Improving Current and Voltage Transformers Accuracy Using Artificial Neural Network - Artificial Intelligence Applications and Innovations - Part I (AIAI 2012) Access content directly
Conference Papers Year : 2012

Improving Current and Voltage Transformers Accuracy Using Artificial Neural Network

Haidar Samet
  • Function : Author
  • PersonId : 1008008
Arash Dehghani
  • Function : Author
  • PersonId : 1008019

Abstract

Capacitive Voltage Transformers (CVTs) and Current Transformers (CTs) are commonly used in high voltage (HV) and extra high voltage (EHV) systems to provide signals for protecting and measuring devices. Transient response of CTs and CVTs could lead to relay mal-operation. To avoid these phenomena, this paper proposes an artificial neural network (ANN) method to correct CTs and CVTs secondary waveform distortions caused by the transients. PSCAD/EMTDC software is employed to produce the required voltage and current signals which are used for the training process and finally the results show that the proposed method is accurate and reliable in estimation of the CT primary current and the CVT primary voltage.
Fichier principal
Vignette du fichier
978-3-642-33409-2_45_Chapter.pdf (172.1 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01521404 , version 1 (11-05-2017)

Licence

Attribution

Identifiers

Cite

Haidar Samet, Farshid Nasrfard Jahromi, Arash Dehghani, Afsaneh Narimani. Improving Current and Voltage Transformers Accuracy Using Artificial Neural Network. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.435-442, ⟨10.1007/978-3-642-33409-2_45⟩. ⟨hal-01521404⟩
54 View
329 Download

Altmetric

Share

Gmail Facebook X LinkedIn More