Open AccessPhasor estimation in power systems using a neural network with online training for numerical relays purposes
Author(s) -
Silva Chrystian Dalla Lana,
Cardoso Junior Ghendy,
Mariotto Lenois,
Marchesan Gustavo
Publication year - 2015
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2014.0312
Subject(s) - phasor , artificial neural network , harmonics , waveform , computer science , electric power system , offset (computer science) , amplitude , perceptron , electronic engineering , multilayer perceptron , control theory (sociology) , power (physics) , engineering , artificial intelligence , voltage , control (management) , electrical engineering , telecommunications , physics , quantum mechanics , radar , programming language
There are a few components of the current signal that may lead to inaccurate current measurement in power systems, and therefore, may cause malfunction on numerical protective relays and control devices. Some of these components include harmonics, the decaying DC offset, and noises. In this study, a phasor estimation method based on artificial neural networks is proposed, which will provide fast response time and accuracy. The method uses the multilayer perceptron structure to precisely estimate the amplitude and phase angle of the current waveform by determining its input weights during an online training process. The proposed algorithm is tested and compared with other reliable and well‐known methods for a performance evaluation.