Current transformer saturation can cause protective relay misoperation or even prevent tripping. This paper presents the use of artificial neural networks (ANN) to correct current transformer (CT) secondary waveform distortions. The ANN is trained to achieve the inverse transfer function of iron-core toroidal CTs which are widely used in protective systems. The ANN provides a good estimate of the true (primary) current of a saturated transformer. The neural network is developed using MATLAB ® and trained using data from EMTP simulations and data generated from actual CTs. In order to handle large dynamic ranges of fault currents, a technique of employing two sets of network coefficients is used. Different sets of network coefficients deal with different fault current ranges. The algorithm for running the network was implemented on an Analog Devices ADSP-2101 digital signal processor. The calculating speed and accuracy proved to be satisfactory in real-time application.

Current transformer saturation can cause protective relay misoperation or even prevent tripping. This paper presents use of artificial neural networks (ANN) to correct CT secondary waveform distortions. The ANN is trained to achieve the inverse transfer function of iron-core toroidal current transformers which are widely used in protective systems. The ANN provides a good estimate of the true (primary) current for a saturated transformer. The neural network is developed using MATLAB and trained using data from EMTP simulations, and data generated from actual CTs. In order to handle large dynamic ranges of fault currents, a technique of employing two sets of network coefficients is used. Different sets of network coefficients deal with different fault current ranges. The algorithm for running the network was implemented on an Analog Devices ADSP-2 101 digital signal processor. The calculating speed and accuracy proved to be satisfactory in real-time application.

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