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.
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.435-442, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_45〉
https://hal.inria.fr/hal-01521404
Contributeur : Hal Ifip
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Soumis le : jeudi 11 mai 2017 - 17:10:25
Dernière modification le : vendredi 1 décembre 2017 - 01:16:31
Document(s) archivé(s) le : samedi 12 août 2017 - 13:43:07
Haidar Samet, Farshid Jahromi, Arash Dehghani, Afsaneh Narimani. Improving Current and Voltage Transformers Accuracy Using Artificial Neural Network. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.435-442, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_45〉. 〈hal-01521404〉
