An Artificial Neural Network Developed in MATLAB-Simulink for Reconstruction a Distorted Secondary Current Waveform. Part 2

Recently, there has been an increased interest in the use of artificial neural networks in various branches of the electric power industry including relay protection. The operation of the traditional microprocessor-based relay protection device is based on calculation the RMS values of the monitored current and voltage signals and its comparison with the predetermined thresholds. However, calculated RMS values often do not reflect the real processes occurring in the electrical equipment under protection due to, for example, current transformer saturation. In this case secondary current has a characteristic distorted waveform, which is significantly differs from its ideal (true) waveform. This causes underestimation of the calculated RMS value of the secondary current compared to its true value; also, it causes a trip time delay or even to a relay protection devices operation failure. In this regard, one of the perspective applications of the artificial neural network for the relay protection purposes is the current transformer distorted secondary current waveform restoration due to its saturation. The article describes in detail the stages of the practical implementation of the artificial neural networks in the MATLAB-Simulink environment by the example of its use to reconstruct the distorted secondary current waveform of the saturated current transformer. The functioning of the developed neural networks was verified in the MATLAB-Simulink environment; with the use of the SimPowerSystems component library a model was implemented which allow simulating the current transformer saturation, accompanied by the secondary current waveform distortion, and its further restoration using developed artificial neural networks. The obtained results confirmed the ability of the neural networks that had been developed to almost completely restore the distorted secondary current waveform. Thus, it seems promising to use pre-trained artificial neural networks in real relay protection devices, since such use will ensure the speed of real relay protection devices; their operation reliability will also increase.

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