A Novel Approach for the Error Correction of CT in the Presence of Harmonic Distortion

Current transformers (CTs) are extensively used for the measurement of electrical energy, and hence they play a crucial role in the revenue generation for power distribution utilities. The novelty of this paper is that it presents artificial neural network (ANN) application for the ratio and phase angle error corrections of CTs in the presence of harmonic distortions. In this paper, the behaviors of silicon iron alloy and nanocrystalline alloy CTs are examined by the means of a number of experimental tests under the sinusoidal and distorted waveform conditions. The relation between the nature of CTs secondary current waveforms (CTSwf) with their metrological properties, connected burdens, and nature of load currents (linear or nonlinear) is established. The training data are obtained by the nature of CTSwf in terms of amplitude errors, time shifts, and deviations caused by different harmonic contents between input and output quantities. The ANN is trained for various operating conditions. The implementation of the error correction system and data acquisition is done using dSPACE 1104 software. The experimental results illustrate that the proposed technique can be used for error compensation in case of sinusoidal and the distorted waveform composed of fundamental and many harmonics.