Threshold Voltage for Digital Residual Current Circuit Breaker Based on Tensor Flow

Abstract

The article describes a new way to define the threshold voltage for Machine Learning-based Digital Residual Current Circuit Breaker (RCCB), enabling the right cut-off point. Using the described methods, the authors obtained a gap to common mid voltage being around 0.5 V. The proposed technique is illustrated with three different loads of 3W, 5W, and 9W as the scope of this work. The authors try to apply it in Residual Current Circuit Breaker (RCCB). It could be useful in a hospital with a limited number of technicians to maintain various machines quickly. This work tries to realize a machine that could find out the best condition to cut off the electricity when there is any leakage current but keep the supply if it is still under tolerance. This allows improving the mistake of the midpoint about 16.97% over its wide range. The effectiveness of Python libraries usage realized the Artificial Neural Network (ANN) implementation as one of machine-learning algorithms. The learning process is applied to the measured leakage current data set. It goes with input preprocessing, training, testing, and data analysis. From all of those steps, it is possible to determine the induction voltage threshold at 1.080 from 3.3V as its maximum value with a negligible loss value of 0.0006. By comparing the value with a reference, it can be concluded that this method could be used in a real situation.