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.%针对如何从低压电网总泄漏电流中检测出生物体触电电流信号的难题，提出了一种基于网格搜索和交叉验证的最小二乘支持向量机的触电电流信号检测方法。首先在剩余电流动作保护装置触电物理试验系统平台上通过故障录波器获得生物体在3个典型时刻（电源电压最大时刻、电源电压过零时刻及电源电压任意时刻）发生触电过程的总泄漏电流和触电电流波形，并截取触电前1个周期和触电后3个周期共800个采样点的信号数据作为触电试验样本数据；然后将触电试验样本数据进行滤波预处理，预处理后的多个样本采样点的总泄漏电流组合成特征向量输入最小二乘支持向量机（least square-support vector machine，LS-SVM），相应样本采样点的触电电流作为其输出，并通过网格搜索与交叉验证相结合的方法来优化最小二乘支持向量机参数，利用输出最优参数组合对触电电流与总泄漏电流的关系进行训练，从而建立了触电电流的检测模型；最后利用该方法对10组测试样本数据进行了检测，检测结果为：当训练样本数据为20组时，检测均方误差为14.0040，当训练样本数据为40组时，检测均方误差为11.7469，当训练试验数据为65组时，检测均方误差为11.1849。与径向基（radial basis function， RBF）神经网络方法相比，最小二乘支持向量机方法比径向基神经网络方法检测均方误差分别低3.7272、1.9132、0.1556，从而可较准确地从总泄漏电流中检测出生物体触电电流信号，为开发新一代基于生物体触电电流分量而动作的自适应型剩余电流保护装置提供理论依据。
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