Abstract: Abstract: Currently, residual current operated protective devices (RCDs) have a wide range of application in low voltage power grids and play an important role in preventing electric shock hazards and avoiding fire disasters caused by ground fault. But, the stocking current of the animals and human beings has no relationship with the setting value of action current from the protection devices, and the root mean square (RMS) value of residual current detected is considered the current value to determine if the protector acts or not. Theoretical analysis and operation experiences indicate that such criterion is unavailable in identifying the shocking current signals of the animals and human beings from the summation leakage current .Thus, in order to identify the electric shock signal from the summation leakage current automatically and accurately, intelligent information processing techniques are adopted and identification method based on least square-support vector machine (LS-SVM) with grid search and cross validation optimization are proposed. Firstly, through the experiments simulating various scenarios of rabbits electric shocking on the electric shock experiment platform of residual current operated protective devices(RCDs), signal data of 800 sample points before the one cycle and after three cycles of electric shock are used as electric shock sample data obtained by fault recorder to get the leakage current and electric shock current waveform on the electric shock process of the power supply voltage at maximum time, zero time, and any time. Secondly, the above sample data needed to be filtered to reduce the impact of the non-stationary for noise data. Then, the leakage currents of sampling points are combined into a high dimensional feature vector which is input into LS-SVM and the corresponding electric current of sampling point is employed as output of LS-SVM. The relation between input and output is trained by applying grid search and cross validation to determine the optimal parameters of LS-SVM automatically, and the ideal model of electric shock signal is obtained. Thirdly, a total of 75 groups of sample data are used as the research object. Among them 10 groups of sample data are used as testing samples, with the experimental results showing that when 20 groups of sample data are used as training samples, the identification mean square error is 14.0040. When 40 groups of sample data are used as training samples, the identification mean square error is 11.7469. When 65 groups of sample data are used as training samples, the identification mean square error is 11.1849. In comparison with the electric shock current identification method proposed method recently, the radial basis function (RBF) neural network method, the proposed method has the lower identification error. Consequently, it can identify the shocking current signals of the animals and human beings from the summation leakage current more accurately and provide a more reliable theoretical basis for developing new generations of adaptive residual current protection devices, which is based on the electric current component of the animals and human beings causing its action.