Open AccessEstimation of voltage instability inception time by employing k‐nearest neighbour learning algorithm
Author(s) -
Khalilifar Mahtab,
Joorabian Mahmood,
Seifosadat Ghodratollah,
Shahrtash Seyed Mohammad
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.2018.6284
Subject(s) - instability , computer science , voltage , algorithm , set (abstract data type) , disturbance (geology) , stability (learning theory) , control theory (sociology) , engineering , artificial intelligence , machine learning , control (management) , physics , mechanics , electrical engineering , paleontology , biology , programming language
In this study, the proposed algorithm is a help for utilities to find out when voltage instability will happen, as soon as they realise that a disturbance may/will result in voltage instability. In this regard, the data gathered, on‐line, by a wide area monitoring system (WAMS) is supposed to be processed in an energy management system, where, finally, voltage instability inception time will be determined. The proposed algorithm employs k‐nearest neighbour algorithm as an instance‐based learning algorithm. This estimation uses only pre‐disturbance information and exact disturbance data, so it is fast enough for real‐time applications. A comprehensive investigation of the input features has resulted in introducing the most appropriate feature set and the most effective search procedure in estimating the instability time. The simulation results show an acceptable accuracy in estimation of voltage instability time.