Open AccessIslanding detection technique based on relevance vector machine
Author(s) -
Makwana Yogesh,
Bhalja Bhavesh R.
Publication year - 2016
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2016.0184
Subject(s) - islanding , computer science , real time digital simulator , support vector machine , classifier (uml) , power network , electronic engineering , power (physics) , electric power system , pattern recognition (psychology) , artificial intelligence , control theory (sociology) , real time computing , engineering , physics , control (management) , quantum mechanics
This study present a new islanding detection technique based on relevance vector machine (RVM) containing various types of distributed generations (DGs). The proposed scheme is based on utilising negative sequence component of current ( I 2 ), acquired at the terminal of the target DG. Various islanding and non‐islanding events with variable real and reactive power, change in network topology and diverse X/R ratio have been generated by modelling IEEE 34 bus system in real time digital simulator (RTDS ® /RSCAD) environment. The samples of I 2 for one cycle duration from the inception of islanding/non‐islanding events are given as input to the proposed RVM classifier. The proposed classifier is able to discriminate between islanding and non‐islanding events with an accuracy of 98.62% by utilising only 40% of the total dataset in training. Moreover, it is capable to classify islanding condition rapidly and correctly even with perfect power balance situation. Furthermore, it remains immune to change in network configuration/entirely different network and X/R ratio of various types of DGs. Comparative analysis of the proposed scheme in terms of accuracy, testing time and number of relevance vectors (RVs) with the existing schemes shows its superiority in discriminating islanding situation with non‐islanding events.