Abstract
Current transformers (CTs) provide instrument-level current signals to meters and protective relays. Protective relays' accuracy and performance are directly related to steady-state and transient performance of CTs. CT saturation could lead to protective relay maloperation or even prevent tripping. This paper proposes the use of an artificial neural networks scheme to correct CT secondary waveform distortions. The proposed module uses samples of current signals to achieve the inverse transfer function of CT. Simulation studies are preformed and the influence of changing different parameters is studied. Performance studies results show that the proposed algorithm is accurate and reliable. The proposed algorithm has also been implemented and tested on a digital signal processor board. Details of the implementation and experimental studies are provided in this paper.
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