Title :
Neural network-based technique used for recovery the CCVT primary signal
Author :
Saleh, S.M. ; Aboul-Zahab, E.M. ; Eldin, E. Tag ; Ibrahim, D.K. ; Gilany, M.I.
Author_Institution :
Minist. of Electr. & Energy, Egypt
Abstract :
The coupling capacitor voltage transformers transient response during faults can cause protective relay mal-operation or even prevent tripping. This paper presents the CCVT transient response errors and the use of artificial neural network (ANN) to correct the CCVT secondary waveform distortion. In this paper, an ANN program is developed to recover the primary voltage from the distorted secondary voltage. The ANN is trained to achieve the inverse transfer function of the coupling capacitor voltage transformer (CCVT), which provides a good estimate of the true primary voltage from the distorted secondary voltage. The neural network is developed and trained using MATLAB simulations. The accuracy of the simulation program is confirmed by comparison of its response with that of the target value from the simulation data.
Keywords :
artificial intelligence; electric machine analysis computing; neural nets; potential transformers; relay protection; transfer functions; transient response; CCVT primary signal recovery; MATLAB simulation; artificial neural network; coupling capacitor voltage transformer; distorted secondary voltage; neural network-based technique; primary voltage recovery; protective relay maloperation; simulation data; transfer function; transient response; Artificial neural networks; Capacitors; Error correction; Neural networks; Predistortion; Protection; Protective relaying; Transfer functions; Transient response; Voltage transformers; Artificial Neural Network (ANN); Coupling Capacitor Voltage Transformer (CCVT); Extra High Voltage (EHV) systems; High Voltage (HV); and The transfer function (TF);
Conference_Titel :
Power & Energy Society General Meeting, 2009. PES '09. IEEE
Conference_Location :
Calgary, AB
Print_ISBN :
978-1-4244-4241-6
DOI :
10.1109/PES.2009.5276017