Open AccessEnhanced self‐adaptive differential evolution multi‐Objective algorithm for coordination of directional overcurrent relays contemplating maximum and minimum fault points
Author(s) -
Shih Meng Yen,
Conde Arturo,
ÁngelesCamacho Cesar
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6995
Subject(s) - overcurrent , computer science , backup , algorithm , robustness (evolution) , weighting , mathematical optimization , engineering , mathematics , current (fluid) , electrical engineering , medicine , biochemistry , chemistry , radiology , database , gene
In this study, a parameter tune free enhanced self‐adaptive differential evolution multi‐objective (ESA‐DEMO) approach has been proposed for coordination of directional overcurrent relays. The advantages of the proposed method are: avoid the use of conventional single‐objective function, which requires tuning of weighting parameters; avoid tuning of algorithm parameters; minimisation of primary, backup and coordination time interval; zero violation of coordination constraints in large interconnected network; and low computational resource consumption leading to fast algorithm execution time. The proposed method has been implemented on the highly interconnected 6‐bus, IEEE 14‐ and 30‐bus systems, where results have shown robustness and consistency of the algorithm. Moreover, two‐fault point coordination criterion considering close‐ and far‐end (maximum and minimum) faults has been performed. ESA‐DEMO has been compared with popular genetic algorithms and state‐of‐the‐art multi‐objective algorithm for protection coordination study.