Guocheng,
Yang Jun,
Tian Xin,
Xu Ying,
Ren Li,
Tang Yuejin
Publication year - 2019
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.443
Subject(s) - pareto principle , multi objective optimization , particle swarm optimization , inverter , fault (geology) , mathematical optimization , computer science , node (physics) , control theory (sociology) , engineering , mathematics , voltage , electrical engineering , structural engineering , control (management) , artificial intelligence , seismology , geology
Abstract To efficiently reduce the fault currents of active distribution networks (ADNs), this paper proposes a novel methodology for Pareto optimal allocation of resistive‐type fault current limiters (R‐FCLs). The proposed approach enables to simultaneously optimize the number, location, and size of the R‐FCLs in the ADNs with inverter‐interfaced and synchronous distributed generators (DGs). The sensitivity analysis is introduced to rank candidate locations, and a constrained multiobjective function is created to minimize the cost of the R‐FCLs and the fault currents of the ADNs. An improved multiobjective particle swarm optimization (MOPSO) algorithm and a multiobjective artificial bee colony (MOABC) algorithm are designed to obtain the Pareto optimal solution set. The proposed approach is verified using the modified IEEE 33‐node and 69‐node distribution systems, where both centralized and dispersed access of DGs are considered. The numerical results demonstrate that: (a) The fault currents in all nodes of the two testing systems are limited to the permissible levels using the least capital cost of the R‐FCLs, and (b) the improved MOPSO outperforms the MOABC to achieve a better Pareto front and decrease the number of iterative computation. In consequence, the feasibility and superiority of the proposed approach are well validated.