Fault diagnosis model for biological electric shock based on residual current intrinsic mode function energy features

Abstract: Residual current operated protective devices (RCDs) have a wide range of application in low-voltage power grids. RCDs play an important role in preventing electric shock hazard and avoiding fire disaster caused by ground fault. In general, the root mean square (RMS) value of whole leakage current signal detected is considered as the unique criterion to determine whether the protector acts or not. The traditional RCDs cannot classify and identify electric shock fault type automatically based on whole leakage current signal. Theoretical analysis and operation experience indicate that such a criterion is unavailable in identification if an organism electric shock fault has occurred. The uncertain potential regularity and mapping relations exist between the biological shock fault diagnosis and residual current. To decrease the malfunction and tripping phenomenon and increase the reliability and the rate of proper commissioning for RCDs, a fault diagnosis model for biological electric shock based on residual current intrinsic mode function (IMF) multidimensional energy features is proposed innovatively for residual current protection technologies in the future low-voltage power grid. First, the electric shock current of organism (animal) is decomposed into five IMF components and one residual component by Hilbert-Huang transform method. The energy share of low frequency component IMF4 and IMF5 is as high as 86.35%, which can meet the needs of more than 86% for the measured signal, and the correlation coefficient of the amplitude of the low frequency IMF components is up to 0.99 or more. And the distribution of IMF energy on time and various frequency scales is made clear when the biological electric shock fault occurs. Residual current signal performance information is converted into energy feature vectors. The extraction method of IMF energy features in residual current is established. Then, the five-dimensional energy eigenvector in each residual current IMF component is selected to provide effective characteristics information source for biological shock fault diagnosis model. By combining the rapid optimization of quantum genetic computation and the self-adaptability of neural computation, the quantum genetic fuzzy neural network is established as the decision system of electric shock failure mode classification. The method has the advantages of self-adaptive resolution, good fault tolerance, high robustness and high accuracy. And the accuracy of simulation experiment reaches 100%, and the method avoids the local minimum of traditional gradient descent learning algorithm and improves the learning efficiency. The network error is 0.00099758 when the optimal learning algorithm is iterated to 1 156 times, which meets the error accuracy requirement. The problem that the electric shock fault type is not identified effectively in the engineering is resolved using this method. The reliable theoretical basis and supporting method for developing new generation residual current protection device are provided to ensure the personal safety and the safe operation of the low-voltage power grid, which is based on the action being caused by electric current component of the human body electric shock.