Open AccessLoad margin assessment of systems with distributed generation with the help of a neuro‐fuzzy method
Author(s) -
Zambroni de Souza Matheus Ferreira,
Reis Yuri,
Almeida Adriano Batista,
Lima Isaias,
Zambroni de Souza Antonio Carlos
Publication year - 2015
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2014.0090
Subject(s) - margin (machine learning) , intermittency , continuation , computer science , monte carlo method , fuzzy logic , electric power system , control theory (sociology) , control engineering , reliability engineering , engineering , power (physics) , mathematics , artificial intelligence , machine learning , statistics , physics , control (management) , quantum mechanics , turbulence , thermodynamics , programming language
Voltage security problems became a matter of concern over the last three decades because of the systems complexity. This is about to worsen as the penetration of renewable sources grows. Different operating scenarios must be addressed because of the intermittent nature of some sources. This study discusses this problem by proposing a neuro‐fuzzy methodology to determine the load margin when the intermittency of the sources is taken into account. Load margin is obtained by the continuation method enhanced by the Constrained Reactive Implicit Coupling (CRIC) method, so its computational effort is reduced. Monte Carlo simulation is employed to generate the bunch of data considered by the neuro‐fuzzy and the results are obtained in two ways. First, a sample IEEE 34‐bus system is employed, so the results may be reproduced. Then, a modified Brazilian real system with 115 buses is considered.